Probabilistic Kinematic State Estimation for Motion Planning of Planetary Rovers

Kinematics-based collision detection is important for robot motion planning in unstructured terrain. Especially, planetary rovers require such capability as a single collision may lead to the termination of a mission. For onboard computation, typical numeric approaches are unsuitable as they are computationally expensive and unstable on rocky terrain; instead, a light-weight analytic solution (ACE: Approximate Clearance Evaluation) is planning to be used for the Mars 2020 rover mission. ACE computes the state bounds of articulated suspension systems from terrain height bounds, and assess the safety by checking the constraint violation of states with the worst-case values. ACE's conservative safety check approach can sometimes lead to over-pessimism: feasible states are often reported as infeasible, thus resulting in frequent false positive detection. In this paper, we introduce a computationally efficient probabilistic variant of ACE (called p-ACE) which estimates the probability distributions of states in real time. The advantage of having probability distributions over states, instead of deterministic bounds, is to provide more flexible and less pessimistic worst-case evaluation with probabilistic safety guarantees. Empirically derived distribution models are used to compute the total probability of constraint satisfaction, which is then used for path assessment. Through experiments with a high-fidelity simulator, we empirically show that p-ACE relaxes the deterministic state bounds without losing safety guarantees.