Cooperative Coevolution For Dynamic Multi-target Tracking

Dynamic multi-target tracking covers the core applications in robotics such as searching,following and obstacle avoidance,which has broad application prospects.The dynamic multi-target tracking problem belongs to the pursuit-evasion problem,which is a classic problem.It has attracted the attention of many research fields,such as game theory,path planning,reinforcement learning,and multi-robot systems.There are many related studies,but most of them tend to treat the pursuit-evasion problem as an application to verify the validity of their theory,which is not suitable for instantiation into real robots.So the purpose of this thesis is to design a dynamic multi-target tracking system that is more suitable for real application scenarios,and make an experimental simulation on the physical simulator Gazebo.There are two groups of robots in this thesis.A group of robots make random movements,called evaders;another group of robots can communicate with each other,and their task is to find the evaders in the map,and evenly surround and follow around them.They do not collide with obstacles or other robots during this period,called pursuers.This thesis models the dynamic multi-target tracking problem as a dynamic optimization problem.The decision vector is composed of the target point of pursuers,and there are eight optimization objectives: obstacle avoidance,collision avoidance,exploring unvisited areas,reducing angular velocity,approaching global sub-target point,keeping distance from the escape robot,evenly surrounding the escape robot and the camera facing the target robot.Using cooperative co-evolutionary brain storm optimization algorithm(CCBSO)to decompose the tracking problem with pursuers into subproblems,which is transforming the optimization of target points problem to problems of optimizing target point.Each subproblem is solved by a separate subpopulation,the evaluation of the fitness value of each individual in a subpopulation depends on the collaboration among subpopulations,and the final complete solution is composed of the results of the optimization of each subpopulation.This thesis builds a dynamic multi-target tracking system mainly includes two modules,a perception module and a navigation module.The perception module is mainly used to detect obstacles and evaders;the navigation module is mainly used to determine the robot's behavior and control the robot's motion,including global path planner,behavior planner,local path planner.The global path planner is usedto dynamically plan the global path of the pursuer to each captured evader;the behavior planner determines the current task of pursuers.The tasks are divided into searching,tracking,monitoring and following;the local path planner uses CCBSO to optimize the target point of the pursuer,and then use the target point to calculate its velocity control signal to control the robot's movement.Experiments show that,for the evader with a low speed,the pursuer can effectively capture and surround it.