navigation of a mobile robot using a virtual potential field and artificial neural network

mobile robot navigation is one of the basic problems in robotics. in this paper, a new approachis proposed for autonomous mobile robot navigation in an unknown environment. the proposedapproach is based on learning virtual parallel paths that propel the mobile robot toward the trackusing a multi-layer, feed-forward neural network. for training, a human operator navigates themobile robot in some different paths in the environment. both of human operator navigating dataand virtual parallel paths train the neural network. the neural network is able to map thecoordinate of a position to a mobile robot orientation and velocity. after training, the mobile robotcan plan a track between start and target position without the need of any human operator. whenthe environment surrounding the mobile robot is unknown, sensors are used to detect obstaclesand avoid collision. the simulated mobile robot is equipped with a rangefinder sensor. thesimulation shows promising results and high speed for real-time implementation in unknown andpartially dynamic environments.