Research On Cooperative Control Algorithm Of Non-interactive Multi-agent Swarm In Unknown Environment

Unmanned swarm systems can perform tasks efficiently,adaptively,and robustly in various military and civilian domains,such as coverage bombing,pesticide spraying,and post-disaster rescue.However,traditional coverage solutions rely on large-scale communication methods,such as radio communication and satellite positioning,that do not consider the dynamic constraints of unmanned vehicles.These methods limit the application of unmanned swarms in scenarios with electromagnetic interference or poor wireless signal quality.Members of the vehicle swarm can only use non-communication interaction methods when communication fails,such as cameras and radars,to obtain environmental information.Thus,the environment of communication failure challenges the sweep coverage control of unmanned swarms.To solve this problem under the following realistic constraints,such as limited perception distance of swarm individuals,mutual anonymity among members,unknown boundaries of coverage areas,unknown distribution of obstacles,and restricted movement of members.The main contributions of this thesis are summarized as follows:(1)This thesis addresses the problem of cooperative coverage control for a multivehicle system under the constraints of limited sensing range of unmanned vehicles,mutual anonymity among unmanned vehicles,and unknown coverage area boundaries.A control scheme based on the virtual artificial potential field method is designed to meet these challenges,and it consists of the active connection strategy and coordinates compression strategy.The active connection strategy helps unmanned vehicles search for two adjacent swarm members to form a flexible chain formation and achieve coverage of all target areas.The coordinate compression strategy realizes the synchronous sweep control of the vehicle swarm in the sweep direction.Compared with existing results,the simulation results show that the proposed control scheme can adapt to extremely wide or narrow terrain environments,effectively improve the area coverage and reduce the repetition rate of the coverage area,and reduce the maximum angular acceleration of the heading during the movement of the unmanned vehicles,making the trajectory smoother.(2)Given the above three constraints,a nonholonomic dynamic constraint(affine unicycle model)is added to limit the motion performance of the unmanned vehicle,and the unknown position and size of obstacles in the target area with unknown boundary changes are introduced.The multi-constrained cooperative coverage control problem for the swarm system is studied,and an affine swarm control law based on the CBF-CLF-QP obstacle avoidance method is proposed.Two controllers for the control law are designed depending on the presence of obstacles in the environment.For the target segment with obstacles,a quadratic program(QP)is designed to control the safe obstacle avoidance of the nonholonomic dynamic model.The barrier Lyapunov function(CBF)in the planner is used to control the distance between the unmanned vehicle and the obstacle,and the control Lyapunov function(CLF)is used to control the obstacle avoidance direction of the unmanned vehicle.For the target segment without obstacles,a swarm control strategy based on the finite artificial potential field method is designed to control the speed reduction of the unmanned vehicle before obstacle avoidance while completing the area coverage task.Simulation results show that the control law is more suitable for obstacle avoidance control of affine swarms and performs well in different boundary and obstacle distribution scenarios.(3)Building on previous research,this study further limits the movement ability of unmanned vehicles by adding a single-track dynamic constraint(non-affine bicycle model).It investigates the cooperative coverage control problem for the swarm system under non-communication interaction.A non-affine swarm control law based on the Improved Artificial Potential Field(IAPF)is proposed.The strategy consists of an obstacle repulsion field and a gravitational weakening strategy.The obstacle repulsion field is generated by known information such as the position and speed of the unmanned vehicle itself.It is used to control the deceleration and obstacle avoidance of the unmanned vehicle and the recovery of the synchronous sweeping ability after obstacle avoidance.The gravitational weakening strategy helps the unmanned vehicle to achieve the control effect of avoiding obstacles first and then following by weakening the gravitational field when the unmanned vehicle avoids obstacles.The simulation experiments show that the control law can not only control the single-track dynamic model swarm,but also the non-holonomic dynamic model swarm,and realize the cooperative coverage control for obstacle avoidance in unknown environments,with better control effects for nonaffine swarms.