Chain-based Reconfigurable Robots: SuperBot and it’s applications

Ilknur Kaynar-KabulFall 2006

Overview SuperBot

A Deployable, Multi-Functional, and Modular Self-Reconfigurable Robotic System Distributed Control of the Center of Mass of a Modular

RobotMark Moll, Peter Will, Maks Krivokon, and Wei-Min Shen. In Proc. 2006 IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems, Beijing, China, October 2006.

Multimode Locomotion via SuperBot RobotsWei-Min Shen, Maks Krivokon, Harris Chiu, Jacob Everist, Michael Rubenstein, and Jagadesh Venkatesh In Proc. 2006 IEEE Intl. Conf. on Robotics and Automation, pp. 2552–2557, Orlando, FL, 2006.

Self-reconfigurable robots Lattice-based reconfigurable robots Chain-based reconfigurable robots

Polybot Conro SuperBot

Hybrid systems M-TRAN module Tetrobot

SuperBot SuperBot is a modular robot that consists of many reconfigurable

modules that can demonstrate multifunction and reconfiguration [Salemi 2006]

SuperBot is being designed for NASA space exploration programs

SuperBot Each module has

3 revolute joints 6 genderless

connectors 2 Atmega 128 CPUs

Some modules have wireless capabilities, video cameras

SuperBot More flexible, mobile and efficient compared to

the existing robots A module can perform different gaits (e.g.,

caterpillar, sidewinder, push-and-pull, etc.) and turn and flip without any external help

Modules can be packaged in a way that is appropriate for transportation

Distributed Control of the Center of Mass of a Modular Robot

Mark Moll, Peter Will, Maks Krivokon, and Wei-Min Shen. In Proc. 2006 IEEE/RSJ Intl. Conf. on Intelligent Robots and

Systems, Beijing, China, October 2006.

Motivation Much of work on modular and self-

reconfigurable robots focuses on Specific design of robots Reconfiguration planning Gait development

Few work on locomotion of modular robots in the presence of uncertainty - uneven and unknown terrain.

Idea of the paper A robot can prevent itself from falling over by

controlling the center of mass (COM) Uses a gait only as a guideline for locomotion Uses contact information & mass information to

ensure a stable pose at all times.

Overview of the approach (1) Presents a distributed and decentralized

algorithm that computes the mass properties of the robot at each step

Modules compute the total mass, the center of mass (COM) and the inertia tensor

This information enables a module to compute joint displacements that will move the COM towards a desired position

Overview of the approach (2) A gait is specifies where the COM needs to

go and which leg needs to be moved, rather than specifying joint angle for every module.

Advantage: Simplify the specification of a gait Allow a modular robot to move over uneven

terrain

Main issues Computing the mass properties Stabilizing Behavior

Computing the mass properties Assumption: the modules are connected to form a

tree-like structure, i.e. there are no loops Each module computes the mass properties of the

whole system Based on its own state and on information it receives from

its neighbors It receives an estimate of the mass properties from a given

connector of just the modules that are connected (directly or indirectly) to that connector

Computing the mass properties A module sends new estimate to its

neighbors when the modules move If the modules do not move, the modules will

eventually all converge to the true mass properties and stop sending updates to each other

Algorithm for Mass Computation

Algorithm for Mass Computation

After d iterations of the main loop, each module will have computed the correct COM, assuming the modules do not moved: largest tree distance between 2 modules

Stabilizing Behavior To stabilize an arrangement of modules

1. Change the joint angles in the modules OR2. Rearrange the modules OR3. Combination of both

Option 2 can be slower than option 1

Stable configuration for a simple module General idea: A configuration is stable if the contact

forces can balance the gravitational force

Simple case: One point of contact and no friction Stable if the center of mass lies on the support line

Support line: the vertical line through the point of contact If it is not stable, then each module should adjust its joint

angles

Simple case: Revolute joint Consider one revolute joint: One side of the

joint is connected to the contact point and the other side attached to it move along an arc of a circle

Simple case: Revolute joint

p1: COM of the part of the system that remains fixedp2: COM of the part of the system that is going to be rotatedq: the position of the jointw = p2 − qRθ is a 3-by-3 rotation matrix representing a rotation of θ radians about u.

