Path and Trajectory Planning

ANHUI UNIVERSITY OF FINANCE & ECONOMICS 1/31

Path and Trajectory Planning

for ROBOTS


PRESENTATION BY

Deepak Kumar : 2K13/CSE/26Sandeep Jain : 2K13/CSE/29Nidhi Joshi : 2K13/CSE/27Archana Saxena : 2K13/CSE/3Nitin Saini : 2K13/CSE/28Sunil Bisaiya : 2K13/CSE/30


INTRODUCTION

Path and Trajectory planning means the way that a robot is moved from one location to another in a controlled manner.

Trajectories can be planned either in joint space (directly specifying the time evolution of the joint angles) or in Cartesian Space (specifying the position and orientation of the end frame).Issues in trajectory planning include attaining a specific target from an initial starting point, avoiding obstacles, and staying within manipulator capabilities.


1. Optimal and Efficient Trajectory Planning in Industrial Robots

2. Path and Trajectory Planning for Nanorobots

3. Environment based Path and Trajectory Planning

4. Path Planning using Artificial Intelligence.

5. Path and Trajectory Planning in Air-Crafts

6. Path and Trajectory Planning for Underwater Robots

RESEARCH TOPICS COVERED


Optimal and Efficient Planning in Industrial Robots

By : Deepak Kumar Roll No : 2K13/CSE/26


Industrial Robots are widely used in many fields of industry, such as automotive an Aircraft industries. They are accustomed to be used to carry out repeat tasks : pick an place, welding, assembling…etc. Nowadays, due to their flexibility and ability to perform complex tasks in a significant workspace, robots are finding their way into SMEs to realize more than these classical tasks.

Industrial Robots are reshaping the manufacturing industry. They are often used to perform duties that are dangerous or unsuitable for human workers. Ideal for situations that require high output and no errors, the Industrial robot is becoming a common fixture in factories. They are grouped according to number of axes, structure type, size of work envelope, payload capability, and speed.

Introduction to Industrial Robots

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Industrial robots must have high flexibility to execute different technological operations and work together with human workers.

To move between two space points, different tasks must be solved. The best trajectory must be found, obstacles and collisions must be avoided, other limitations must be considered, and the high efficiency and work productivity must be achieved.

Objective


1. Guan Xiaoqing and Wang Jidong “Trajectory planning theory and method of Industrial Robot” IEEE 3rd International Conference on Computer Research and Development (ICCRD), 2011 at Shanghai .

2. Panwadee Tangpattanakul and Pramin Artrit “Minimum Time Trajectory of Robot Manipulator using Harmony Search Algorithm”, IEEE 6th International Conference on Electrical Engineering/ Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) 2009 at Pattaya, Chonburi.

3. Guangliang Liu, Yali Wang, Jianbin Zhi, Shiguo Ma and Tie Chen “Optimized Trajectory planning algorithm for Industrial Robot”, IEEE 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 2012 at Sichuan.

4. Olabi, A. Bearee and R. Nyiri, “Enhanced Trajectory planning for Machining with Industrial Six-Axis Robots”, IEEE International Conference on Industrial Technology (ICIT), 2010 at Vi a del Mar.

5. R. Saravanan, S.Ramabalan and J. Fakrudeen Ali Ahamed “Evolutionary Minimum Cost Trajectory planning for Industrial Robots” Journal of Mechanical Sciences, Vol. 1, No. 1, pp 24-36.

Research References


Trajectory planning theory and method of Industrial Robot [1]

• This paper focus on the basic theory of Trajectory planning and control for in-dustrial robots which means that the joint trajectories of a industrial robot should take the minimum traveling time of the motion of the robot’s hand be-tween two point.

• Generally, each trajectory of joint coordinates is fitted together by the principle of MSES which means minimum square error sum. It includes a lot of matrix operations and differential operations and therefore is not the best method.

• In order to reduce the computational burden, the motion control of robots is performed at the joint level. In motion control, it is necessary to make each joint move along the corresponding trajectory and all joints can move along the irrespective trajectories, that is the hand of the robot moves approximately along the specified path.


• In order to observe the tracking accuracy of the path by means of trajectory planning method of industrial robot, simulation for the trajectory planning of industrial robot by the proposed method is employed. The testing results show that the trajectory planning method of industrial robot is feasible in the optimization of the trajectory planning and control for industrial robots.

No

Yes

Interface initialization

linkage success?

linkage again

Extract job file information

Initialization

Trajectory planning algorithm

Perform industrial robot

Process of Trajectory planning of industrial robot


Minimum Time Trajectory of Robot Manipulator using Harmony Search Algorithm [2]

• In this paper, an algorithm namely minimum time trajectory planning is defined which uses Harmony Search (HS) method clamped with cubic splines.

• The objective function is minimum interval traveling time of robot manipulator with the kinematics constraints (velocity, acceleration and jerk).

• Harmony Search (HS) method is used as optimization technique. This algorithm successfully obtains the better solution than Sequential Quadratic programming (SQp) while the consumed process time is nearby.

• Moreover HS does not need to use the initial values in the optimization solving process like SQp. The study case indicates that HS algorithm is efficient for solving the optimal trajectory and the obtained trajectory keep under the kinematics limitations.

• The simulation compares two techniques, the sqp and HS. Sqp techniques result for minimum time trajectory is 8.572sec. The HS simulation results give the minimum time trajectory 8.5718sec for 10,000 iterations an 8.5577sec for 2,00,000 iterations. Although the HS process uses the more number of iterations, the whole sqp process takes computation time nearby the 10,000 iterations of HS process. Therefore, the HS method gives a better time of trajectory than sqp method where the kinematics constraint is satisfied.


Optimized Trajectory planning algorithm for Industrial Robot [3]

• It is an effective trajectory planning algorithm for industrial robot in which both running velocity and time are normalized.

• It can be conveniently incorporated into the existing industrial manipulator without undertaking significant cost of risk.

• The basis to find this algorithm is to obtain the expected trajectory by taking into consideration not only the constraints of the controller, but also the specification of the task.

• In order to obtain the optimal cubic spline joint trajectories, firstly the planning is formalized as a global constrained mini-max optimization problem. Then an algorithm based on interval analysis is exposed which converges within an arbitrary precision to a global solution.

• The algorithm is tested on the simulated robot with one smooth trajectory in the Cartesian space of the robot, which has given the reasonable output of each joint of the robot.


Enhanced Trajectory planning for Machining with Industrial Six-Axis Robots [4]

• This paper focus on the trajectory planning stage, which can roughly be split in two parts : path planning and feed rate profiling.

• Path planning is firstly done by means of L1 spline interpolator. This interpolator generates smooth c2- continuous, shape preserving paths. Then a smooth feed rate profile, taking account of the joint kinematics constraints, is generated by means of a parametric speed interpolator. The parametric interpolator takes advantage of the properties of finite-impulse response (FIR) to give a smooth pattern to the feed rate profile (jerk limited, jerk minimum or others).

• These two different trajectories along the selected path have been tested on a six-axis industrial robot. It was observed that the feed rate planning strategy is an effective solution for controlling the tool-tip motion of a robot.


Evolutionary Minimum Cost Trajectory planning for Industrial Robots [5]

• This paper focus on a method for computing minimum cost trajectory planning for Industrial robots manipulators. The objective function minimizes the cost function with the constraints being joint positions, velocities, jerks and torques by considering dynamic equations of motion. A clamped cubic spline is used to represent the trajectory. This is a non-linear constrained optimization problem.

• This problem of optical dynamic free motion of robotic arms is solved by evolutionary techniques such as Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and Differential Evolution (DE) methods.

• The same approach can be easily extended to handle complex situations.

• The proposed NSGA-II and DE converge quickly and show their superior nature by giving better results than the conventional optimization method SQP.


