Path planning for mobile robot using sonar map and neural network

The purpose of this paper is to present a new approach For path planing of a mobile robot in static outdoor environments. A simple sensor model is developed for fast acquisition of environment information. The obstacle avoidance system is based on a micro-controller interfaced with multiple ultrasonic transducers with a rotating motor. Using sonar readings and environment knowledge, a local map based on weight evaluation function is built for the robot path planing. The path planner finds the local optimal path using the A* search algorithm. The robot is trained to learn a goal-directed task under adequate supervision. The simulation experiments show that a robot, utilizing our neural network scheme, can learn tasks of obstacle avoidance in the work space of a certain geometrical complexity. The result shows that the proposed algorithm can be efficiently implemented in an outdoor environment.