基于倾覆引导的崎岖地形自主导航轨迹优化
Capsizing-Guided Trajectory Optimization for Autonomous Navigation with Rough Terrain
AI总结：
针对崎岖地形下机器人自主导航的翻车风险，提出感知倾覆的轨迹规划器CAP，通过定义可通行方向并构建倾覆安全约束优化轨迹，显著提升了不平坦地形上的导航性能与安全性。
摘要：
由于存在复杂的障碍物和不平坦的地形，地面机器人在恶劣环境中进行自主导航是一项具有挑战性的任务。这要求轨迹规划在安全性和效率之间取得平衡。主要挑战在于生成一条可行的轨迹，既能防止机器人翻车，又能确保有效的导航。在本文中，我们提出了一种感知倾覆的轨迹规划器（CAP），以在不平坦地形上实现轨迹规划。分析了机器人在崎岖地形上的翻车稳定性。基于翻车稳定性，我们定义了可通行方向，该方向指示了机器人方向的安全范围。然后将该方向纳入轨迹优化的倾覆安全约束中。我们采用基于图的求解器来计算稳健且可行的轨迹，同时遵守倾覆安全约束。大量的仿真和真实世界实验验证了所提方法的有效性和稳健性。结果表明，CAP 优于现有的最先进方法，在不平坦地形上提供了增强的导航性能。
英文摘要：
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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