A machine-learning approach to multi-robot coordination

0209 industrial biotechnology 02 engineering and technology
DOI: 10.1016/j.engappai.2007.05.006 Publication Date: 2007-07-13T11:45:21Z
ABSTRACT
This paper presents a machine-learning approach to the multi-robot coordination problem in an unknown dynamic environment. A multi-robot object transportation task is employed as the platform to assess and validate this approach. Specifically, a flexible two-layer multi-agent architecture is developed to implement multi-robot coordination. In this architecture, four software agents form a high-level coordination subsystem while two heterogeneous robots constitute the low-level control subsystem. Two types of machine learning-reinforcement learning (RL) and genetic algorithms (GAs)-are integrated to make decisions when the robots cooperatively transport an object to a goal location while avoiding obstacles. A probabilistic arbitrator is used to determine the winning output between the RL and GA algorithms. In particular, a modified RL algorithm called the sequential Q-learning algorithm is developed to deal with the issues of behavior conflict that arise in multi-robot cooperative transportation tasks. The learning-based high-level coordination subsystem sends commands to the low-level control subsystem, which is implemented with a hybrid force/position control scheme. Simulation and experimental results are presented to demonstrate the effectiveness and adaptivity of the developed approach.
SUPPLEMENTAL MATERIAL
Coming soon ....
REFERENCES (37)
CITATIONS (58)
EXTERNAL LINKS
PlumX Metrics
RECOMMENDATIONS
FAIR ASSESSMENT
Coming soon ....
JUPYTER LAB
Coming soon ....