Guided Autowave Pulse Coupled Neural Network (GAPCNN) based real time path planning and an obstacle avoidance scheme for mobile robots

• Investigates the shortcomings of the MPCNN model proposed recently for mobile robots.
• Directional constraints on the autowave for informed search of configuration space.
• Dynamic thresholding of the underlying neural network to increase time efficiency.
• On ground implementation that verifies the efficacy of the proposed algorithm.

Real time path planning for mobile robots requires fast convergence to optimal paths. Most rapid collision free path planning 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 (MPCNN) by introducing directional autowave control and accelerated firing of neurons based on a dynamic thresholding technique. Simulation studies and experimental results in both static as well as dynamic environments confirm GAPCNN to be a robust and time efficient path planning scheme for finding optimal paths.