The optimal trajectory planning is an important function in a robot control area. Generally, the operating function of manipulators requires the highest performance such as minimum time, minimum energy, and no damage to the system .

Each of these working parameters has an impact on the work of the robot. Most of the algorithms that address the problem of collision-free trajectory planning for industrial robots try to optimize some of the working parameters or some of the objective functions. The optimization criteria most widely used can be classified as follows:

minimum execution time (related to productivity);minimum jerk (related to the quality of work, accuracy and equipment maintenance);minimum energy consumed (or minimum actuator effort) (related to savings);andhybrid criteria, e.g. minimum time and energy.

The selected algorithm should take into account the presence of obstacles (to avoid collisions) and the dynamics of the robotic system and the method to solve an optimization problem to find the minimum time trajectory to perform the tasks the robot should do.

Conclusion


PATH AND TRAJECTORY PLANNING IN NANO ROBOTOS

By : Sandeep Jain Roll No : 2K13/CSE/29


Introduction to Nano-Robots

Nanotechnology based nanorobots has framed science with engineering activities at the cellular level. Nano-sized nanorobots can investigate any biological environment (cell culture or human body) at the microscopic level.

Recently, nanorobots has been tried to function as a next generation pills which is a nano-shaped capsule, carrying the required therapeutic material for the targeted region. These capsules are considered to have their own decision making ability for avoiding obstacles such as immune system attack and reaching the particular region based on path planning algorithm. The human body is a complex system with 3D structure, thus modelling of such system at cellular level also requires 3D mapping of each cell.

A nanorobot is a tiny machine designed to perform a specific tasks with precision at nanoscale dimensions (1 nm = 10 to power of -9 meter).


Since the Human Body is a highly liquid environment that too consist variety of liquid like blood, chemical, acid etc. The objective is to design a path and trajectory for a nanorobot that should be having collision free navigation in a liquid environment inside the human body. It is important that the nanorobot should have a smooth path and trajectory movement while navigating in the blood environment.

It is important to note that since the human body is highly sensitive to the foreign particles and their movements in the body as it may cause high reaction in the body so it is important, while planning control motion of nanorobots, that these should be safe for human and not cause any damage to the other cells.

Objective


1. Cavalcanti A., “Hardware Architecture for Nanorobot Application in Cerebral Aneurysm”, 7th IEEE International Conference on Nanotechnology August 2 - 5, 2007, Hong Kong.

2. Sanchita Paul “Nano Robots : A Survey on Recent Development in Medical Applications”, International Journal of Advanced Research in Computer Science and Software Engineering, 2012.

3. Khin Haymar Saw Hla “Obstacle Avoidance Algorithm for Collective Movement in Nanorobots”, IJCSNS International Journal of Computer Science and Network Security 2008.

4. John Reif “Nano-Robotics Motion Planning and Its Applications in Nanotechnology and Biomolecular Computing”, 3rd Workshop of the Algorithmic Foundations of Robotics, Houston, Texas.

5. Khin Haymar Saw Hla “Mobility Enhancement in Nanorobots by using Particle Swarm Optimization”. IEEE International Conference on Computational Intelligence and Security 2008.

Research References


Hardware Architecture for Nanorobot Application [1]

The Architecture

The architecture of the nanorobot plays a very important role in the Trajectory & Path Planning of the nanorobots.

In the designing of the architecture

a) the integrated system, b) smart biosensors and c) programmable nano devices

are important parts. Recently, use of RFID (radio frequency identification) are explored for data collection and transmission inside the body. The nanorobots are designed in such a fashion so as to prevent it from the immune attacks.

Now Chemical Assembled Electronic Nanotechnology has been adopted as an altern-ative to the metal oxide semiconductors (CMOS). Emphasis is given to ASIC (application specific integrated circuit) based nanorobots. The nanorobot archi-tecture depends upon the environment in which it is to be used. For biomedical application, temperature, concentration of chemicals in blood, electromagnetic signatures, acoustic signals, velocity of nanorobot and fluid are some of the parameters which are required to be considered for the nanorobot architecture.


NanoRobots : A Survey on Recent Development in Medical Applications [2]

Path and Trajectory Planning

The nanorobot navigation in a liquid environment is the main consideration during the Path and Trajectory Planning inside the body. It is important that the device is having a smooth trajectory path while navigating in the blood environment and at the same time does not cause any damage to the other cells.

Sitti M. in April 2009, Miniature Devices: Voyage of the nanorobots proposed a swimming microrobots which are propelled by artificial flagella to destroy the blood clot that threatens the patient’s life. These tiny shaped microrobots access small space inside human body that can currently be reached only by using invas-ive methods. Gayle proposes an algorithm for short distance path travel based on 2.5 D overlap.

Now a day’s self trajectory plan for path detection by the nanorobots are being practised.


Nano Robots : A Survey on Recent Development in Medical Applications [2]

Control Movement in Nanorobots

Cavalcanti A. provided some new features in the nanorobots which are effectively used as nanomedicine. The pill shaped nanorobots are employed with a self tra-jectory plan where theses nanorobots are made decision makers inside the human body. Three behavioural control techniques have been considered to control the nanorobots’ motions.

First approach used nanorobots’ small Brownian motions to find the target by random search.

In second approach, nanorobot monitors for chemical concentration intensity for E-cadherin signals. After detecting the signal, the nanorobot estimates the con-centration gradient and moves toward higher concentrations until it reaches the target.

In third approach, nanorobots at the target release another chemical, which is used as an additional guiding signal to find the target.


NanoRobots : Obstacle Avoidance Algorithm for Collective Movement in Nanorobots [3]

One of the major aspects in the movement control of a nanorobot is obstacles avoidance. Every nanorobot placed inside the human body will encounter immune system as obstacles during flowing within a human body. Thus nanorobot must use strategy for avoiding and escaping from such immune system. The nanorobot equips with sensors to detect obstacles and identifies when it has encountered. To avoid obstacles during trajectory, self organized trajectory planning is required.Even though obstacles can be all shapes and sizes, CIRCLE representation is con-sidered here for the simplicity.

The structure of obstacles can be defined as follows.

Obstacle =< Pcenter ,Pradium, Pvelocity > (Pcenter, Pradium and Pvelocity are the center point, the radius and the velocity of the moving obstacle).

Polar coordinate system, which is the two dimensional coordinate system in which points on a plane is determined by an angle and a distance, is used to find out the trajectory for obstacle avoidance. It can be used effectively for computing the de-sirable direction angle for movement trajectory of nanorobot. Each obstacle can be represented in polar coordination in the equation. The nanorobot will detect dynamic obstacles at real-time and get obstacles information and then determine the movement trajectory.


NanoRobots : Obstacle Avoidance Algorithm for Collective Movement in Nanorobots [3]

Algorithm Obstacle AvoidanceBegin1. Calculate Δd, distance between nanorobot si and obstacle2. Calculate time to collision Δtc based on Δd3. Start to generate Δθij, based on Δtc4. Calculate Cartesian coordinate’s xij,yij from polar coordinateswherexij is r*Cos(Δθij);yij is r* Sin(Δθij);r is the radius of the obstacle (0< Δθij<180)5. Move nanorobot si from xi1,yi1 to xij,yij6. Calculate coverage of range si to it’s neighbors N(si)7. If coverage value>current optimum8. Current optimum target=current selected target9. Move nanorobot si to new best position10.End ifEnd


NanoRobotics Motion Planning and Its Applications in Nanotechnology and Bio molecular Computing [4]

Major Problem being faced during Trajectory & Path Planning

Frictional Mover's Problem

We define the frictional mover's problem to be the problem of determining whether the objects in a frictional mechanical system can be moved legally from a specified initial configuration to a specified final configuration. The frictional mover's problem is undecidable by reducing the acceptance problem for Turing Machine (TM). This frictional mechanical system will have the property that the objects in this system can be moved from an initial configuration, which encodes an input string of M, to a configuration corresponding to the accepting state of M if and only if M accepts . Therefore, as the acceptance problem for Turing Machine is undecidable, so is the frictional mover's problem. This implies that there is no realistic machine that can solve this problem, it can be reduced only.

