Research On Agent Path Planning And Formation Control

At present,with the development of technology,artificial intelligence and unmanned driving have become the focus of attention in the world today.Especially unmanned driving,a comprehensive system that integrates artificial intelligence,machine vision,planning and decision-making functions.This article mainly focuses on the path planning and formation control of the agent.First,the research background and significance of this subject are introduced,and the current research status of multi-agent path planning and formation control is analyzed.The advantages and disadvantages of classic planning algorithms and common improvement schemes are listed.Aiming at the formation of agents,the commonly used architecture and formation control principles are mainly introduced.Secondly,in view of the problems of ant colony algorithm in planning path,the early heuristic is weak,and the convergence speed is slow,etc.,it is improved by fusing the A*ant colony algorithm.Then,aiming at the problem of poor efficiency of the pheromone update mechanism in a complex environment,the method of strengthening the elite ants and weakening the pheromone weight of ordinary ants based on the principle of inequality,efficiently updates the pheromone,and strengthens the positive feedback effect.The experiment proves that the improved ant colony algorithm shows obvious superiority.Thirdly,for the multi-agent environment,first use the improved ant colony algorithm for global path planning,and then gradually use the artificial potential field algorithm for local path planning.In order to realize the dynamic obstacle avoidance of multi-agents,first,the optimal path is found through the ant colony algorithm and the inflection point is reselected to reduce the number of inflection points.Then the rearrangement inflection point is regarded as the sub-target point of the artificial potential field algorithm.Finally,the artificial potential field algorithm is used to gradually move to the sub-target point.Artificial potential scenes have high flexibility and adaptability to dynamic environments,but are easy to fall into the trap of local minimum.Therefore,a combination of ant colony potential fields is proposed to solve the local minimum problem.The experimental results prove the effectiveness of the proposed algorithm.Finally,a multi-agent formation model was designed based on the first-order consensus algorithm,and the artificial potential field algorithm was used to realize the agent's active obstacle avoidance and planned path.Then,according to the leader-follower formation control scheme,the multi-agent formation was successfully realized.Formation control.The experiments of formation transformation and formation of intelligent body facing static and dynamic obstacles are designed respectively.It proves that the formation of agents can not only change and maintain a stable formation,but also effectively deal with static or dynamic obstacles.It is verified that the agent system has strong robustness and adaptability.