Many practical applications of robotics require systems that can operate safely despite uncertainty. In the context of motion planning, two types of uncertainty are particularly important when planning safe robot trajectories. The first is environmental uncertainty -- uncertainty in the locations of nearby obstacles, stemming from sensor noise or (in the case of obstacles' future locations) prediction error. The second class of uncertainty is uncertainty in the robots own state, typically caused by tracking or estimation error. To achieve high levels of safety, it is necessary for robots to consider both of these sources of uncertainty. In this paper, we propose a risk-bounded trajectory optimization algorithm, known as Sequential Convex Optimization with Risk Optimization (SCORA), to solve chance-constrained motion planning problems despite both environmental uncertainty and tracking error. Through experiments in simulation, we demonstrate that SCORA significantly outperforms state-of-the-art risk-aware motion planners both in planning time and in the safety of the resulting trajectories.

翻译：许多机器人实际应用要求系统在存在不确定性的情况下仍能安全运行。在运动规划中，规划安全机器人轨迹时，两类不确定性尤为重要。其一是环境不确定性——由于传感器噪声（或对于障碍物未来位置的预测误差）导致的附近障碍物位置不确定性。其二是机器人自身状态的不确定性，通常由跟踪或估计误差引起。为实现高水平安全性，机器人必须同时考虑这两类不确定性来源。本文提出一种名为“带风险优化的序列凸优化”（Sequential Convex Optimization with Risk Optimization, SCORA）的风险有界轨迹优化算法，以求解同时存在环境不确定性和跟踪误差的机会约束运动规划问题。通过仿真实验，我们证明SCORA在规划时间和生成轨迹的安全性方面均显著优于最先进的风险感知运动规划器。