Research On Path Planning Algorithm Of Indoor Mobile Robot Based On ROS System

With the continuous progress of global science and technology,intelligent robots have been widely used in industry,agriculture and other fields.Among them,the most used scene is mobile robots,and path planning is an important research content of this type of machine.This article mainly studies from the perspective of global and local planning,conducts an in-depth analysis of the design principles and design theories of the path planning algorithm in the mobile robot navigation system,and conducts simulation experiments to verify the optimization and improvement of related algorithms.On this basis,for the complex indoor environment,a hybrid path planning algorithm using static global optimization and dynamic real-time local optimization is proposed,and the two improved algorithms are applied to the actual robot platform to achieve accurate obstacle avoidance and obstacle avoidance of indoor mobile robots.Fast pathfinding theory verification.The main contents of this paper are as follows:(1)Aiming at the problems of inefficient execution of traditional A* algorithm in path planning tasks and too many turning points,an improved A* algorithm based on optimized key point selection and smooth path is proposed.Use an improved jumping point search algorithm to improve the traditional A* algorithm,speed up the node search speed by selecting expanded sub-nodes;at the same time,introduce the RRT*pruning idea to eliminate non-essential nodes in the secondary path planning;and based on the Bezier curve Perform smoothness processing on the generated path.Simulation and comparison experiments are performed on grid maps of different sizes to verify that the improved A* algorithm reduces the number of extended nodes and shortens the pathfinding time,and the performance of the path planning algorithm is significantly improved.(2)For the traditional artificial potential field algorithm used in the real-time pathfinding process,there are two problems that the target point is unreachable and the local minimum state exists.The repulsion force is improved by introducing a function related to the distance between the robot and the target point by using the repulsion function,which is to improve the gravitational force of the target point on the robot,and solve the problem of unreachable target point.At the same time,the artificial potential field algorithm combined with the simulated annealing algorithm is used.Based on the adaptive step size,new solutions are generated by adding disturbances to escape the local minimum state.Simulation comparison experiments show that the improved artificial potential field algorithm can effectively solve the above-mentioned problems and has better flexibility and reliability.(3)Aiming at the shortcomings of the above-mentioned algorithms in path planning in complex environments,this paper proposes a hybrid planning algorithm of static global optimization and dynamic local real-time optimization.The combination of offline global planning and real-time local planning is realized to satisfy the robot’s operation in complex indoor environments.Simulation experiments verify the feasibility and superiority of the hybrid algorithm.Finally,the improved A*algorithm and the improved artificial potential field algorithm are configured in the actual mobile robot experimental platform,and the path planning comparison experiment of the improved A* algorithm and the mixed path planning experiment in the dynamic and static environment are adopted.The results fully prove that the hybrid path optimization method based on the two improved algorithms meets the basic requirements of real-time dynamic and accurates obstacle avoidance and fast pathfinding of indoor mobile robots.