In recent years, advancements have been made towards the goal of using chaotic coverage path planners for autonomous search and traversal of spaces with limited environmental cues. However, the state of this field is still in its infancy as there has been little experimental work done. Current experimental work has not developed robust methods to satisfactorily address the immediate set of problems a chaotic coverage path planner needs to overcome in order to scan realistic environments within reasonable coverage times. These immediate problems are as follows: (1) an obstacle avoidance technique which generally maintains the kinematic efficiency of the robot's motion, (2) a means to spread chaotic trajectories across the environment (especially crucial for large and/or complex-shaped environments) that need to be covered, and (3) a real-time coverage calculation technique that is accurate and independent of cell size. This paper aims to progress the field by proposing algorithms that address all of these problems by providing techniques for obstacle avoidance, chaotic trajectory dispersal, and accurate coverage calculation. The algorithms produce generally smooth chaotic trajectories and provide high scanning coverage of environments. These algorithms were created within the ROS framework and make up a newly developed chaotic path planning application. The performance of this application was comparable to that of a conventional optimal path planner. The performance tests were carried out in environments of various sizes, shapes, and obstacle densities, both in real-life and Gazebo simulations.

翻译：近年来，在利用混沌覆盖路径规划器对环境线索有限的空间进行自主搜索与遍历方面取得了进展。然而，该领域仍处于初期阶段，相关实验工作十分有限。现有实验尚未开发出稳健方法，以妥善解决混沌覆盖路径规划器在合理覆盖时间内扫描真实环境所需应对的一系列直接问题。这些问题包括：(1) 一种能大体保持机器人运动学效率的障碍规避技术；(2) 一种将混沌轨迹扩散到需覆盖环境（尤其对于大型和/或复杂形状环境至关重要）的方法；(3) 一种实时、精确且独立于网格尺寸的覆盖计算技术。本文旨在通过提出解决所有上述问题的算法，提供障碍规避、混沌轨迹扩散与精确覆盖计算的技术，以推动该领域发展。这些算法可生成大体平滑的混沌轨迹，并实现对环境的高扫描覆盖率。该算法是在ROS框架内开发的，构成了一个新开发的混沌路径规划应用。该应用的性能与常规最优路径规划器相当。性能测试在多种尺寸、形状和障碍密度（包括真实环境与Gazebo仿真）的环境中进行。