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Collision avoidance in unknown obstacle-cluttered environments may not alwaysbe feasible. This paper focuses on an emerging paradigm shift in whichpotential collisions with the environment can be harnessed instead of beingavoided altogether. To this end, we introduce a new sampling-based onlineplanning algorithm that can explicitly handle the risk of colliding with theenvironment and can switch between collision avoidance and collisionexploitation. Central to the planner's capabilities is a novel jointoptimization function that evaluates the effect of possible collisions using areflection model. This way, the planner can make deliberate decisions tocollide with the environment if such collision is expected to help the robotmake progress toward its goal. To make the algorithm online, we present a stateexpansion pruning technique that significantly reduces the search space whileensuring completeness. The proposed algorithm is evaluated experimentally witha built-in-house holonomic wheeled robot that can withstand collisions. Weperform an extensive parametric study to investigate trade-offs between(user-tuned) levels of risk, deliberate collision decision making, andtrajectory statistics such as time to reach the goal and path length.

Motion Planning for Collision-resilient Mobile Robots in Obstacle-cluttered Unknown Environments with Risk Reward Trade-offs