Generation of Adapting Behavior by Multi-Robot using Parameter Searching on Subsumption Architecture

Abstract

In this research, we proposed multi-robot system that has robustness to the change of environments and has adaptability to the change of tasks and number of robots. To implement them, we use parameter learning on subsumption architecture.The subsumption architecture control robots' fundamental behaviors and robots move continuously in principle.The optimal value of the parameter and the number of robot that does the task in field are searched by learning. As the result,the robot's action on the task is improved (updated).Proposed system has functions to recognize the task and to generate the recordation memory.And then robots adapt to the change of tasks and the number of robots.In this paper, the function of proposed system is tested in simulation experiments.