Heuristic Algorithm for Robot Path Planning Based on a Growing Elastic Net

A simple effective method for path planning based on a growing self-organizing elastic neural network, enhanced with a heuristic for the exploration of local directions is presented. The general problem is to find a collision-free path for moving objects among a set of obstacles. A path is represented by an interconnected set of processing units in the elastic self organizing network. The algorithm is initiated with a straight path defined by a small number of processing units between the start and goal positions. The two units at the extremes of the network are static and are located at the start and goal positions, the remaining units are adaptive. Using a local sampling strategy of the points around each processing unit, a Kohonen type learning and a simple processing units growing rule the initial straight path evolves into a collision free path. The proposed algorithm was experimentally tested for 2 DOF and 3 DOF robots on a workspace cluttered with random and non random distributed obstacles. It is shown that with very little computational effort a satisfactory free collision path is calculated.