Research On Key Problems Of Multi-Agent Motion Formation Control

With the development of society,robots have been widely used in all walks of life,playing an increasingly important role in medical,living,military,exploration,and fire protection.Due to the limitations of individual robots' own resources and capabilities,it It has become increasingly unable to meet the needs of today's complex work.In order to improve the efficiency of robots,a robotic formation system composed of multiple robots has emerged.The robot formation system cooperates with multiple robots to complete complex work tasks.This paper studies the path planning of single robot and the cooperative control of robot formation.After implementing a single robot to complete the obstacle avoidance task,it returns to formation and keeps it.Formation formation.The entire study includes the following:1: The path planning problem of a single robot is studied.The artificial potential field method is used to plan the walking path of the robot.When the artificial potential field method is used for path planning,the defects of the traditional artificial potential field method are found.Up to the local minimum value problem,and improved on these two problems,solved the problem of target unreachability by improving the traditional repulsion,and introduced the virtual traction point to solve the local minimum value problem,through MATLAB simulation experiment.The effectiveness of the improvement was verified.2: Combine the improved artificial potential field with ant colony algorithm and particle swarm algorithm,and use the improved artificial potential field and traditional ant colony algorithm to construct a new heuristic function to search for the collision-free optimal in complex environment faster.Path,and the convergence rate of the potential field ant colony algorithm is faster than the convergence speed of the basic ant colony algorithm.3: Applying the improved artificial potential field algorithm to the improved particle swarm optimization algorithm,by combining the gravity magnitude with the fitnessfunction in the particle swarm algorithm path planning,a new fitness function is constructed,which is used on the original basis.Gravitational constrained particle swarms,and a new variable step size method is designed.The gravitational force is combined with the inertia factor in the particle swarm optimization algorithm.In the early stage,the global search weight of the particle swarm optimization algorithm is significant,and the gravitational force is reduced later.The local search ability of the group algorithm is increased to solve the local optimal problem of the particle swarm optimization algorithm.The experimental results show that the potential field particle swarm optimization algorithm can better complete the obstacle avoidance task,improve the convergence speed,and search for a shorter path.4: Using the artificial potential field to design the method of robot formation cooperative control,using the formation control strategy of pilot following,analyze the force of the pilot in the formation,the whole formation has four robots,and the follower robot is artificial.The field is also affected by the potential of the pilot,setting the desired distance and desired angle between the follower robot and the pilot robot,and designing to follow the mutual repulsive force between the robots to prevent mutual interaction between the robots.Collision,simulation experiments on the entire robot formation,formation in a rhombic shape movement,follow the robot to change the angle and relative distance to avoid obstacles when encountering obstacles,return to the desired angle after completing the obstacle avoidance,follow the pilot robot to arrive The target point,the experiment proves that the design method is effective.