Curvature-constrained Shortest-path Planning

The curvature-constrained shortest-path problem is to plan a path (from an initial position to a final position, where a position is defined by a location and the orientation angle) in the presence of obstacles, such that the path is the shortest among all paths with a prescribed curvature bound.


NanoRobotics Motion Planning and Its Applications in Nanotechnology and Bio molecular Computing [4]

Fortune and Wilfong gave the first algorithm which computes the exact shortest paths avoiding obstacles. However, their algorithm required super-exponential time.

Agarwal, Raghavan, and Tamaki developed efficient approximation algorithms for the 2D shortest paths for a restricted class of obstacles (moderate obstacles).

Canny and Reif proved that the problem of finding a shortest 3-dimensional path, with no curvature constraints, and avoiding polygonal obstacles, is NP-hard.

Asano, Kirkpatrick and Yap proved using similar techniques the NP-hardness of optimal motion of a rod on a plane with polygonal obstacles.)

As a consequence of the above result, the curvature-constrained shortest-path problem in 3D is at least NP-hard. Although the problem in 2D is long conjectured to be a hard problem, its complexity is never stated or proved.


Mobility Enhancement in Nanorobots by using Particle Swarm Optimization [5]

The mobility enhancement is one of the key areas of research for the Trajectory and Path Planning in nanorobots. A Particle Swarm Optimization (PSO) algorithm has been introduced to achieve the mobility enhancement.

In this approach, the optimize function is used which is based on the coverage criteria, which reflects how well a target area is tracked. Suppose there are two nanorobots Si and Sj located in positions (Xi, Yi) and (Xj, Yj) with sensing ranges Ri and Rj respectively. Without loss of generality, movement of nanorobots will be addressed how Si perimeter covered by Sj and how much target area covered by both nanorobots Si and Sj.

The following three criteria are used for in the PSO

a) Movements in the Nanorobots

b) Coverage of the Target Area

c) Positioning of the Nanorobots

by the use of Positioning algorithm based of the fitness function.


Mobility Enhancement in Nanorobots by using Particle Swarm Optimization [5]

How it works

Initialize with random coordinates and initial velocities are found out based on fluid forces measured by Reynolds number. Fitness function is evaluated for each nanorobot based on the coverage of the target area. By comparing fitness value of each nanorobot over predefined threshold value, the positions and velocities are adjusted. When a particle or a nanorobot discovers a pattern that is better than any it has found previously, it stores the coordinates as new best positions. To find targets, the nanorobots use the chemicals recognition of the background concentration. If no signal was detected, a nanorobot just keeps flowing with the bloodstream to avoid spending power. When a nanorobot detects the higher concentration signal of the target, it releases another signal to attract other nanorobots. It stops attracting others when the target region has k covered.

Whenever the changes occur, the nanorobots are placed to the new best positions. The movement positions of the nanorobots are decided based on how much target area is k covered.


The Trajectory and Path Planning for the Nanorobots mainly depends upon the following factors.

1. The architecture design of the nanorobot2. Motion planning of the nanorobots3. Collision free movement of the nanorobot4. Mobility enhancement i.e. result should come up in minimum time without

compromising any of the factor.

The polar coordinate system based Particle Swarm Optimization approach has been proposed as a feasible approach for the self organized control of nanoscale robots. Obstacles and possible collisions can be avoided while still continuing on an efficient trajectory, leading towards convergence on the target. The simulation results have approved that the proposed scheme effectively constructs a self organized mobility within the optimized coverage area.

The nanorobots are a topic of advanced research work and many different technological approaches are under study. Nanorobots are at a far earlier stage of development in medical field and it can be extended to study the adaptation techniques inside our body using nanorobots. Though MRI, PET, CT (computer Tomography) can find the affected regions at tissue level. However, we can find the affected regions at the cellular level using these tiny robots.

Conclusion


ENVIRONMENT BASED TRAJECTORY AND PATH PLANNING

By : Nidhi Joshi Roll No : 2K13/CSE/27


As Robots are integrating into our living environment, it becomes im-perative to develop methods which enable them to navigate in a safe, reliable, comfortable and natural way around humans. In order to ac-complish that, robots need to predict the trajectory/path of dynamic obstacles (people, pets, cars etc).

Being able to plan paths efficiently will allow robots to be more respons-ive and robust to a constantly changing environment. It has also been observed that along with optimum path planning employing multiple robots instead of a single one for performing specific tasks can prove very useful.

Introduction and Objective


1. Bak, T.Andersen, H.J. “Trajectory Planning for Robots in Dynamic Human Environment”, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2010 at Taipei.

2. Gong Dunwei, Geng Na “Local Robot path planning with predicting band trajectories of obstacles”, IEEE Seventh International Conference on Natural Computation (ICNC), 2011 at Shanghai .

3. Mike Phillips and Maxim Likhachev, “Safe Interval Path Planning for Dynamic Environment”, 2011 IEEE International Conference on Robotics and Automation, Shanghai International Conference Center

4. Liang Liu, Qing Liu and Yongyu Qu “Improvement of D* Algorithm for Mobile Robot Path Planning in Partial Unknown Environment”, IEEE Second International Conference on Intelligent Computation Technology and Automation, ICICTA 2009.

5. Conesa-Munoz , Ribeiro, A. , Andujar, D., Fernandez-Quintanilla C. and Dorado, J. “Multi-Path Planning for a Fleet of Robots to work on Agricultural Tasks”, IEEE Congress on Evolutionary Computation (CEC), 2012.

Research References


Trajectory Planning for Robots in Dynamic Human Environment [1]

• Planning a minimal cost trajectory through a potential field. Potential field is defined from perceived position and motion of persons

• A new method introduced in RRT (Rapidly exploring random Tree algorithm) based on the cost of traversing potential field

• MPC(Model Predictive Control Approach) is taken, where only small part of trajectory is executed while a new trajectory is calculated. Planning is done in C-T space using an MPC scheme. A good motion unicycle type robot like Segway has been considered

• Input to RRT Trajectory Planner – previous best trajectory, the person trajectory estimates & dynamic model of the robot

• RRT Control Input Sampling – New vertex to be added is now found by using dynamic robot motion model and the controller to simulate the trajectory one time step. It ensures that added vertex is reachable

• Introduction of Node pruning and Trajectory Selection - If vertex corresponding to node ends up in a place where potential field has a value above specific threshold, then node is not added to the tree. Node is also pruned if time taken to reach the node is above a given threshold. Finally instead of returning the trajectory of the vertex of the last added node, the trajectory with the lowest cost is returned

• Algorithm has been demonstrated to work in a simulated pedestrian street and has been observed that it is fast as it does not have to calculate distances to all velocity vectors and is robust as well

• Conclusion-It has a better coverage over the configuration space than sampling and use of MPC enables planner to continuously plan reachable trajectory on an online system


Local robot path planning with predicting

band trajectories of obstacles [2]• Different from traditional method, predicted trajectory is a band not a curve. • PSO(Particle Swarm Optimization) is employed to solve it. It is simple and

easy to implement. We determine the risk of collision between the robot and the obstacle according to the distance between the path of the robot and the band trajectory of the obstacle so as to decide whether to plan a local

• Assumption: constant velocity to minimize energy consumption and size of obstacle 1.1 times its original size

• Once time to sample information about obstacle is known, robot begins to sample position of the obstacle at each sample time and predict its trajectory. Trajectory of the obstacle a curve is extended to band to avoid collision . At this time , selecting the width of band is crucial. If width is too small, robot may be outside of the trajectory causing the collision

• Performance Analysis – Comparing with a curve trajectory of an obstacle

• Total number of samples say n where m1 samples are in curve trajectory suggesting n-m1 are outside the curve, so chances are more that robot will collide

• Conclusion - Probability of collision between robot and the obstacle decreases greatly


Safe Interval Path Planning for Dynamic Environment [3]

• Planning in presence of dynamic obstacles is still challenging because it requires adding time as an additional dimension. Most real time approaches to path planning treat dynamic obstacle as static and constantly re-plan as they move.

