Research On Intelligent Algorithm Of Path Planning For Mobile Robot

The advent of the fourth scientific and technological revolution has made the application of mobile robots more and more popular in daily life,and gradually penetrated into all walks of life that people are engaged in.As the key content of the mobile robot to complete the job,it is particularly important to ensure the efficiency of path planning.That is,the mobile robot can autonomously avoid various threats and obstacles that hinder its normal movement during operation,and reduce the stagnation rate of movement in path planning.Therefore,high-performance path planning is a problem that needs to be implemented urgently.In order to deal with the above-mentioned problems caused by the path planning of the mobile robot,algorithm optimization can be considered to achieve the unity of autonomous obstacle avoidance and the shortest path during the movement of the mobile robot.This article focused on the problems of the A-star algorithm in the path planning algorithm of mobile robots,such as long planning path length and particle swarm optimization algorithm easily falling into local optima in the early stage.Based on different environmental conditions,the algorithms were improved and simulated.The simulation results showed that after the improved algorithm,a path with a shorter length The algorithm improvement path was carried out with a shorter path,which proved the effectiveness of the algorithm.The full text mainly carried out the following work contents:(1)Described the environment modeling of a mobile robot taking into account the model of obstacles when planning its path and illustrating the threat model for its mission execution.This paper summarized the classification of mobile robot path planning algorithms,expounded the heuristic algorithm,and includes five types of classic algorithms:ant colony algorithm,expressed expanding tree algorithm,artificial potential field algorithm,A-star algorithm and particle swarm algorithm and through the corresponding image realization of the algorithm,expressed the principle of the algorithm Advantages and disadvantages.In the face of different complex environments,through the comparison of various common algorithm experiments and simulations,the A-star algorithm and the particle swarm algorithm were selected as the research and improvement objects of the algorithm in this paper.(2)Aiming at the small-scale complex environment,used MATLAB to carry out lowdimensional grid modeling processing for the environment with obstacles.In terms of global planning,in order to improve the problem of low search efficiency and abundant path nodes of the classic intelligent A-star algorithm,a kernel-containing or de-kernel model was obtained through full convolution interpolation;in terms of local planning,multiple naturalization algorithms were used to meet the needs of mobile robots In the case of corner constraints,the redundant nodes were discarded,the pseudo code of the improved algorithm was summarized and the experimental simulation was carried out.The simulation results showed that after the improvement,the path length was shortened and the number of nodes was relatively reduced,which proved the effectiveness of the improved algorithm.(3)For the wide-area complex environment,used MATLAB to geometrically model the environment with obstacles,analyzed the principle of the brainstorming algorithm and improved the key steps of the algorithm,and obtained the feasible scheme of the brainstorming algorithm combined with the particle swarm algorithm and carried out mathematical induction and equation representation.In order to deal with the problem of falling into local optimum and low convergence accuracy in the path planning of the classic intelligent particle swarm optimization algorithm at low iteration times.Learned from the thinking mechanism of the human brain,made full use of the effective information between individuals and groups,improved the population richness of the particle swarm optimization algorithm,jumped out of the local optimum,and realized the global shortest path.Besides,through the local path improvement based on the principle of expansion,the safety and reliability of the algorithm planning path was improved,and the mobile robot could achieve the purpose of safe obstacle avoidance in the process.The simulation results of MATLAB experiments showed that the improved path length was efficient and stable,which proved the feasibility of the improved algorithm.