Defense Innovation Institute, Chinese Academy of Military Science, Beijing 100071, China.
Sensors (Basel). 2022 Nov 2;22(21):8429. doi: 10.3390/s22218429.
Exploration is an important aspect of autonomous robotics, whether it is for target searching, rescue missions, or reconnaissance in an unknown environment. In this paper, we propose a solution to efficiently explore the unknown environment by unmanned aerial vehicles (UAV). Innovatively, a topological road map is incrementally built based on Rapidly-exploring Random Tree (RRT) and maintained along with the whole exploration process. The topological structure can provide a set of waypoints for searching an optimal informative path. To evaluate the path, we consider the information measurement based on prior map uncertainty and the distance cost of the path, and formulate a normalized utility to describe information-richness along the path. The informative path is determined in every period by a local planner, and the robot executes the planned path to collect measurements of the unknown environment and restructure a map. The proposed framework and its composed modules are verified in two 3-D environments, which exhibit better performance in improving the exploration efficiency than other methods.
探索是自主机器人的一个重要方面，无论是在目标搜索、救援任务还是在未知环境中的侦察中。在本文中，我们提出了一种利用无人机（UAV）有效探索未知环境的解决方案。创新性地，基于快速扩展随机树（RRT）构建并维护拓扑图，贯穿整个探索过程。拓扑结构可以为搜索最佳信息路径提供一组航点。为了评估路径，我们考虑基于先验地图不确定性和路径距离成本的信息度量，并制定标准化效用来描述路径上的信息丰富度。通过局部规划器在每个周期确定信息路径，机器人执行规划路径以收集未知环境的测量值并重构地图。所提出的框架及其组成模块在两个 3D 环境中得到验证，与其他方法相比，该框架在提高探索效率方面表现出更好的性能。
