Abstract
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, these neural models are susceptible to approximation errors and are therefore often limited to serving as coarse filters for downstream collision checkers. In this work, we propose to learn a signed distance function of the swept volume as a probabilistic field, enabling quantification of epistemic uncertainty, incorporation of perception noise, and eventual integration into a chance-constrained trajectory optimization framework. We demonstrate our approach on challenging high-dimensional manipulation problems with significant sensor noise, both in simulation and on real hardware.
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Publication details
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- Open access
- Green open access
Cite this article
APA 7
Chen, Q., Zhang, K., Chen, L., & Kingston, Z. (2026). Stochastic Neural Signed Swept Volume for Real-time Chance-Constrained Trajectory Optimization. https://omanscience.com/en/articles/stochastic-neural-signed-swept-volume-for-real-time-chance-constrained-trajectory-optimization
MLA 9
Chen, Qingyi, et al. "Stochastic Neural Signed Swept Volume for Real-time Chance-Constrained Trajectory Optimization." https://omanscience.com/en/articles/stochastic-neural-signed-swept-volume-for-real-time-chance-constrained-trajectory-optimization.
Chicago (author–date)
Chen, Qingyi, Kevin Zhang, Lucas Chen, and Zachary Kingston. 2026. "Stochastic Neural Signed Swept Volume for Real-time Chance-Constrained Trajectory Optimization." https://omanscience.com/en/articles/stochastic-neural-signed-swept-volume-for-real-time-chance-constrained-trajectory-optimization.
Harvard
Chen, Q., Zhang, K., Chen, L. and Kingston, Z. (2026) 'Stochastic Neural Signed Swept Volume for Real-time Chance-Constrained Trajectory Optimization', Available at: https://omanscience.com/en/articles/stochastic-neural-signed-swept-volume-for-real-time-chance-constrained-trajectory-optimization.
Vancouver
Chen Q, Zhang K, Chen L, Kingston Z. Stochastic Neural Signed Swept Volume for Real-time Chance-Constrained Trajectory Optimization. https://omanscience.com/en/articles/stochastic-neural-signed-swept-volume-for-real-time-chance-constrained-trajectory-optimization
IEEE
Q. Chen, K. Zhang, L. Chen, and Z. Kingston, "Stochastic Neural Signed Swept Volume for Real-time Chance-Constrained Trajectory Optimization," https://omanscience.com/en/articles/stochastic-neural-signed-swept-volume-for-real-time-chance-constrained-trajectory-optimization.