This paper addresses the cooperative object transportation by a multi robot distributed system, which is a difficult problem due to path planning and robot cooperation challenges. In this problem, a number of robots should transport an object to a goal point safely whil More
This paper addresses the cooperative object transportation by a multi robot distributed system, which is a difficult problem due to path planning and robot cooperation challenges. In this problem, a number of robots should transport an object to a goal point safely while avoiding obstacles and utilizing a proper coordination and cooperation mechanism. The proposed method has a two-layer structure which benefits from both centralized and decentralized architectures. The global level takes advantage of full knowledge of environment to plan an optimal path using the new Optimally-Connect Random Tree (ORT) method, and the local level performs some local processes to reduce the system’s overall processing load and cost and increase its robustness. The required coordination between the robots is realized via radio communication, and for local path planning of the robots a combination of potential fields and TangentBug algorithms has been used. The proposed method has been implemented on multiple KUKA youBot mobile manipulators in the Webots simulation software, and its performance has been evaluated through various experimentations and the results of implementing and comparing the ORT and Rapidly-exploring Random Trees (RRT) showed the advantage of the proposed method.
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