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.

这项工作考虑了混沌路径规划的问题，使用改进的内存技术来提高性能。在此应用中，首先使用两个简单混沌映射的动力学来生成伪随机位生成器。以此为源，自主机器人生成并使用一系列导航命令来探索一个区域，同时保持随机和不可预测的运动。这种导航策略可以带来整体区域覆盖，但也会产生对先前小区的大量重新访问。在这里，应用了一种记忆技术来将机器人的混沌运动限制在访问次数最少的相邻单元格中，从而导致性能的整体改进。进行数值模拟以评估路径规划策略。仿真结果展示了与无内存技术相比以及与作者先前开发的反向信息素技术相比，覆盖性能的重大改进。此外，使用所提出的技术显着减少了对先前单元格的多次访问次数。