Robot Path Planning Algorithm Based on Particle Swarm Optimization and Feedforward Neural Network in Network Environment

Abstract

The main task of mobile robot path planning is: according to the environment model, the mobile robot is based on one or some optimization criteria: such as the minimum work cost, the shortest walking route, the shortest walking time, etc., to find a path in the motion space that does not occur with obstacles. Under the premise of collision, the collision-free path from the starting coordinate point to the target coordinate point allows the robot to reach the destination safely. At present, the path planning methods of mobile robots can be roughly divided into three categories according to their working methods. The first is path planning based on environmental models. It can handle path planning under the condition of fully known obstacle positions and shapes. In the environment, the path planning method based on the environment model will not be applicable. Specific methods such as A * [1], topological graph method [2], etc .; second is local path planning method based on sensor information, typical methods are: artificial potential field method [3], fuzzy logic method [4], etc .; third Behavior-based path planning method [5], which decomposes the navigation problem into independent modules such as collision avoidance and target guidance [6]. Practice shows that it is an effective method to apply neural network to automatic generation of robot trajectory and path planning of mobile robot.