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Cooperative Transportation by Multiple Robots with Machine Learning

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2 Author(s)
Ying Wang ; Mechanical Engineer Department, The University of British Columbia, Vancouver, BC, V6T 1N3, Canada (phone: 604-822-4850; e-mail: ywang@mech.ubc.ca) ; C. W. de Silva

This paper investigates the development of a physical multi-robot system, where a group of intelligent robots work cooperatively to transport an object to a goal location and orientation in an unknown dynamic environment. Multi-agent technology and machine learning are integrated into the same physical platform to provide innovative capabilities for carrying out the task. First, a new multi-agent architecture is developed. Second, two methods that facilitate optimized machine learning, Reinforcement learning (RL) and genetic algorithms (GA), are integrated into the decision-making agent in the architecture. An arbitrator is incorporated into a probabilistic switching scheme for selecting the optimal strategy in a given state of the task. Finally, the robot force/motion control and local modeling are integrated into the architecture to implement a physical multi-robot system. Simulation and experimental studies are carried out to demonstrate the feasibility of the system.

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2006 IEEE International Conference on Evolutionary Computation

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