Research On Cooperation Methods Of Multiple Mobile Robots

The development of the artificial intelligtnce science and the communication technique lays the firm foundation for further research and extensive application of multiple mobie robots system. As robots are charged with increasingly difficult tasks, the application domain of multiple mobile robots system expands. Many tasks can be better achieved by a team of robots than by a single robot. Multiple mobie robots system not only can effectively utilize the relative localization technique and information sharing mechanism to advance the system efficiency and the quality of accomplishing missions, but also can accomplish the spatial parallelism tasks which can not be realized by a single robot.However, if there is not a good cooperation mechanism among multiple robots, the whole system performance will decrease because of repeat work and mutual interference. The whole system will have safety hidden danger and even break down and can not accomplish the scheduled mission. So the research of multiple mobile robots cooperation method is the key problem of multiple mobile robots system research. The multiple mobile robots cooperation method based on psychology concept and the multiple mobile robots cooperation method based on biological inspired strategies are emphatically analyzed. At the same time, the RL method for loosely-coupled task and tightly-coupled task are respectively studied. Various single robot control structures are analyzed and compared. On the basis of this, a kind of single robot control structure named Target Type Behavior FSM was presented. Various group architectures are analyzed and compared. On the basis of this, a kind of distributed structure based on swarm intelligent method was presented. To fully describe the task conception and to lay a foundation for task based multi-robot cooperation methods research, a kind of task hierarchical structure called MTB was presented.Simulating the trophallaxis behavior of the social insects such as bees and ants, a kind of multi-robot systems mission consistency maintenance method was presented. It provided a robust and reliable swarm intelligence method for the effective communication of multiple mobile robots mission especially in limited communication range environment and security environment. The paper presented a multi-robot systems mission consistency maintenance method based on trophallaxis. A multiple mobile robots task allocation method based on a kind of Ant Colony Algorithm (ACA) named Repulsion Pheromone Ant Colony Algorithm (PR-ACA) was presented. Under the unknown unstructured environment, multi-robot cooperative foraging experiment was performed. Experiment results showed that the method presented in the paper could make multiple robots perform task allocation autonomously and effectively, and could decrease spatial confliction among robots, especially when the group was large.A multiple mobile robots cooperation behavior decision method based on psychological state parameters was presented. Robots gave birth to psychological state parameters based on the estimations of environment, teammates and themselves. The mapping relationship between psychological state parameters and cooperation tendency threshold values were set up with neural network. Robots could make decision on the basis of these threshold values on cooperation occasions. Experiment results showed that the multi-robot cooperation method could ensure both the rationality and the speediness of robots'decision-making. Few had been thought about the application occasion problem of the auction method for the cooperative multi-robot foraging. To solve the problem, anxiety conception was introduced. A new algorithm named Anxiety and Auction Cooperation Method (AACM), combined anxiety concepton with auction method, was presented. Experiment results showed that, compared with the only auction method, the method pesented in the paper could improve the efficiency of performing multiple mobile robots cooperation foraging.The paper studied multiple mobile robots formation and navigations according to the respective characteristics of tightly-coupled tasks. The high level used station-behavior pair reinforcement learning to learn the circumambulating direction according to the obstacles. A Role-Cross-Subsumption control structure was presented to be the middle layer of the three layers control structure. The low level used action-station pair reinforcement learning to learn the regulations for preventing collisions. Multiple mobile robots formation and navigations stimulation experiments validated the effectiveness and high efficiency of the method. As the further research of the single robot control structure for multiple mobile robots cooperation in the paper, a kind of Target Type Behavior RL with share section was presented. The paper studied the method to obtain Target Type Behavior FSM with RL. Stimulation experiments validated the feasibility of the method.Finally, a hardware experiment system for validating multiple mobile robots cooperation methods was built. The experimental results validated the system efficiency of multiple mobile robots cooperation methods presented in the paper.