论文标题
采取综合感知和运动计划，并具有分配强大的风险限制
Towards Integrated Perception and Motion Planning with Distributionally Robust Risk Constraints
论文作者
论文摘要
在不确定和动态的环境中安全部署机器人需要对各种风险进行系统的核算，这些风险在自主堆栈中从感知到运动计划和控制的自主堆栈中的各个层。许多广泛使用的运动计划算法并未充分纳入固有的感知和预测不确定性，通常完全忽略它们或对高斯的可疑假设。我们提出了一个基于分布的强大增量运动计划框架，该框架明确，连贯地结合了感知和预测不确定性。我们设计了输出反馈策略，并考虑基于力矩的歧义分布集，以在模棱两可集合中最差的案例分布下执行概率碰撞避免限制。我们的解决方案方法，称为输出反馈分布在稳健的$ rrt^{*} $（ofdr- $ rrt^{*}）$中，可为在动态，杂乱和不确定的环境中运行的机器人的渐近最佳风险的轨迹产生最佳的风险轨迹，并明确地构图和本地化误差，可置于障碍，无效的动作，无效的动作，无效的动作，并实现了宽松的动作。数值实验说明了所提出的算法的有效性。
Safely deploying robots in uncertain and dynamic environments requires a systematic accounting of various risks, both within and across layers in an autonomy stack from perception to motion planning and control. Many widely used motion planning algorithms do not adequately incorporate inherent perception and prediction uncertainties, often ignoring them altogether or making questionable assumptions of Gaussianity. We propose a distributionally robust incremental sampling-based motion planning framework that explicitly and coherently incorporates perception and prediction uncertainties. We design output feedback policies and consider moment-based ambiguity sets of distributions to enforce probabilistic collision avoidance constraints under the worst-case distribution in the ambiguity set. Our solution approach, called Output Feedback Distributionally Robust $RRT^{*}$(OFDR-$RRT^{*})$, produces asymptotically optimal risk-bounded trajectories for robots operating in dynamic, cluttered, and uncertain environments, explicitly incorporating mapping and localization error, stochastic process disturbances, unpredictable obstacle motion, and uncertain obstacle locations. Numerical experiments illustrate the effectiveness of the proposed algorithm.