• Safe interval is a time period for a configuration with no collisions and if it were extended one time step in either direction it would then be in collision.

• Developed a planner that uses state defined by config. (x,y, θ etc) and safe interval as independent variables, while only storing actual time step as dependent variable

• A* algorithm with safe interval has been used which makes a call to getSuccessors function. When generating successors, we say that s uses “wait and move” actions. It means that for each safe interval in the new config, we wait the minimal amount of time possible, so that when the move to the new configuration is used, we arrive in the new safe interval as early as possible.

• During A*, whenever a shorter path to is found, the cost is replaced with the smaller one.


A node is written which uses PR2’s lidar sensors to track people. A basic clustering of lidar points from each scan is done and the matching of clusters is done to identify dynamic or static obstacle

Result: Person was walking towards the robot , so planner had the robot duck into doorway , wait for the person to go and then proceed to its goal.

Conclusion - It proves completeness and optimality by arriving at state in earliest possible time and by providing a collision free path to the goal

Simulation: Test on PR2


Improvement of D* Algorithm for Mobile Robot Path Planning in Partial Unknown Environment [4]

• D* Algorithm combines local and global planning, makes sufficient use of global environment and information from sensors, that had been practically tested.

• An improved D* plans better path. - At first, 2D workspace with some obstacles is expressed by regularity grids

- Then optimal path is planned by using D* Algorithm which searches for the neighbor grid cells on 16 directions, so that it makes robot turning the smallest increment angle down to ╥/8


• Basic D* theory makes use of following functions:PROCESS-STATE : To compute optimal pat cost to goal.MODIFY-COST : To change the arc cost function and enter affected states on the open list.

• It is a kind of Incompletion Re-planning where in original path differs while re-planning a path. But it makes use of original planning information.

• Improved D* reduces the unnecessary path cost and makes the mobot path planning smoother by searching on 16 directions.

• Tested on WiRobotX80 - It was proved total length of path and total angles changes planned by improved D* algorithm are less than the conventional one.


Multi-Path Planning for a Fleet of Robots to work on Agricultural Tasks [5]

• Using multiple robots to handle tasks that are too complex or too expensive to be performed by a single robot. In such scenarios, path has to be planned in such a way that robots do not collide with each other. In this paper, Weed treatment in an agricultural field has been considered.

• NSGA II (Non Dominated Sorting Genetic Algorithm) is used for solving this. Aim is to determine multi-path plan that ensures lowest cost in term of both money and time for overall treatment.

• Solution is represented by an integer vector that indicates which robot should treat each col in the field.

• To define order of treatment – i> given a robot, col closest to its original position on the 2 most external cols in selected; ii>once robot has left the col it must advance to the header to the closest untreated col


• Main factors to take into account are :- Distance : to determine shortest path the covers the entire field- Turns : Minimum turns- No. of Robots Used : Balance between time and No. of Robots- Herbicide Application : Amount of herbicide depends on no. of herbicide

operations- Herbicide tank capacity : To avoid frequent refilling as need to reach to

header for that. After performing various tests, can be concluded NSGA-II offers a method of

obtaining a set with the best solutions not only for a single vehicle but for a fleet


CONCLUSION

We have presented five different approaches to path/trajectory planning in a dynamic environment for obtaining a collision free path to the target within minimum possible time and cost. Various factors have been considered within respective algorithms to evaluate the environment and provide the optimal path. We have also seen that employing multiple robots can simplify a complex task considering all parameters are taken into account while designing the path.


Artificial Intelligence Based Path Planning

By : Archana Saxena Roll No : 2K13/CSE/3


Introduction• One of the most important issues in the design and development of intelligent

mobile system is the navigation problem. This consists of the ability of a mobile robot to plan and execute collision free motions within its environment.

• This environment may be imprecise, vast, dynamical and either partially or non-structured. Robots must be able to understand the structure of this environment.

• To reach their targets without collisions, the robots must be endowed with

perception, data processing, recognition, learning, reasoning, interpreting, decision-making and action capacities. The ability to acquire these faculties to treat and transmit knowledge constitutes the key of a certain kind of artificial intelligence.

• Reproduce this kind of intelligence is a human ambition in the construction and development of intelligent machines, and particularly autonomous mobile robots.

• To reach a reasonable degree of autonomy two basic requirements are:

– Sensing is provided by onboard sensory system that gathers information about robot with respect to the surrounding scene.

– Reasoning is accomplished by devising algorithms that exploit this information in order to generate appropriate commands for robot.


References1. Dmitry Berenson, Pieter Abbeel and Ken Goldberg, “A Robot Path Planning

Framework that Learns from Experience”, IEEE International Conference on Robotics and Automation, 2012.

2. Preetha Bhattacharjee, Pratyusha Rakshit, Indrani Goswami (Chakraborty), Amit Konar and Atulya K. Nagar, “Multi-Robot Path-Planning Using Artificial Bee Colony Optimization Algorithm”, IEEE Third World Congress on Nature and Biologically Inspired Computing, 2011.

3. I. Hassanzadeh, K. Madani and M. A. Badamchizadeh,”Mobile Robot Path Planning Based on Shuffled Frog Leaping Optimization Algorithm”, 6th annual IEEE Conference on Automation Science and Engineering, 2010.

4. Masoud Samadi and Mohd Fauzi Othman, “Global Path Planning for Autonomous Mobile Robot using Genetic Algorithm”, IEEE International Conference on Signal-Image Technology & Internet-Based Systems, 2013.

5. Wing Kwong Chung and Yangsheng Xu, “A Generalized 3-D Path Planning Method for Robots Using Genetic Algorithm with An Adaptive Evolution Process”, IEEE Proceedings of the 8th World Congress on Intelligent Control and Automation, 2010.


Objective and Motivation• Given an object with an initial location and orientation, a goal location and

orientation, and a set of obstacles located in workspace, the problem is to find a continuous path from the initial position to the goal position, which avoids collisions with obstacles along the way.

• The motion planning problem is divided into two sub-problems.– The findspace problem is construction the configuration space of a given

object and some obstacles. – The findpath problem is in determining a collision-free path from a given

start location to a goal location for a robot.

The major difficulties in the path planning approach are: expensive computation is required to create the configuration space from the robot shape and the obstacles and the number of searching steps increases exponentially with the number of nodes.

Thus, there is a motivation to investigate the use of algorithms for solving these problems, which has the potential for much increased speed of calculations.


A Robot Path Planning Framework that Learns from Experience[1]

• A long-standing goal of research in robotics is to create a robot whose ability to perform a task improves with experience through performing similar tasks.

• A path planning algorithm is only given the robot and environment models along with the start and goal configurations and asked to generate a path.

• While this approach, planning-from-scratch (PFS), is general, it can produce unacceptably long planning times for difficult problems. Even if the robot previously generated a path for a very similar task, PFS affords no way to take advantage of this previous computation.

• This paper propose a framework, called Lightning, for planning paths in high-dimensional spaces that is able to learn from experience, with the aim of reducing computation time.


The Lightning framework (see Figure) consists of two main modules, which are run in parallel:

* planning-from-scratch (PFS)* Retrieve-Repair (RR)Given a new query, both modules are started simultaneously and the first path produced by either module is executed on the robot while the other module is stopped.

