Research On Dynamic Path Planning Method For Multi-robot Systems

Since the development of multi-robot systems in the late 80s of the 20th century, the research on multi-robot systems has made much progress as it outperforms single-robot system in many respects. The advantages of multi-robot systems mainly include broader applications field, better fault tolerance, lower cost to complete complex tasks, more efficient, more scalable, and easy to study and development. Therefore, the study of multi-robot systems has become a vigorous and prospective research direction in robotics. Path planning is one of the most basic problems in the area of multi-robot systems and it is the fundamental precondition to achieve the task. Because multi-robot systems in practical application mostly work under the complex and dynamic environment, the research on the dynamic path planning has an important academic and practical meaning. Supported by the National "211 Project" construction project "Intelligent Mobile Robot System", this dissertation mainly do some research on dynamic path planning method.Firstly, by combining velocity obstacle and behavior dynamics, a new dynamic path planning method is proposed for MRS when robots can communicate with each other. This method considers the velocity information of the moving obstacles and the other robots, and it also defines obstacle avoidance and collision avoidance region for the robot. In addition, it improves the attitude angle dynamics and speed dynamics of obstacle avoidance and collision avoidance behavior. These unique ideas can effectively avoid failing in dynamic path planning including obstacle avoidance ahead of time or too late. The simulation verifies the effectiveness and feasibility of the method.Secondly, the dynamic path planning method, which combines the velocity obstacle and behavior dynamics, is optimized. In the first place, particle swarm optimization algorithm is adopted to fuse three behavior patterns including move-to-goal behavior, obstacle avoidance behavior and collision avoidance behavior. Thus, the robot could obtain the weight of each basic behavior according to the real-time environmental information collected by sensors. We compare two behavior coordination methods of particle swarm optimization method and competitive dynamics method, the simulation results show the feasibility and superiority of this algorithm. Then an improved potential grid method is utilized to determine the sub-target position of robot, which is used to substitute for the ultimate goal position to establish the attitude angle dynamics of the move-to-goal behavior. This idea makes the path planning more reasonable in case of the robot's target point far away from the initial point and enlarges serviceable range of behavior dynamics path planning methods.Thirdly, the dynamic path planning problem of multi-robot systems in unknown dynamic environment is considered for the case that communication between robots is unavailable. Double-layer fuzzy controller is proposed to design danger fuzzy controller and speed fuzzy controller. By using double-layer fuzzy controller, the fuzzy relationship of input and output of each layer are clarified. In addition, the fuzzy rules are simplified. The speed information of moving obstacle is fully taken into account by the danger fuzzy controller. The possibility of collisions between robot and obstacles is represented by danger degree which is determined by collision time factor and the collision distance factor. Consequently, it is better to express the relationship of the robot and the environment. The algorithm is more suitable for dynamic environments. The effects of target azimuth, obstacles azimuth and collision danger degree are fully considered when designing the speed fuzzy controller. Fuzzy rules, designed based on the ideology of behavior, reflect the behavior of move to goal, avoiding obstacles and walking along the obstacle. This design principle makes the algorithm more suitable for the special circumstances with narrow barrier. Simulation results show that the method is feasible and effective.Finally, the problems of how to automatically extract and simplify fuzzy rules, and how to automatically compute the input and output parameters of membership functions based on artificial neural networks are considered. So an improved artificial fish swarm algorithm is designed and applied to structure optimization and parameter optimization for fuzzy neural network. Thereby the fuzzy rules can be automatically extracted and simplified, and the input and output parameters of membership functions can be automatically determined. Simulation result shows that when the multi-robot systems carry through dynamic path planning utilize double-layer fuzzy controller, the proposed method can reduce the computation complexity and enhance the real-time performance. Moreover; by increasing cross and mutation operation to artificial fish after the behavior evaluation in artificial fish swam algorithm, the speed of searching optimal value is faster and the possibility of searching result falling into local optimal value is reduced. In addition, the maximum step method is proposed to adapt to variable domain.In addition, experimental test uses the tracked robot which is equipped with panoramic vision sensors and ultrasonic sensors. The proposed dynamic path planning methods and their related technologies are verified by experiments including panoramic vision-based artificial landmark recognition experiment, the robot's triangle positioning experiment, the odometer-based positioning experiment, the behavioral dynamics path planning experiment and double-layer fuzzy controller experiment.