让机器人在崎岖地形上不翻倒地高效导航
Capsizing-Guided Trajectory Optimization for Autonomous Navigation with Rough Terrain
- 基于翻倒稳定性分析，定义安全朝向范围
- 引入防翻倒约束，优化轨迹避免失稳
- 仿真与实测均验证效果优于现有方法
地面机器人在复杂环境中自主导航面临非平凡障碍与不平地形的挑战，需在安全与效率间取得平衡。核心难点在于生成既能防止翻倒又可有效导航的可行轨迹。本文提出一种防翻倒轨迹规划器（CAP），通过分析机器人在崎岖地形上的翻倒稳定性，定义可通行朝向（traversable orientation），即机器人安全朝向范围，并将其嵌入轨迹优化的防翻倒约束中。采用基于图的求解器，在满足防翻倒约束条件下计算出鲁棒且可行的轨迹。大量仿真与真实世界实验验证了该方法的有效性与鲁棒性，结果表明CAP优于现有最先进方法，在不平地形上展现出更优的导航性能。
原文摘要 · Abstract (English)
It is a challenging task for ground robots to autonomously navigate in harsh environments due to the presence of non-trivial obstacles and uneven terrain. This requires trajectory planning that balances safety and efficiency. The primary challenge is to generate a feasible trajectory that prevents robot from tip-over while ensuring effective navigation. In this paper, we propose a capsizing-aware trajectory planner (CAP) to achieve trajectory planning on the uneven terrain. The tip-over stability of the robot on rough terrain is analyzed. Based on the tip-over stability, we define the traversable orientation, which indicates the safe range of robot orientations. This orientation is then incorporated into a capsizing-safety constraint for trajectory optimization. We employ a graph-based solver to compute a robust and feasible trajectory while adhering to the capsizing-safety constraint. Extensive simulation and real-world experiments validate the effectiveness and robustness of the proposed method. The results demonstrate that CAP outperforms existing state-of-the-art approaches, providing enhanced navigation performance on uneven terrains.
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