• Diagram of the Lightning framework


• The Lightning framework has several advantages. – There is no need to arbitrarily pre-generate some number of paths before

running the system.– These paths will require little repair and feasible paths will be produced

much more quickly than using a sampling-based planner to plan from scratch.

– The system can run over the lifetime of the robot, even as the robot transitions between different types of tasks.

• The contributions of this paper are the following:– A general and efficient framework for building and using a path library.– A formal definition of the problem of retrieving and repairing a path from

the library.– A fast method for retrieving paths based on the above definition.– Experiments showing a range of situations in which the Lightning

framework outperforms PFS.


Multi-Robot Path-Planning Using Artificial Bee Colony Optimization Algorithm[2]

• The artificial bee colony optimization (ABC) technique is a population based algorithm for numerical function optimization that draws inspiration from the stochastic behavior of foraging in bees.

• This paper proposes an alternative approach to path planning of mobile robots using the ABC optimization algorithm. The problem attempts to determine the trajectory of motion of the robots from predefined starting positions to fixed goal positions in the world map with an objective to minimize the path length of all the robots.

• Proposed optimization scheme outperforms two well-known algorithms with respect to standard metrics, called average total path deviation and average uncovered target distance.

• This path-planning problem is formulated by a centralized approach, where an iterative algorithm is invoked to determine the next position of all the robots satisfying all the constraints imposed on the multi-objective function. The algorithm is iterated until all the robots reach their destination.


• The fitness function of the ABC has two main components: – The objective function describing the selection of next position on an

optimal trajectory.– The constraint representing avoidance of collision with other robots and

with static obstacles, placed co-ordinates.• In ABC algorithm, the colony of artificial bees contains three groups of bees:

– A bee waiting for making decision to choose a food source , onlooker.– A bee going to the food source visited by it previously, employed bee.– A bee carrying out random search, scout.

• In ABC algorithm, the position of a food source represents a possible solution of the optimization problem and the nectar amount of a food source corresponds to the fitness of the associated solution. The number of employed bees and onlooker bees is equal to the number of solutions in the population.

• The ABC algorithm consists of following steps:– Initialization– Placement of employed bees on the food sources– Placement of onlooker bees on the food sources– Scout Bee Phase

This problem is taken care of in ABC as the scout is assigned the task of searching for new food sources, after the employed and onlooker bees have exhausted the profitable food source obtained after certain iterations of the program.

Simulation results obtained in this experiment show that ABC algorithm performs better than the other algorithms and can be efficiently employed to solve practical engineering problems with high dimensionality.


Mobile Robot Path Planning Based on Shuffled Frog Leaping Optimization Algorithm[3]

• Proposed a method of robot path planning in partially unknown environments based on shuffled frog leaping (SFL) optimization algorithm.

• The proposed algorithm allows a mobile robot to navigate through static obstacles and finds its path in order to reach from its initial position to the target without collision.

• Consider a group of frogs leaping in a swamp. The swamp has a number of stones at discrete locations where the frogs can leap onto. The goal of all the frogs is to find the stone with the maximum amount of available food as quickly as possible through improving their memes. The frogs can communicate with each other, and can improve their memes by passing information on each other. Improvement of the memes results in changing an individual frog’s position by adjusting its leaping direction and step size.

• The difference and potential advantage of the memetic representation over genetic algorithms is that information is passed among all individuals in the population, whereas in a genetic algorithm only parent–sibling interactions are allowed.


• In the SFL, the population is partitioned into subsets called memeplexes. The different memeplexes are considered as different cultures of frogs, each performing a local search. Within each memeplex, the individual a frogs hold ideas, that can be influenced by the ideas of other frogs, and evolve through a process of memetic evolution. After a defined number of memetic evolution steps, ideas are passed among memeplexes in a shuffling process. The local search and the shuffling processes continue until defined convergence criteria are satisfied.

• The procedure of real-time path planning is divided into the following three steps. – Transform the path planning problem into an optimization one, and define

the optimization objective based on the target and the obstacles in the environment.

– Apply the SFL to solve the above optimization problem. During this process, the position of the globally best frog in each iterative is selected, and the robot reaches these positions in sequence.

– The robot constantly updates the information detected by its sensors, and the optimization objective function changes accordingly.

• Each position to be reached by the robot is evaluated based on the distance between itself and the target and obstacles in the environment. Therefore the nearer a position to the target, the greater the fitness of the position should be the proposed method is simulated and implemented in a Khepera II robot in webots™ simulator. The simulation results show the effectiveness and robustness of this new algorithm and the robot moves smoothly toward its target.


Global Path Planning for Autonomous Mobile Robot using Genetic Algorithm[4]

• The proposed method is a global path planning method with hexagonal grid map modeling. It reads the map of the environment and plans the optimized path by using GA method simulated in MATLAB R2012b software.

• Localization and Planning are fundamental components of navigation. The ability to estimate the accurate position of obstacles in the environment based on perceptional sensors is known as localization while Planning is defined as generating the map through the environment.

• The initial population may include impassable paths and there is a possibility for the next generation to have the same issue and convert the search environment into a graph.

• Based on the original genetic algorithm, the initialization, fitness function, Selection and other parts should be defined.



Results

• The GA has a good capability to improve the path planning algorithm and generate an optimized path with respect to path length and cost of the path.

• The use of special

selection and crossover functions were able to reduce the GA calculation time and led to better path planning algorithm.


A Generalized 3-D Path Planning Method for Robots Using Genetic Algorithm with An

Adaptive Evolution Process[5]• Propose a new criterion for the selection of genetic operators during evolution

in order to further facilitate the searching efficiency.

• Propose a generalized 3-D path planning method for robots using GA with an adaptive evolution process.

• Introduce a new genetic operator, called Bind-NN which randomly divides and recombines an elitist chromosome based on nearest neighbor. It not only keeps the randomness of the genetic evolution (randomly cut of a chromosome) but also introduces a greedy local search effort by nearest neighbor recombination of genes segments.

• The proposed algorithm consists of three major contributions. – A complete framework of modeling and solving a 3-D path planning problem– The use of a new genetic operator which facilitates the searching

effectiveness (final solution) – A genetic operators selection scheme which enhances the searching

efficiency (time for convergence).


• For the workspace of a robot, it used as a frame structure. Different check points can be defined by a user at those edge points. The length of an edge represents the distance between the two edge points.

• The length a chromosome is equal to the total number of check points.

• Fitness function counts the total distance travel (DT) along the path given by a chromosome.

• During the evolution process, different chromosomes are first grouped into different portions each with equal size. For each portion, a chromosome with the highest fitness value is selected. Reproduction is then performed.

• Genetic operators are – Genes Flip– Genes Swap– Gene Slide– Bind-NN: NN means nearest neighbor

Experimental results show a significant improvement in terms of search effectiveness and efficiency. The proposed algorithm describes a complete framework of modeling and solving a 3-D path planning problem.


Conclusion

This investigation is based on AI for path planning as well as development of autonomous navigation learning algorithm for a mobile robot, functioning in a known and partially unknown environment in presence of obstacles.


Trajectory Planning in

Air Craft

By : Nitin Saini Roll No : 2K13/CSE/28


Introduction• Several concepts in Air Traffic Management involve the use of long term

prediction of aircraft trajectories as a way of ensuring efficient and conflict free flights from gate to gate.

• The mission effectiveness of tactical aircraft can be assessed by methods ranging from using mathematical models of the aircraft and threat weapon systems to flight testing tactics in a simulated combat environment.

• Investigate the feasible sky-lanes for aircraft by minimize the impact to populated area and the threats from terrain obstacles to the aircraft.

