Mobile Robot Navigation Using MLP-BP Approaches in Dynamic Environments

To find an optimal path for robots in an environment that is only partially known and continuously changing is a difficult problem. This paper presents a new method for generating a collision-free near-optimal path and speed for a mobile robot in a dynamic environment containing moving and static obstacles using artificial neural network. For each robot motion, the workspace is divided into five equal segments. The multilayer perceptron neural network is used to choose a collision-free segment and also controls the speed of the robot for each motion. Experimental results show that the method is efficient and gives near-optimal path reaching the target position of the mobile robot.