Stabilizing all revolute joints Finding optimal displacements for all joints

simultaneously is very difficult Solution: Use an approximate solution which

tends to converge to a desired configuration very quickly. Each joint computes its own optimal displacement

independently of each other

Solving oscillation problem This solution computes a desired direction to

move in for all modules Problem: Modules can oscillate around the

support line due to the momentum Solution: 2 heuristics

Based on the distance between the estimated COM and the support line

Based on momentum

Heuristic 1: Distance based Reduce the gains as the COM gets closer to

the support line, so that the robot does not overshoot the goal position.

Proportional gain is adjusted as follows:

c0 and c1 are constants dsupport is the distance to the support lineKP0 is the nominal proportional gain

Heuristic 2: Momentum based An ensemble of modules should not gain too

much momentum For each joint, consider the mass and the

distance to the joint of the COM of the modules that will be moved by this joint

Proportional gain is adjusted as follows:

Simulation Results Random trees of modules are used as robots

20 modules divided into 4 branches of 5 modules Each module has 3 DOF, the whole tree has 60 DOF The root is always in vertical direction and fixed to the

ground

Simulation Results To evaluate the performance, distance

between the COM and the support line as function of time is used

Tested on 3 different control schemes: Default: The gains on all modules are identical

and constant Distance: The gains depend on the estimated

distance to the support line Momentum: The gains depend on the momentum

Performance for Robot (a)

Performance for Robot (b)

Performance for Robot (c)

Performance for Robot (d)

Conclusion Presents the feasibility of using distributed

control to move the COM of a modular robot to a desired position

Control methods with heuristics move the COM to a desired position No control method outperforms the others Momentum heuristic gives the best overall

behavior All methods exhibit the desired behavior most of

the time

Future work The performance can be improved if each

module computes the optimal joint angles for all three joints simultaneously

Inertia tensor can be used in balancing the behavior

External forces, such as gravity and friction, at the contact points can be taken into account

Multimode Locomotion via SuperBot Robots

Wei-Min Shen, Maks Krivokon, Harris Chiu, Jacob Everist, Michael Rubenstein, and Jagadesh Venkatesh

In Proc. 2006 IEEE Intl. Conf. on Robotics and Automation, pp. 2552–2557, Orlando, FL, 2006.

Overview Presents SuperBot for multiple locomotion

modes based on reconfigurable modules Shows the validity of the SuperBot for

the movements of forward, backward, turn, sidewinder, maneuver, and travel on batteries up to 500 meters on a flat terrain

Multimode locomotion Multimode locomotion : Ability to use different

moving modes in different environments. “climb” if it is to go up a slope “run” if it is to cover more distance with less

energy “balance” if the terrain is rugged and uneven “get up on feet” if it fell down by mistake

Multimode locomotion To support multimode locomotion, a robot

must have at least four capabilities.

1. it must be able to perform different locomotion mode.

2. it must be able to recover from unexpected locomotion failures.

3. it must be able to shift from one mode to another. 4. it must be able to choose the correct mode for the

correct environment.

This paper focuses

Multimode locomotion 2 competing and even conflicting criteria for

multimode locomotion: the robot must be general

To deal with many types of environments and difficulty tasks

the robot must be special To achieve goals with greater efficiency.

Reconfigurable robots can achieve these goals

Locomotion modes Each mode consists of

characteristics for the environment type speed turning-ability energy-efficiency recoverability from failures

The 6M-loop mode 6 M-modules are in a ring configuration of

hexagon shape Advantage:

Energy efficient and allows high speeds Disadvantage:

Tolerance to environment obstacles is limited by the size of the wheel

The robot cannot stand up once it falls down

The 6M-loop mode Shapes alter between a regular hexagon and a

deformed hexagon that tends to fall forward. Starting from the regular hexagon, the movement is

controlled by the deformation of the shape to change the centre of gravity of the traveller.