• Preferred trajectories from several aircraft will inevitably lead to conflicts which must be resolved by constraining one or more aircraft to fly a non-optimum trajectory. Such traffic avoidance constraints may occur at any point in a trajectory and may involve changes to the lateral track, to altitudes or to timing. The aim is to modify an optimum trajectory as little as possible whilst still meeting the necessary constraints.


Research Publications

• Trajectory Planning Algorithm Based on Quaternion for 6-DOF Aircraft Wing Automatic Position and Pose Adjustment Method

• Real-time trajectory planning for UCAV air-to-surface attack using inverse dynamics optimization method and receding horizon control

• Route Planning for Light-Sport Aircraft in Constrained Airspace .

• Robust optimization of aircraft weapon delivery trajectory using probability collectives and meta-modeling.

• Aircraft trajectory planning under uncertainty by light propagation


Trajectory Planning Algorithm Based on Quaternion for 6-DOF Aircraft Wing Automatic

Position and Pose Adjustment Method

Introduction• The precise adjustment of wing position and pose is a primary and necessary

condition for wing-fuselage accurate connection, and the connection efficiency and precision are closely associated with wing position and pose adjustment.

• Wing-fuselage connection is one of the most important aircraft final assemblies whose quality plays a great role in aircraft flight safety. Many assembly points need to be connected accurately during the connection, and wing as well as fuselage has heavy mass and complex shape, therefore the connection is a complicated assembly process.

Aim• The aim of wing trajectory planning is to solve intermediate position and pose

between the initial and target position and pose during the adjustment process. In engineering application, wing should move smoothly, and beginning and terminating velocities as well as accelerations should be zero to eliminate impact.

• The traditional adjustment of wing by manual labor employs a lot of craft equipment. Moreover, it is inefficient with low and unstable precision.

• The traditional adjustment method cannot satisfy final assembly requirements of modern civil aircraft.



Methodology• ARJ21, is a jet manufactured with traditional adjustment method for wing-

fuselage. Based on the automatic and digital assembly technology, a 6-degree of freedom (6-DOF) wing automatic position and pose adjustment method is proposed to improve ARJ21 wing adjustment efficiency and precision.

• The method combines laser tracker tracking and measuring, automatic positioning and wing-fuselage connection integrated software system.

• According to initial, target position and pose, as well as engineering constraining conditions, wing trajectory planning algorithm based on quaternion is proposed to avoid singular and multiple solutions of Euler angle.

Results• The method realizes accurate adjustment of wing position and pose, improves

wing-fuselage connection efficiency and employs less craft equipment. The method employs fewer servo motors than the over-actuated position and pose adjustment method.

• Simulation results show that the automatic position and pose adjustment method has good dynamic characteristics, steady response performances, and all engineering constraining conditions are satisfied during the adjustment process.

Conclusion• The automatic adjustment method can also be used for other aircraft wings in

addition to ARJ21, and the method provides theoretical and practical basis for China-made large aircraft wing-fuselage automation connection.


Real-time trajectory planning for UCAV air-to-surface attack using inverse dynamics optimization method

and receding horizon control

Introduction• Unmanned combat aerial vehicles (UCAVs) are equipped with missiles or

guided bombs, and can launch an attack and return to base or land on aircraft carriers. These vehicles will provide more tactical flexibility than a cruise missile because they will be able to loiter in the area and search for a moving target, and then strike it with their weapons. Compared with a reconnaissance flight, during the course of an air-to-surface (A/S) attack, UCAVs will autonomously fly with highly aggressive maneuvers and fly frequently on the fringe of the flight envelope, to meet the pre-weapon launch targeting conditions as quickly as possible, while avoiding the detection and engagement of surface-based air defenses. The idea of directly commanding the UCAV to track and attack a moving target, would be impractical, considering the fast and complicated dynamics of a typical UCAV. Therefore, one of the key technologies of UCAV is the autonomous, on-board, real-time trajectory planning.

Aim• The objective is to develop an efficient real time trajectory planning

framework for the typical UCAV platform, which can provide a solution that optimizes the system objective while satisfying the boundary, path and platform kinematic and dynamic constraints, as well as sensor and weapon employment constraints


Method• This paper presents a computationally efficient real-time trajectory

planning framework for typical unmanned combat aerial vehicle (UCAV) performing autonomous air-to-surface (A/S) attack. It combines the benefits of inverse dynamics optimization method and receding horizon optimal control technique.

• Firstly, the ground attack trajectory planning problem is mathematically formulated as a receding horizon optimal control problem (RHC-OCP). In particular, an approximate elliptic launch acceptable region (LAR) model is proposed to model the critical weapon delivery constraints.


• Secondly, a planning algorithm based on inverse dynamics optimization, which has high computational efficiency and good convergence properties, is developed to solve the RHCOCP in real-time.

• Thirdly, in order to improve robustness and adaptively in a dynamic and uncertain environment, a two-degree-of-freedom (2-DOF) receding horizon control architecture is introduced and a regular real-time update strategy is proposed as well, and the real-time feedback can be achieved and the not-converged situations can be handled.

Results• The results show that the framework is able to generate both feasible

and near-optimal attack trajectories very efficiently, even in the presence of moving target or obstacles, as well as disturbances due to wind. The results show that the computational speed of inverse dynamics method is more than an order of magnitude faster than that of GPM, at small loss of optimality


Route Planning For Light-sport Aircraft In Constrained Airspace

Introduction• Light-sport aircraft is defined as simple-to-operate, easy-to-fly aircraft by

Federal Aviation Administration in United States. It also includes powered parachutes, weight-shift control aircraft, balloons, gyroplanes, gliders and airships. These aircraft can be categorized as standard, experimental amateur-built, experimental light-sport aircraft (E-LSA), or special light-sport aircraft (S-LSA). The airspace for the activities of light-sport aircraft in Taiwan is strongly restricted due to mountainous terrains and populated areas.

AIM• To investigate the feasible sky-lanes for light-sport aircraft by minimize the

impact to populated area and the threats from terrain obstacles to the aircraft.

Methodology• The route planning problem for LSA was dealt with three stages: (1) threat

identification and modeling; (2) the spatial segmentation and the generation of initial routes; (3) the search of an optimized route.

• In this work, the Dijkstra algorithm is adopted to find the optimized route. Dijkstra algorithm is one of the most popular graph search algorithm to solve the single-source path problem for a graph with non-negative edge path costs. Its inefficiency can be ignored by higher computing capability of modern computers. The efficiency can be improved by largely reducing the data number considering only the obstacles higher than cruise height.


Result• To validate the feasibility of this technique in complex terrain constraints,

three scenarios of different types of lanes are adopted for the simulation. They are

• A straight lane, • A continuous curved lane for curved valleys, • A dispersed prohibited areas and obstacles between the departure airfield and

the destination.

Conclusion• This work provides one feasible way to construct the safe sky lanes for LSA

flight for expanding the airspace


Robust Optimization Of Aircraft Weapon Delivery Trajectory Using Probability

Collectives And Meta-modelingIntroduction• Weapon delivery planning (WDP) is mainly considered as an issue of path planning, in

which a flight path is generated to guide an aircraft to reach a weapon delivery point. The development of trajectory optimization techniques, aerodynamic models have been introduced to solve aircraft motions under a set of position and attitude constraints.

Aim• The aim of this paper is to create a robust and efficient approach for aircraft trajectory

optimization under complex mission environments.

Method• In this study, a new framework named tactics template is proposed to model an aircraft

weapon delivery trajectory through templates of basic flight maneuvers (BFMs). The BFMs are a set of parameterized simple maneuvers, such as level turn, climb, and so on. The combination of the BFMs (also called BFM sequence) can build up complex aircraft maneuvers. A template represents a specified BFM sequence containing a set of maneuver variables, through which the profile of the trajectory can be determined.

Result• Results reveal that the proposed approach is capable of generating robust and optimal

trajectories with both accuracy and efficiency.


