Hopfield neural networks for path planning in dynamic and unknown environments

Abstract

To deal with the path planning of mobile robot in dynamic and unknown environment,an efficient and locally connected Hopfield neural network (HNN) is proposed to represent the workspace of the robot.The robot dynamically traced the numerical potential field of the HNN by hill climbing method to find the collision-free path without any unexpected local attractive points.The safety of the planned path was considered in the weight design of the HNN, and local virtual repulsive forces were formed around obstacles to generate safe path.The HNN model considered the time delay of signal diffusion and had asymmetric weights.The stability of the HNN was analyzed and the given stable condition of the HNN was independent on the time-delay of signal diffusion.Because the model emphasizes on the diffusion of maximal stimulation, the given stable condition is more relaxed and leads the HNN to represent a large workspace with more grids.To efficiently simulate the HNN,combining the constrained distance transformation and the delays in HNN,sequential simulation of the HNN on a single processor is proposed to plan path in \%O(N)\% time,where \%N\% is the number of the nodes of the HNN.The \%O(N)\% time complexity of sequential simulation accelerates the path re-planning on-line.The simulations and experiments demonstrate the effectiveness of the method.