发表机构
Daniel Guggenheim School of Aerospace Engineering; Georgia Institute of Technology（丹尼尔·古根海姆航空航天工程学院; 佐治亚理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对自主空间探索中环境交互与感知系统的不确定性导致的安全规划问题，提出结合AO-RRT采样规划与序列凸规划的风险感知动力学运动规划方法，可将轨迹风险降低约97%。
AI 中文摘要
对于自主空间探索而言，机器人智能体需要执行运动规划，而环境交互可能是未知的。学习这类交互（例如轮式机器人的地形力学）会引入不确定性，进而导致危险的运动规划，可能引发危险操作或任务失败。此外，感知系统引发的不确定性会加剧安全运动规划的问题。在本论文中，我们研究具有风险感知的成本最优动力学运动规划问题，分两步解决：第一步，基于采样的规划器AO-RRT生成动态可行、风险感知且渐近成本最优的轨迹；第二步，我们将运动规划建模为非线性优化问题，以AO-RRT轨迹为初始解，使用序列凸规划（SCP）求解。通过使用条件风险价值（CVaR）量化风险，我们在仿真和硬件实验的所有轨迹中证明风险降低了约97%以上。
英文摘要
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.
Comments3 pages, 4 figures
