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Safely deploying robots in uncertain and dynamic environments requires asystematic accounting of various risks, both within and across layers in anautonomy stack from perception to motion planning and control. Many widely usedmotion planning algorithms do not adequately incorporate inherent perceptionand prediction uncertainties, often ignoring them altogether or makingquestionable assumptions of Gaussianity. We propose a distributionally robustincremental sampling-based motion planning framework that explicitly andcoherently incorporates perception and prediction uncertainties. We designoutput feedback policies and consider moment-based ambiguity sets ofdistributions to enforce probabilistic collision avoidance constraints underthe worst-case distribution in the ambiguity set. Our solution approach, calledOutput Feedback Distributionally Robust $RRT^{*}$(OFDR-$RRT^{*})$, producesasymptotically optimal risk-bounded trajectories for robots operating indynamic, cluttered, and uncertain environments, explicitly incorporatingmapping and localization error, stochastic process disturbances, unpredictableobstacle motion, and uncertain obstacle locations. Numerical experimentsillustrate the effectiveness of the proposed algorithm.

Towards Integrated Perception and Motion Planning with Distributionally Robust Risk Constraints