Aircraft Trajectory Planning Under Uncertainty By Light Propagation

Introduction• The Light Propagation Algorithm (LPA) was introduced to successfully manage the full day

traffic over the French airspace. Light Propagation Algorithm (LPA) is based on an optical analogy and its objective is to find optimal 4D trajectory, for each aircraft. It minimizes a criterion based on a local metric. However, LPA does not take into account the uncertainties present in Air Traffic Management (ATM)

Aim• To find an optimal 4D trajectory for each aircraft that avoids conflicts with other aircraft, a

destination point and optimizes a cost function which depends on the travel duration.

Method This paper deals with an important Air Traffic Management (ATM) problem that involve,

generation of sets of 4D conflict-free trajectories (the tactical planning problem). A methodology was developed that permit to solve real-world conflict resolution problems under uncertainty. This methodology includes two steps. The first step takes into account the conservative open-loop uncertainty model whereas the second step imposes RTA (Real Time of Arrival constraints) points on some conflicting trajectories. The goal of RTA points is to impose an aircraft to be at a specified position at some given time.

Uncertinity-LPA assumes that each aircraft follows perfectly its intended trajectory during this time window. This assumption is not realistic. The uncertainty that we consider in this study, is the difference between the aircraft actual position and its intended position during the prediction time horizon Δt. This uncertainty depends on weather data accuracy, including actual wind speed and actual temperature at the aircraft flight level.



Result Results show that taking into account uncertainty on aircraft positions,

there has been a considerable increase in the problem difficulty. Indeed, the number of detected conflicts is 1.5 times greater than in the case without uncertainty, due to the protection-area volume growth, for the same traffic situation. On the other hand, the conflict resolution percentage drops to 88% in the case of open-loop uncertainty, and improves to 93% when applying RTA point.

Conclusion Indeed, RTA (Real Time of Arrival constraints) can be used, but there are

probably more sensible rules that would achieve results that could be almost as good as the ones obtained in the framework without uncertainty. Hence it is concluded that future work will improve the way they place RTA


Trajectory and Path Planning in

Underwater Robots

By : SUNIL BISAIYA Roll No : 2K13/CSE/30


TRAJECTORY PLANNING UNDERWATER

• A trajectory planning underwater aim to identify path that is safe and efficient in unsafe water-space.

• Submarine robots are one of the widely applied robots, which are used in submarine exploration and marine environmental research.


Research Publications

• LOW COST SUBMARINE ROBOT Ponlachart Chotikarn1, Werapong Koedsin2, Boonlua Phongdara3 and Pattara Aiyarak1* 1 Innovation in Physics and Intellectual Properties Research Unit,Department of Physics, Faculty of

Science,2 Faculty of Technology and Environment,Prince of Songkla University, Phuket Campus, Kathu, Phuket, 83120, Thailand. 3 Department of Physics, Faculty of Science,Prince of Songkla University, Hat Yai, Songkhla, 90112 Thailand.

• AUV PATH PLANNING: AN A* APPROACH TO PATH PLANNING WITH CONSIDERATION OF VARIABLE VEHICLE SPEEDS AND MULTIPLE,OVERLAPPING,TIME-DEPENDENT EXCLUSION ZONES.

Kevin P. Carroll, Stephen R. McClaran, Eric L. Nelson, David M. Barnett, Donald K. Friesen, and Glen N. Williams,Computer Science DepartmentTexas A&M University

• LOW COST AUTONOMOUS UNDERWATER VEHICLE FOR PIPELINES AND CABLE INSPECTIONS.

CONICET and INTELYMEC Group, Electromechanical Dept., Engineering Faculty, Universidad Nac. del Centro Prov. de Buenos Aires - UNCPBA, Argentina;A. Calvo Ibanfiez GTE Group, Physic Dept., Universitat de les Illes Balears - UIB, Spain; H. J. Curti INTIA Group, Computer Sciences Dept., Exact Sciences Faculty, Universidad Nac. del Centro Prov. de Buenos Aires - UNCPBA, Argentina;A. F. Rozenfeld,Physic Dept., Universitat de les

Illes Balears - UIB, Spain

• OPTICAL GUIDED AUTONOMOUS DOCKING METHOD FOR UNDERWATER RECONFIGUARABLE ROBOT

Donny Sutantyo, David Buntoro, and Paul Levi,Institute of Parallel and Distributed System,University of Stuttgart,Stuttgart, Germany

• EXPANDING THE TIME HORIZON IN UNDERWATER ROBOT SWARMS.• Vincenzo Fioriti1, Stefano Chiesa1 , Fabio Fratichini1 and Andrea Arbore 1 ENEA, CR Casaccia, UTTEI-ROB Unit, Robotics Laboratory 2 Aletea SpA, Rome, Italy


Low Cost Submarine Robot• A submarine robot is a semi-autonomous submarine robot used mainly for

marine environmental research.

• We aim to develop a low cost, semi-autonomous submarine robot which is able to travel underwater. The robot’s structure was designed and patented using a novel idea of the diving system employing a volume adjustment mechanism to vary the robot’s density.

• A light weight, flexibility and small structure provided by PVC can be used to construct the torpedo-liked shape robot.

• Hydraulic seal and O-ring rubbers are used to prevent water leaking. This robot is controlled by a wired communication system.

• This robot can be moved up and down by controlling air pressure in its ballast, while in the study of Bokser et al. (2003) the movements of the robot were controlled by adjusting air pressure using water ballast.

• This robot communicated with a computer using radio waves.

Hardware Specifications

• The low cost submarine robot is a small size robot with 81-116 centimeters in length, and 10.16 centimeters in diameter.

• The maximum weight of the robot is about 10 kg.

• Most of electronic devices, motor, and batteries are in the hull, which is watertight. O-ring rubbers were used to make a watertight system.


Conceptual Diagram of Robot’s Hardware


Discussion and Conclusions

• The low cost submarine robot is a semi-autonomous submarine robot used for marine environmental research.

• We aim to develop a low cost, semi-autonomous submarine robot which is able to travel underwater.

• The robot’s structure was designed and patented using the novel idea of the diving system employing a volume adjustment mechanism to vary the robot’s density. When the robot volume decreases, the density increases, so that the robot can move downward and vice versa.


Path Planning of Autonomous Underwater Vehicles in Current Fields with Complex Spatial

Variability: an A* Approach• Autonomous Underwater Vehicles (AUVs) operate in ocean environments

characterized by complex spatial variability which can jeopardize their missions.

• This work explores the benefits, in terms of energy cost, of path planning in marine environments showing certain spatial variability.

• Extensive computations have been carried out to calculate, by means of an A* search procedure, optimal paths on ocean environments with different length scale of eddies and different current intensities.

• To get statistical confidence, different realizations of the eddy field and starting-ending points of the path have been considered for each environment.

• Substantial energy savings of planned paths compared to straight line trajectories are obtained when the current intensity of the eddy structures and the vehicle speed are comparable.


Path planner

1. Monitoring Resources2. Planning with Multiple heeds3. Planning with Corridors4. Planning along Great Circles

Path Generation• The path planner is given a start task point and a goal task point along with

constraints about the path that is to be generated between them.• The planner will attempt to generate a minimum-cost, collision-free, great circle

path that meets the time and resource constraints.• A path is considered collision-free if it does not cross obstacles, active exclusion

zones, or land masses (collectively referred to as non entry zones).• In the event that a direct path does not exist between the start and goal task

points, the planner will insert navigation points to guide the AUV around non entry zones. If an exclusion zone blocks the desired path.

A* ALGORITHM• It is implemented as a search engine to find the optimum path in a given ocean

condition.• The traditional Euclidean distance between field point and the destination goal,

divided by the maximum possible nominal speed was employed like Heuristic function.

