A guided autowave PCNN for improved real time path planning

Real time path planning for mobile robots requires fast convergence to optimal paths. Most rapid collision free path finding algorithms do not guarantee the optimality of the path. In this paper we present a Guided Autowave Pulse Coupled Neural Network (GAPCNN) approach for mobile robot path planning. The proposed model is a novel approach that improves upon the recently presented Modified PCNN by introducing directional autowave control and accelerated firing of neurons based on a dynamic thresholding technique. Simulation and experimental evaluation in both static and dynamic environments confirm GAPCNN to be a robust and time efficient path planning scheme for finding optimal paths.