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实现有限环境线索下的自主搜索与空间遍历方面取得了进展。然而，该领域仍处于初期阶段，相关实验工作较少。现有实验尚未开发出稳健方法，以充分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克服的一系列直接问题。这些问题包括：（1）能基本维持机器人运动学效率的避障技术；（2）将混沌轨迹分散到待覆盖环境中的方法（对大型或复杂形状环境尤为关键）；（3）精确且不依赖于网格单元大小的实时覆盖计算技术。本文通过提出解决上述所有问题的算法（包括避障、混沌轨迹分散及精确覆盖计算技术）来推动该领域发展。这些算法能生成整体平滑的混沌轨迹，并提供高扫描覆盖率。算法基于ROS框架开发，构成了一套新设计的混沌路径规划应用。该应用的性能与传统最优路径规划器相当，并在不同大小、形状及障碍物密度的真实环境与Gazebo仿真中进行了测试。