Obstacle avoidance by using modified Hopfield neural network

Abstract

In this paper, path planning of a mobile robot by using a modified Hopfield neural network is studied. An area, which excludes obstacles and allows gradually changing of activation level of neurons from a starting point to a goal, is derived. Path can be constructed in this area by searching the next highest activated neuron. Even though asymmetric weight matrix is used, decreasing of system energy can be investigated. By comparison to a symmetric weight network, simulation results show more effective path, which could be generated by this algorithm. Simulation results showed constructed path from the starting point to the goal, which can avoid obstacle successfully.