ColNav: Uncertainty-Aware Spacetime Collision Checking with Bounding Volume Hierarchies
Overview
This paper introduces ColNav, a robust collision checking method for mobile robots that encodes uncertainty into spacetime tubes organized via bounding volume hierarchies. The approach handles both bounded-set and Gaussian uncertainty descriptions, unifies static and dynamic obstacles, and achieves a 100% success rate under prediction model mismatch compared to 65% for the best baseline while maintaining up to three times the clearance in dense traffic; real-time operation on a physical robot is also validated.
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Reported quantity · Reported success rate: 100 percent · Basis not reported
“ers the entire horizon, rather than testing sampled time instants, and returns a robust geometric certificate together with an estimate of the time to collision. Static and dynamic obstacles share the same representation. Our approach reaches the goal with a 100% success rate under prediction model mismatch, where the best baseline reaches 65%, and maintains up to three times the clearance in dense traffic. We also v”
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- arXiv Robotics — research abstractsSignalColNav: Uncertainty-Aware Spacetime Collision Checking with Bounding Volume Hierarchies
This paper introduces ColNav, a robust collision checking method for mobile robots that encodes uncertainty into spacetime tubes organized via bounding volume hierarchies. The approach handles both bounded-set and Gaussian uncertainty descriptions, unifies static and dynamic obstacles, and achieves a 100% success rate under prediction model mismatch compared to 65% for the best baseline while maintaining up to three times the clearance in dense traffic; real-time operation on a physical robot is also validated.
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