Path Planning for Autonomous Robots Using Neural Networks

Abstract

The realization that some forms of artificial potential fields (APF) for path planning can be developed by parallel distributed means has prompted research efforts in applying neural networks to the problem of generating these fields. This paper presents a neural network paradigm capable of exhibiting behavior that is useful for developing a variety of artificial potential fields for evaluating paths for autonomous robots. The neural network is called the Wave Expansion Neural Network or WENN. The WENN develops a topologically ordered neural map of the robot's environment, specifying the obstacles and the target configuration. The WENN neuron activity distribution is used as an APF to evaluate paths for the robot. The results are supported by computer simulations to illustrate the performance of the network.