🤖 AI Summary
This work addresses the high-risk motion planning challenges in planetary environments arising from terrain mechanics and perception uncertainties by proposing a safety-aware planning framework that integrates risk awareness with dynamical feasibility. The approach leverages AO-RRT to generate asymptotically cost-optimal initial trajectories and employs sequential convex programming (SCP) to solve the resulting nonlinear optimization problem. Notably, it introduces Conditional Value-at-Risk (CVaR) into planetary motion planning for the first time, enabling quantitative assessment and optimization of trajectory risk. Experimental results demonstrate that the proposed method reduces trajectory risk by over 97% in both simulation and hardware platforms, significantly enhancing navigation safety and mission reliability for autonomous robots operating in unknown planetary terrains.
📝 Abstract
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.