Path Planning of Mobile Robot Based on Improved Particle Swarm

Abstract

Path planning is one of the key technologies of robot navigation and control, in path planning, there are some problems in the application of ant colony algorithm, such as slow convergence speed, poor optimization results and incomplete search. In order to improve the mobile robot's ability to search the optimal path to the target point in the global static environment. In this paper, a double improved fusion algorithm of particle swarm optimization and ant colony algorithm is proposed to solve the path planning problem. Firstly, the occupied grid map is constructed based on visual slam technology of depth camera in static environment; Secondly, the improved particle swarm optimization -ant colony algorithm is used for path planning in the grid map: The sub optimal solution is obtained by using the advantages of global search ability and search speed of improved particle swarm optimization, which is transformed into the increment of initial pheromone distribution in the improved ant colony algorithm, and the exact solution of the path problem is solved by using the positive feedback mechanism of the improved ant colony algorithm; Finally, a robot experimental platform is built to verify the effectiveness and practicability of the improved particle swarm optimization and ant colony fusion algorithm. The experimental results show that the fusion algorithm has a certain guiding role for mobile robot path planning.