Path Planning and Obstacle Avoidance for Mobile Robots in a Dynamic Environment

Abstract

Because traditional obstacle avoidance path planning methods have a lot of problems, such as large amount of calculation, low efficiency, poor optimization capability, and lack of dealing with dynamic obstacles, a new method which implements real-time path planning of mobile robot is presented. The method builds a neural network model for the robot workspace, and then it uses the model to obtain the relationship between the dynamic obstacles and the network output. It can choose the local optimal collision-free path by the path planning in a dynamic environment (PPIDE) algorithm to find the path between two points for dealing with obstacles. The proposed method is suitable for dynamic environment where both linear and planar obstacles exist. Simulation results prove its effectiveness.