Enhanced Probabilistic Collision Detection for Motion Planning Under Sensing Uncertainty

Enhanced Probabilistic Collision Detection for Motion Planning Under Sensing Uncertainty Xiaoli Wang* Sipu Ruan* Xin Meng Gregory S. Chirikjian Abstract --Probabilistic collision detection (PCD) is essential in motion planning for robots operating in unstructured environments, where considering sensing uncertainty helps prevent damage. Existing PCD methods mainly used simplified geometric models and addressed only position estimation errors. This paper presents an enhanced PCD method with two key advancements: (a) using superquadrics for more accurate shape approximation and (b) accounting for both position and orientation estimation errors to improve robustness under sensing uncertainty. Our method first computes an enlarged surface for each object that encapsulates its observed rotated copies, thereby addressing the orientation estimation errors. Then, the collision probability under the position estimation errors is formulated as a chance-constraint problem that is solved with a tight upper bound. Both the two steps leverage the recently developed normal parameterization of superquadric surfaces. Results show that our PCD method is twice as close to the Monte-Carlo sampled baseline as the best existing PCD method and reduces path length by 30% and planning time by 37%, respectively. A Real2Sim pipeline further validates the importance of considering orientation estimation errors, showing that the collision probability of executing the planned path in simulation is only 2%, compared to 9% and 29% when considering only position estimation errors or none at all. I NTRODUCTION Collision detection is essential to motion planning, which helps to prevent robots from colliding with their surroundings. Although traditional collision detection methods have been developed for decades, they usually assume perfect knowledge of the states of robots and environments [1]. This assumption does not apply in most real-world applications, especially for service robots with a high degree of freedom (DOF) manipulating objects in domestic settings.