Iterative Transportation by Cooperative Mobile Robots in Unknown Environment

Abstract

This paper focuses on a planning method for an iterative transportation task by cooperative mobile robots in an unknown environment. This task requires the acquisition of environmental information, the generation of appropriate robot paths based on the acquired information, and the formation of the group of robots. In order to realize an efficient transportation, a motion planning architecture consisting of ‘environmental exploration phase,’ ‘path-generation phase’ and ‘strategy-making phase’ is proposed. In the first phase, robots explore the environment using a learned visibility graph while transporting. Next, transportation paths consisting of 1-lane paths and 2-lane paths are generated using two kinds of C-spaces. In the final phase, every robot learns a behavior strategy by reinforcement learning and efficient formation of transportation is acquired. Simulation results indicate the effectiveness of proposed architecture.