• This election ensures the heuristic cost will always lower than the actual cost to reach the goal from a given node and thus the optimum solution is guaranteed.



• This paper describes the implementation of an A* based path planner for an AUV.

• The path planner maintains a number of databases to facilitate the planning process. These include bathymetry, exclusion zone, obstacle, and ocean current databases.

• Features of the path 82 planner discussed are monitoring resources, planning with multiple speeds, planning along great circles, super sampling of large quadrants, and refining paths.

• The examples demonstrate that the path planner is able to generate simple paths, paths around land masses, paths at specific depths ,paths through obstacle fields, and paths through/around multiple complex, time-dependent exclusion zones.


Autonomous underwater vehicles for submarine Cable and Pipeline Inspection

• Pipeline trajectory may be estimated from special sensors like a multi-beam echo sounder (MBE), a sensors like a multi-beam echo sounder (MBE), a DIDSON sonar (DID), cameras (CAM) and others.

• This information is combined in a sensor fusion module (SFM) yielding a position and direction estimate of the target to be inspected.

• From these data, the DMP is able to decide a trajectory according to different situations like searching a pipeline, following it, navigating closer to it, or recognizing other objects surrounding it.

• The Path Planner in this architecture only decides if the desired trajectory given by the DMP is possible or not, according to the outcome of the OAS.

• The Obstacle Avoidance System (OAS) receives data from a forward looking multi-beam sonar.

• When an obstacle is detected, a near possible waypoint is proposed to correct the desired trajectory from the DMP.

• Finally, guidance and control systems modules command the from the 4-waypoints trajectories, vehicle actuators (propellers, rudders and pumps).


Expert System Based Dynamic Planner

• When doing an inspection, many situations might appear (like the sudden appearance of a fishing net, a complex pattern of more than one pipeline over the seabed, or a detour due to obstacle detection, and many others) in which the DMP should exhibit an "intelligent" behavior.

• To yield a desired trajectory, the actions are organized in a set of few simple subtasks: find start, search, back to start, skip, and track.

• Then the final trajectory of the AUV is built by one of these sub tasks or by a concatenation of them.


AUVI PROTOTYPE


Discussion and Conclusions• Successful simulation results of the control system based on a PI-

MIMO control and experimental results on a terrestrial 2D platform were concluded.

• This OAS is based on a neural network whose parameters are tuned using evolutive programming. The key aspects of the design consideration of the software and hardware elements in the prototype were presented in this article, as a low-cost adaptation of the AUTOTRACKER vehicle.

• In particular, the dynamic mission planner based on an expert system was described in algorithms. This mission planner showed that this artificial intelligence approach is able to re-plan the vehicle trajectory while in the mission, taking into account the mission settings, the changing underwater environment and the situation of the target under inspection.

• The new low-cost experimental prototype presented here, will surely be an adequate test-bed for the new task and path planning.


Optical-guided Autonomous Docking Method for Underwater Reconfigurable Robot

Four major technological challenges in the field of modular underwater robots:

• Design of the mechanical systems required for docking, underwater sensing system for guiding the docking.

• An algorithm for performing

• The autonomous docking mechanism.

• An algorithm for distributing and synchronizing anguilliform

swimming gait.


ANGELS ROBOT AND DOCKING MECHANISM

• The ANGELS robotic platform is composed of nine reconfigurable AUVs that can navigate autonomously as single agents or can be serially connect together in chain morphologies composed of more than three AUV s that that are capable of anguilliform swimming.

• The possibility to exploit different morphologies aims at improving the versatility of the system: a swarm of single agents can effectively spread to investigate the environment, while the AUVs serially connected can cover long distances by exploiting an energetically efficient undulatory swim.

• AMOUR is the only other underwater robotic system capable to autonomously reconfigure by vertically stacking and unstacking functionally different modules (e. g. batteries, propellers, buoyancy mechanisms and sensors).


ANGEL WITH ITS MAIN SYSTEMS


SENSING AND COMMUNIC ATION FOR UNDERWATER DOCKING SYSTEM

• Blue light system is selected as the main part for the sensing and communication architectures.

• The choice of using optical system is due to its capability to be modulated/encoded and due to its directional pattern.

EXPERIMENTS

• The algorithm developed for guiding the docking is based on the master-slave approach.

• The robot with the master status, which is selected for robot with lower ID number, is kept in a fixed position.

• The master robot transmits blue-light signals to the slave robot during the whole docking procedure.

• A. Using encoded blue-light and digital compass for guiding the docking.• B. Using encoded blue-light, Analog blue-light, and Compass for guiding the

docking

Discussion and Conclusions• By combining the blue light system and the compass, it is possible to create an

autonomous docking method that can work at a inter-robot distance up to five times the robot body length(total distance of 100 cm).

• The addition of the analog blue light circuit for measuring gradient at low distance « 20cm) improves the performance of the docking process.


Expanding the Time Horizon in Underwater Robot Swarms

• The time delays affecting the diffusion of information in an underwater heterogeneous robot swarm in a time-sensitive environment.

• Each member of the swarm must update its knowledge about the environment as soon as possible, thus every effort to expand the time horizon is useful.

• A group of underwater robots resembles closely a fish swarm, suggesting to use the properties of the biological swarm: coordinated movements, decentralized control, small interaction scale, minimal information broadcast.

• The biologically inspired swarm control has advantages over the more complex but single robot: it covers a larger area, is fault tolerant, is self-aware.


Goals

• Depending on the local underwater environment, the speed of acoustic waves in water varies around 1500 m/s.

• The other source of delay is the management of the acoustic channel by proper MAC (medium access control) protocols.

• We focus on the reduction of delays when the fast propagation of short warning messages to the whole swarm is needed.

• We consider the existence of a small group Of AUV equipped with high quality communication devices able to reduce the transmission time with their closest neighbors.


Scenarios and Data Preparation

The Simulation Scenarios• Scenario 1, all delays are derived from an uniform probability distribution.

• Scenario 2, each link between two nodes is affected from its own Gaussian distribution with positive support, because delays are strictly positive.

The Configurations • The configurations assumed by the AUV (in the following called graphs or

networks) considered in this prepare the random Erdos-Renyi (E-R), a graph whose nodes are connected almost randomly; the Grid, a regular disposition of nodes; the Small-World (S-W), a configuration between the grid and the random graph; the Galaxy, a group of interconnected star configurations; the Cluster, a group of interconnected ring configurations.


The Optimization Methods• We are interested in those who can determinate a small set of nodes (the

“budget”) equipped with particular spreading influence.

• The phenomenon is analogous to the diffusion of a malware or a real virus.

• The methods we use are: AV11, degree centrality, betweenness centrality and finally the random choice (in order to check results against the banal predictor).

• Others parameter are available, but these are probably the more relevant to our investigation.

• The degree centrality is the simples algorithm. It suffices to count the number of links connected to a node. It is also intuitive that an high degree designate an “influent” node (called hub).

• Betweenness centrality (BC): total number of shortest paths between every possible pair of nodes that pass through the given node.

• Betweenness looks for vertices connecting separated sub graphs.• AV11: selects a subset of k nodes all at once, according to spectral

combinatorial methods. The selected subset may be optimal or suboptimal with respect to the brute-force method.

• Spectral methods are able to analyze the dynamical behavior of graphs from the static appearance of the topology.

Protocol• The protocol we consider here is a gossip – like (sometimes called “epidemic

spreading”) protocol, i.e. each node-agent in the swarm broadcasts the message to the one-hop neighbor.

• The diffusion process is started by one node and must reach every node of the swarm within a finite time (the time horizon)



• Absolute times should not be regarded as indicative of a general behavior, because of the too wide variation of the conditions in the real marine environment.

• The important point here is the relative time difference among the topologies in each scenario.


Thanks

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Path and Trajectory Planning