2 commands governing the shape transformation: One is to retain the regular hexagon shape. One is to let the rolling traveller to “squeeze” itself to a

deformed hexagon. Commands are selected using gravity sensors

The 6M-loop mode

The 10C-Loop Mode Uses all CONRO-like modules

each module can control its pitch and yaw movement Flexible and can run, turn, and recover from falling

down Can deal with environments where obstacles do not

exceed in size the height of the robot configuration

The 10C-Loop Mode Achieves the rolling track locomotion

At a fixed time interval (OR when all modules have bended forward to the desired angle) each module begins to bend forward again to reach the

angle that is equal to the current angle of the module that is in front of it.

When this process repeats, the rolling track will move forward in a straight path.

The 10C-Loop ModeRecovery from fall down

The 9M-walker mode H-Walker is a 4-legged walker using 2 DOF

on each module 3 possible local topologies:

Torso, upper leg, and lower leg

The 9M-walker mode Distributed locomotion control was achieved

using the digital hormone method [Shen 2002]

4 hormones are used to control each leg Torso sends the hormone messages to the

legs and synchronizes their coordinated actions

The 9M-walker mode H-walker mode has symmetric design

Prevents it from falling into any unrecoverable position

Its topology is in the shape of an 'H' Can walk forwards and backwards using the

same strategy

The 9M-walker mode Fall down: It is easy to achieve the relaxed

position in which the legs are straightened out to the sides in a double-caterpillar shape.

It stands up using the following steps

The 6M4C-training-wheel mode Modified version of 6M Added 4 extra legs as “training wheels” to

6M-loop It can run fast, and can turn and recover from

falling

The 6M4C-training-wheel modeRecovering from falling Straightens all the “leg” modules and

collapses the hexagon to a flat loop The hexagon plane can then be made

vertical and the flat loop will change back to its hexagon shape and continue to roll

The 2M4C-loop mode It uses 6 modules for the loop : MCCMCC It alternates the types of module to enable

the loop to turn and recover from falling

The 2M4C-loop modeRecovery from fall down1. The loop straightens itself by bending the 2

Mmodules into 180 degrees 2. Resets the shape of all 4 C-modules 3. The C-modules then change their yaw servos so

that the robot is rising up yet unbalanced. 4. The unusual movements of the C-modules will

cause the robot to fall sideways 5. The loop will then straighten up again 6. Goes back to its original hexagon shape

The 2M4C-loop modeRecovery from fall down

The 8M-climbing mode 8 M-shape Superbot modules forming a

rolling track that is only 1.5-module in height The advantage of this configuration is to

make use its low height property to stabilize it on the slope

The mode climbs up the slope slowly by moving module by module

The 8M-climbing mode

Conclusion Presents the concept of multimode

locomotion for the Superbot robot and a list of locomotion modes

The effectiveness of these modes are demonstrated by the Superbot modules and configurations in simulation

Future work: the process of how to reconfigure the robot from one mode to another through self-reconfiguration

References [Shen 2002] W.-M. Shen, B. Salemi, and P. Will, Hormone-Inspired

Adaptive Communication and Distributed Control for CONRO Self-Reconfigurable Robots, IEEE Transactions on Robotics and Automation, 18(5), October, 2002.

[Salemi 2006] Behnam Salami, Mark Moll, and Wei-Min Shen. SUPERBOT: A Deployable, Multi-Functional, and Modular Self-Reconfigurable Robotic System. In Proc. 2006 IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems, Beijing, China, October 2006.

[Moll 2006] Mark Moll, Peter Will, Maks Krivokon, and Wei-Min Shen, Distributed Control of the Center of Mass of a Modular Robot,In Proc. 2006 IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems, Beijing, China, October 2006.

[Shen 2006] Wei-Min Shen, Maks Krivokon, Harris Chiu, Jacob Everist, Michael Rubenstein, and Jagadesh Venkatesh, Multimode Locomotion via SuperBot Robots, In Proc. 2006 IEEE Intl. Conf. on Robotics and Automation, pp. 2552–2557, Orlando, FL, 2006.

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