A chaotic path planning generator enhanced by a memory technique

Abstract

This work considers the problem of chaotic path planning, using an improved memory technique to boost performance. In this application, the dynamics of two simple chaotic maps are first used to generate a pseudo-random bit generator. Using this as a source, a series of navigation commands are generated and used by an autonomous robot to explore an area, while maintaining a random and unpredictable motion. This navigation strategy can bring overall area coverage, but also yields numerous revisits to previous cells. Here, a memory technique is applied to limit the chaotic motion of the robot to adjacent cells with the least number of visits, leading to overall improvement in performance. Numerical simulations are performed to evaluate the path planning strategy. The simulation results showcase a major improvement in coverage performance compared to the memory-free technique and also compared to an inverse pheromone technique previously developed by the authors. Also, the number of multiple visits to previous cells is significantly reduced with the proposed technique. © 2021 Elsevier B.V