DESIGN AND IMPLEMENTATION OF PATH PLANNING ALGORITHM FOR WHEELEDMOBILE ROBOT IN A KNOWN DYNAMIC ENVIRONMENT

BY

NITISH KOYYALAMUDIK-ID: K00346319

INSTRUCTOR

DR. LIFFORD MCLAUCHLAN

DEPARTMENT OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE

ABSTRACT

Must ensure an optimal path for mobile robot path planning. Best possible paths, time, energyconsumption, etc. Path planning of robots also depends on static or dynamic operating environmentsuch as a known or unknown. This article uses A * algorithm and genetic algorithm research on globalpath planning. Known as a dynamic environment, and communicate the control station will calculatethe shortest path to mobile robots and a robot will traverse the path to achieving the goal.

Trace the path traversed by the robot control station. The shortest path for mobile robot navigation, ifrobots detect doomed any obstacle in your path, mobile robots will update the information relating tothe environment, and place this information will be sent to the control station. Then the control station,with updated maps and new starting position and recalculate the new shortest path of destination, ifany, and move to the robot, it can reach its destination. This technology has been implemented and inthe real world of experiment and simulation of a wide range of test run. Results show that technologyeffectively calculates the known the shortest path in your environment in dynamic and allows robots tocomplete tasks quickly.

Key Words: A* algorithm, path planning, mobile robot

1. INTRODUCTION

Path Planning of Mobile robot is usually stated as given the description of mobile robots andenvironments, planned two specified locations, between start and end points of the path. Paths shouldbe free of bumps and some optimization condition is met (that is, minimum cost paths). According tothis definition, the path planning is classified as a problem of optimization. Researchers defineddifferent techniques as the solution for the path planning based on various factors like:

1. Environment type (i.e., static or dynamic),2. Path Planning Algorithms (i.e., global or local).

The environment doesn’t contain any moving object is known as the static environment, in addition tothe navigation of robot moving dynamic environment with dynamic objects (that is, human beings,mobile machines, and other mobile robots). Path Planning of the global algorithm requires allenvironment is fully aware of the static environment. While, path planning of local refers toimplementing the path planning while the robot is on the move; other words, A* algorithm is having anability of new path-finding accordingly to the changes in the environment.

1. SYSTEM ARCHITECTURE

Wheeled mobile robot composed of an infrared range finder, position encoder, and communicationmodule soon. 3 Infrared range finder detects obstacles were placed on the RIGHT side, FRONT sideand LEFT side of the robot, as in Figure.1. Position encoders allow a robot to measures itself from thelocation how it is passed from one location. With Communication unit, (Xbee) allows robots to giveand take information.

Figure1: Wheeled Mobile Robot

Grand Central Terminal by the PC desktop and the (Xbee) wireless unit. Path planning algorithm forcentral train station will be running will be wirelessly transmitted to a robot. The environment isassumed to be known as grid environment, here the robot's position expressed in a Cartesian coordinatesystem of two-dimensional. The motion of Robot is assumed to be in horizontal and vertical directionisn't considered to be the movement of diagonal.

The site and assumptions are given below: Mobile robot is assuming as a point in time and size occupies only one cell A series of sensors are equipped, position encoders and communications set 4- directions means NORTH, EAST, SOUTH, WEST are movable

1. DESIGN AND IMPLEMENTATION

3.1 Central StationDesktop as a central control station, A* algorithm for the calculation of the shortest path. The algorithmoutput is moving (forward, backward, left, right, etc) sequences were communicated through wirelesscommunication (Xbee) mobile robot. RS-232 serial port is applied where the pins will Transmit (Tx)and Receive (Rx). Both Coded algorithm and serial port are accessed with “C++”. Serial port is joinedby a serial cable to the (Xbee) communication unit.

3.2 Mobile RobotEmbedded in “C” programming for mobile robots, navigation using the position encoder the desiredpath. Distance or angle of rotation can accurately control by the position encoder. Commands that canbe received wirelessly with the Xbee from the central train station. Once get on to the target, mobilerobots will give to the Central unit.

3.2.1 Localization of RobotInitial direction is EAST assumes for that robot. Mobile robot and then calculate its position using analgorithm in the grid. I and j represent the x and y axis movement respectively. Figure.2 shows theposition values for j and i if it has changed, and to move in various directions.

Robots initial direction is assumed as EAST. The mobile robot will then calculate its position in thegrid using an algorithm. Let i and j represent the movement in x and y axis’s respectively. Figure.2shows the change in location values i and j, while moving in different directions.

Figure.2: Robots Orientation

ORTN values, N, S, E and W said the direction of the robot in the North, South, East and West,respectively. ROTN on behalf of the robot spin around and F, R, L, the robot movement. Assuming theinitial orientation of the robot to the East is the ORTN = E. Robot localization pseudo code is as below

1) If ROTN=F and ORTN=E, then ORTN=E and i++;2) If ROTN=R and ORTN=E, then ORTN=S and j++;3) If ROTN=L and ORTN=E, then ORTN=N and j-- ;

Robot motion every time it updates its value orientation and position values in the x and y axis. Robotscontinue to look for obstacles in the path. Figure.4 shows Setup flow chart.

Figure.3: Flow chart

1. EXPERIMENTAL SETUP

Size of the mesh (20x20) cm arrangements, which obstacles are saved and fed to the central station.Central station will calculate the shortest route, which would communicate wirelessly to the robot andbegin by specifying the path for mobile robots. If the robot detects obstacles in the specified path, therobot stops moving; their location and obstacle location. Such information and directions will be sent tothe central unit. Update of table's central railway station, assigns the current robot position as a startingpoint. Central station rerun algorithms and smallest path if one robot will be notified. The 5×3 grid isapplied. Obstacles matrix provides information of current obstacles in a known environment. Nodes ofstart and destination nodes are given.

Central station inputs contain:

1. Source location 2. Destination location3. Giving the obstacle location in the Obstacle table

Figure.5: Output window at central station

The output central station has:

1. Time to Computation 2. Routing movement and3. Commands

Figure.6: Shortest path planning scenario in static environment

Path planning algorithm in Figure.6 a dynamic environment with known experimental devices. Settingthe start position is supply as a source to a central unit. Hurdles table, namely the size 5 x 3 areinitialized to 0 or 1 according to the obstacles. In addition, to initializing destination point.

Figure.7: Shortest path planning scenario in dynamic environment

Experimental setup in Figure.7 of path planning algorithm in dynamic environments, also in the thirdobstacle placed on a grid. To the target process, the robot detected the third obstacle, then it will belocalized, and unobstructed location. This communication is transmitted to the central unit, runsprogramming algorithm of the shortest path using the updated information, which will enable thedestination smallest path traversal for mobile robots.

1. CONCLUSIONS

Path planning in a familiar environment is constructed and executed. The robot can travel to the targetby the smallest path. It was found to have deviated from the actual and desired paths. This is because ofthe wheel slip position measuring system fault, battery charge fluctuation differences and friction inbetween the wheel and the path, use of position while avoiding and mapping technology as theextended Kalman filter, from the path of deviation, particulate filters, can predict the probability andstatistics methods, it can be amended accordingly.

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