Title :
An object transportation system with multiple robots and machine learning
Author :
Wang, Ying ; De Silva, Clarence W.
Author_Institution :
Dept. of Mech. Eng., British Columbia Univ., Vancouver, BC, Canada
Abstract :
This paper investigates the problem of object transportation, particularly pushing or moving an object to a goal location and orientation, using multiple robots. A multi-agent architecture is established to realize effective cooperation between multiple autonomous intelligent robots, in carrying out the task. Machine learning is incorporated into the architecture. In the developed approach, the world state of the task is established by fusing sensory information. Two machine learning and optimization methods, reinforcement learning (RL) and genetic algorithms (GA), are combined to learn a cooperation strategy and based on which, determine the optimal actions to reach the task goal. The outputs of RL and GA are evaluated by an arbitrator using a probabilistic method, which resolve conflicts and improve the overall performance. The feasibility of the scheme is illustrated through computer simulation.
Keywords :
genetic algorithms; intelligent robots; learning (artificial intelligence); materials handling; mobile robots; multi-agent systems; multi-robot systems; probability; sensor fusion; cooperation; genetic algorithms; machine learning; multi-agent architecture; multiple autonomous intelligent robots; multiple robots; object transportation system; optimization; probabilistic method; reinforcement learning; sensory information fusion; Context modeling; Distributed computing; Intelligent agent; Intelligent robots; Intelligent transportation systems; Machine learning; Motion control; Physics computing; Robot kinematics; Robot vision systems;
Conference_Titel :
American Control Conference, 2005. Proceedings of the 2005
Print_ISBN :
0-7803-9098-9
Electronic_ISBN :
0743-1619
DOI :
10.1109/ACC.2005.1470156