Haddeler Garen, Chuah Meng Yee Michael, You Yangwei, Chan Jianle, Adiwahono Albertus H, Yau Wei Yun, Chew Chee-Meng
Department of Mechanical Engineering, National University of Singapore (NUS), Singapore, Singapore.
Institute for Infocomm Research (I2R), ASTAR, Singapore, Singapore.
Front Robot AI. 2022 Aug 22;9:887910. doi: 10.3389/frobt.2022.887910. eCollection 2022.
Inspired by human behavior when traveling over unknown terrain, this study proposes the use of probing strategies and integrates them into a traversability analysis framework to address safe navigation on unknown rough terrain. Our framework integrates collapsibility information into our existing traversability analysis, as vision and geometric information alone could be misled by unpredictable non-rigid terrains such as soft soil, bush area, or water puddles. With the new traversability analysis framework, our robot has a more comprehensive assessment of unpredictable terrain, which is critical for its safety in outdoor environments. The pipeline first identifies the terrain's geometric and semantic properties using an RGB-D camera and desired probing locations on questionable terrains. These regions are probed using a force sensor to determine the risk of terrain collapsing when the robot steps over it. This risk is formulated as a collapsibility metric, which estimates an unpredictable region's ground collapsibility. Thereafter, the collapsibility metric, together with geometric and semantic spatial data, is combined and analyzed to produce global and local traversability grid maps. These traversability grid maps tell the robot whether it is safe to step over different regions of the map. The grid maps are then utilized to generate optimal paths for the robot to safely navigate to its goal. Our approach has been successfully verified on a quadrupedal robot in both simulation and real-world experiments.
受人类在未知地形上行走行为的启发，本研究提出使用探测策略并将其集成到可通行性分析框架中，以解决在未知崎岖地形上的安全导航问题。我们的框架将可塌陷性信息集成到现有的可通行性分析中，因为仅视觉和几何信息可能会被诸如软土、灌木丛区域或水坑等不可预测的非刚性地形误导。借助新的可通行性分析框架，我们的机器人对不可预测的地形有了更全面的评估，这对其在户外环境中的安全性至关重要。该流程首先使用RGB-D相机识别地形的几何和语义属性以及可疑地形上的期望探测位置。使用力传感器对这些区域进行探测，以确定机器人跨过该地形时地形塌陷的风险。这种风险被公式化为可塌陷性度量，用于估计不可预测区域的地面可塌陷性。此后，将可塌陷性度量与几何和语义空间数据相结合并进行分析，以生成全局和局部可通行性网格地图。这些可通行性网格地图告诉机器人跨过地图的不同区域是否安全。然后利用这些网格地图为机器人生成最优路径，以安全导航至其目标。我们的方法已在四足机器人的模拟和实际实验中成功得到验证。
