Distributed Cooperative Motion Planning For Autonomous Multi-joint Robot System

Autonomous multi-joint robot system, which belongs to the Multi-Agent Robot System (MARS), has advantages over traditional robots in load bearing, accurate operating, flexibility, and adaptability in uncertain environment. The robots in cooperative system are spatial, timing and functional distributed. As a result of resources sharing, say, information and knowledge, the system overcomes the lack in capability of single robot, broadens the application fields of robots. Redundancy in robot resource improves reliability, stability, and the possibility to accomplish tasks.Motion planning is an important issue in robot decision making. In multi-robot cooperation, the demanding for cooperative motion planning is much higher. Centralized planning and decoupled planning have difficulties in space-time integrated optimization, velocity constraints handling, cooperative task diversity, and system reliability. They can not take full advantage of the distributed cooperative robot system.A distributed cooperative motion planning (DCMP) for autonomous multi-joint robot system is proposed in this paper. The motion planning subsystems of individuals in the system are independent and equal. They conduct collision-free motion planning according to limited decision information of other robots, which is acquired by asynchronous communication, therefore fulfill given cooperative tasks. The planning method has the ability to realize space-time integrated optimization, and to work under certain unreliable communication. It is valuable in many new application fields, such as outer-space exploration, interstellar exploration, autonomous cooperative fieldwork and deep-water cooperationIn this dissertation, the main research is focused on the following works:1) The problem of cooperative motion planning for autonomous multi-joint robot system is described mathematically. Optimization goal, evaluation criteria of path, constraints that influence the motion of robots are singled out in detail. By analyzing the consistency and separability of time optimization and space optimization in cooperative motion planning, the importance of the space-time integrated optimization criteria is emphasized. The performance requirements of cooperative motion planning method are specified.2) A new distributed cooperative motion planning (DCMP) method is proposed for cooperation of multiple autonomous multi-joint robots. The space-time integrated optimization is achieved on the same planning hierarchy. Objective to distribute problem-solving of the cooperative motion planning, the system is decomposed, by mathematical derivation strictly, into subsystems of each robot that can be calculated iteratively and independently in an asynchronous way. The performance of DCMP method, especially the resistance capability to the communication interference, is analyzed. cooperative motion planning method are specified. The DCMP method try to improve the collaboration architecture and coordination mechanism of cooperative motion planning.3) Based on mathematical strictness, cooperative co-evolutionary method is exploited in the DCMP method for optimal solution finding in sub-planning of each robot. Benefit from its parallel computation, heuristic search and constraints handling ability, the DCMP method realizes space-time integrated optimization naturally, and solves the problem of dynamic constraints, such as velocity constraint, which is very difficult for most cooperative planning methods.4) According to the characteristics of cooperative motion planning of autonomous multi-joint robot system, key points in the DCMP implementation, such as real-valued problem-specific chromosome representation, problem-specific evolutionary operators design, constraints handling, fitness evaluation method and estimation updating, are discussed in detail. The planner is a general framework and can be used for different kinds of tasks, such as simultaneous start-asynchronous stop, simultaneous start-simultaneous stop, and terminal distance keeping. The effectiveness of the DCMP method is proved by simulation results.5) A motion planning based on the DCMP method is applied to a pilot multiple autonomous multi-joint robot system in the Multi-robot Cooperative Control Lab, of which is the Institute of Intelligent Systems & Decision Making of Zhejiang University. Based on the principles of the DCMP method and its cooperative co-evolutionary implementation, some practical techniques are developed to enhance its practical feasibility and value. Motivated by the reducing dimension concept and point selection technology of the roadmap method, the capability of DCMP method in exploring in very high dimensional composite configuration space is enhanced. The DCMP is improved to satisfy the requirements of high-level cooperative mission planning and to provide more choice. The effectiveness, as well as its ability to work with the cooperative mission planning and under unreliable communication, are validated on a number of experiments.