Distributed Coadaptive Control And Coadaptive Behavior Analysis For Swarm Robots System

With the development of computing technology, sensor technology, communication technology, control theory, artificial intelligence, and some research of robot formed by a number of interdisciplinary have also entered a new stage. Thus swarm robotics is produced by some social insects in nature-inspired. Swarm robots system is a special class of multi-robot system, with the features of robustness, adaptability, and scalability.Research on swarm robotic system has important significance in theory and practice. In theory, with the further research of swarm robotics, it will help reveal the emergence of a fundamental mechanism for intelligent behavior. In practice, a mature swarm robots system can be in the ship manufacturing, product assembly, transportation systems, military equipment, aerospace and other areas of the completion of certain dangerous work independently and therefore have high potential applications.Coadaptivity for swarm robots system means the ability of the autonomous robot optimize their own control strategies constantly, and adjust behavior to meet the dynamic changes in the environment and the characteristics of the task, and ultimately the overall optimality through the local information with other robot and external environment in a complex dynamic environment.This dissertation research on some problems of the coadaptivity for swarm robots system, which are based on the tasks of foraging and flocking control. The work is supported by the National Natural Science Fund of China under Grant 60675057.Name of the project is"coadaptivity theory and methods research for swarm robots system in a complex dynamic environment".The major work of this dissertation studies on the following four aspects:1. The distributed control strategy is studied for flocking under swarm robots system kinematics model. The achievement and maintenance of flocking formation is implemented for swarm robots systems though the method combined the Vicsek model and improved artificial potential field in an environment without obstacles. In order to achieve flocking and avoiding the static obstacles in the environmentrun, the combination of update rule for Vicsek model and artificial coordinating field is adopted to design coorespoding control strategy, then the improved particle swarm optimization algorithm is presented to optimize the coorespoding parameters in order to achieve a stable flocking behavior. The data of simulation experiments show that the distributed control strategy can implement the flocking behavior for swarm robots system effectively in the absence of or with static obstacles environment.2. The distributed control strategy is designed for flocking under swarm robots system dynamics model based on local information exchange. Analysis of the stability of the distributed controller, and estimates the corresponding finish time of the flocking behavior. The improved particle swarm optimization algorithm is presented to optimize the coorespoding parameters in order to minimize the energy consumption during the process of the motion. In order to solve the problem how to design the controller cause the swarm robots system flocking in an dynamic environment. The distributed control strategy is designed based on local information exchange. In order to analyze the stability of the non-autonomous system, Barbalat lemma is introduced under the coorespoding assumption,such that the speed of all the individuals converge to the same curves. Simulation results show that for the above two cases, the designed distributed control strategy can achieve a stable flocking behavior for the swarm robots system effectively and rapidly.3. The distributed c ontrol strategy is designed for social swarm foraging in an consistent environment under swarm robots system dynamics model based on interal average kinetic energy, so that the swarm robots system can finish the foraging task efficiently under the non-flocking condition based on local information exchange, and prove that the value of intermal average kinetic energy will eventually converge to the prior expectation value in a damping environment. Simulation results show that the convergence lower value of internal average kinetic energy, make the swarm robots system as a whole cover a smaller area in the search space, by contrast the convergence higher value of internal average kinetic energy, make the swarm robots system as a whole cover a larger area in the search space, then the swarm robots system can find the extreme value of the environment function more efficiently, to finish the swarm social foraging task.4. The method of cooperative Q-learning is presented based on blackboard architecture, targeted at some shortcomings as follows: poor scalability of the point to point communication, too much communication traffic and too slow convergence speed of reinforcement learning. The learning process is executed at the blackboard architecture making use of the advantage of robots number and distributed sensing capability in the training scenario to explore the learning space and collect experiences. Communication is essential for swarm robots system which can be used to share experiences, parameters and control policies. Resent research proofed that proper communication can largely improve the performance of swarm robots system.It can achieve to independence of each robot reinforcement learning in experience sharing by learning automata and improved particle swarm optimization algorithm. Simulation results show that the model can improve the learning speed and reduce communication traffic.In summary, some problems of the coadaptivity for swarm robots system are studied in this dissertation. The main purpose of this work is to establish a complete theory of coadaptivity and its implementation for swarm robots system. And then, Simulation experiments are performed for the purpose of related verification and analysis.