用风险感知算法让火星机器人更安全地规划路径
Risk-Aware Kinodynamic Motion Planning Under Uncertainty For Safe Navigation on Planetary Environments
- 基于采样与优化结合的方法，生成兼顾动态可行性与风险控制的路径
- 在仿真和实测中实现超过97%的风险降低
- 适合航天探测、高风险自主导航场景
在自主太空探索中，机器人需在环境交互未知的情况下进行运动规划。学习如轮式机器人地形力学等交互特性会引入不确定性，导致高风险路径规划，甚至引发任务失败。此外，基于感知系统的不确定性会加剧安全规划难题。本文提出一种风险感知的代价最优运动规划方法：首先使用采样式规划器（AO-RRT）生成动态可行、风险敏感且渐近代价最优的轨迹；其次将运动规划建模为非线性优化问题，利用序列凸规划（SCP）求解，并以AO-RRT轨迹作为初始解。通过条件风险价值（CVaR）量化风险，在仿真与硬件实验中均证明路径风险降低超97%。
原文摘要 · Abstract (English)
For autonomous space exploration, robotic agents need to perform motion planning in which environmental interactions may be unknown. Learning these interactions, such as terrain mechanics for wheeled robots, can introduce uncertainties that lead to risky motion plans and potentially hazardous operations or mission failures. Moreover, uncertainties induced by perception-based systems can exacerbate the problem of safe motion planning. In this letter, we address the problem of performing cost-optimal kinodynamic motion planning with risk awareness. We approach this in two steps. First, a sampling-based planner (AO-RRT) generates a dynamically feasible, risk-aware, and asymptotically cost-optimal trajectory. Second, we formulate motion planning as a nonlinear optimization problem and solve it using sequential convex programming (SCP), using the AO-RRT trajectory as an initial solution. By quantifying risk using conditional value-at-risk (CVaR), we demonstrate a reduction in risk by over $\sim$97\% across trajectories in simulation and hardware experiments.
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