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Stochastic Neural Signed Swept Volume for Real-time Chance-Constrained Trajectory Optimization

arXiv · AI, language, vision and robotics · article · Sep 18, 2026 · UTC

Collision-free motion planning requires reliable collision models from sensed environments and validation of states along a continuous trajectory. To make this tractable, most planners check for collision at discrete states along continuous trajectories against a single determinized model of the environment, introducing a trade-off between safety and computational efficiency. While continuous collision checking approaches that approximate the swept volume of the robot exist, they are computationally expensive or overly conservative. Data-driven approaches can learn the swept volume; however, t

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