| Volume 27 Issue 3-4 - Publication Date: 1 March 2008 |
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| Million Module March: Scalable Locomotion for Large Self-Reconfiguring Robots |
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| Robert Fitch ARC Centre of Excellence for Autonomous Systems,
Australian Centre for Field Robotics (ACFR),
The University of Sydney, NSW, Australia and Zack Butler Department of Computer Science
Rochester Institute of Technology
Rochester, NY USA |
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| Self-reconfiguring robots have the potential to explore highly variable
terrain, operating as parallel groups or combining to surmount
large obstacles. If the modules are at a smaller scale, they may also
be able to physically render arbitrary shapes in an interactive way.
In order to realize these capabilities, groups with large numbers of
modules must be used, and algorithms to control such large groups
must be extremely scalable in order to be executed on simple modules.
In this work, we present an algorithm for locomotion of latticebased
self-reconfiguring robots that uses constant memory per module
with execution times that are sublinear in the number of modules.
The algorithm is inspired by reinforcement learning and uses dynamic
programming to plan module paths in parallel. We have also developed
a novel localized cooperation scheme that allows the modules to
move both without disconnecting the system and with small amounts
of communication. The combined algorithm is able to direct locomotion
over arbitrary obstacles, and due to continuous replanning the
goal can be moved at any time to ‘joystick’ the robot over the environment.
The formulation of the goal used in the planning also encourages
dynamic stability. We have developed both centralized and
decentralized implementations in simulation, as well as an implementation
for the Superbot system, and present empirical results showing
the sublinear nature of our technique. |
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| Multimedia Key |
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 = Data |
= Code |
= Image |
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Extension |
Type |
Description |
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1 |
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Example
1: SR robot simulation
over random terrain. (4.5 MB) mp4 |
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2 |
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Example
2: SR robot simulation
of 125 000 modules
over flat terrain. (6.8 MB) mp4 |
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3 |
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Example
3: SR robot simulation over a concave obstacle. (2.7 MB) mp4 |
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4 |
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Example
4: SR robot simulation through comb-like obstacle. (3.8 MB) mp4 |
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5 |
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Example
5: Simulation of planar Superbot locomotion. (3.7 MB) mp4 |
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