Interaction-Aware Sampling-Based Motion Planning for Autonomous Surface Vessels in Urban Waterways
Overview
This paper introduces a decentralized motion planner using model predictive path integral control for autonomous surface vessels in urban waterways. The method jointly plans trajectories for the ego vessel and predicts other vessels' paths to handle interactions, encoding traffic rules as discontinuous costs. Evaluated on real maps of Amsterdam canals with 780 randomized runs, it achieves collision-free goal reaching and outperforms optimization-based baselines. Physical validation on two scaled prototypes confirms onboard planning at 10 Hz successfully resolving head-on and crossing encounters without collisions or rule violations.
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Control: reported autonomous
“arXiv:2610.41676v1 Announce Type: new Abstract: Autonomous surface vessels operating in urban waterways must coordinate with other vessels in confined spaces while complying with traffic regulations, often without knowing their intentions. We present a decentralized motion planner based on model predictive path integral control, a sampling-based model predictive control scheme. The planner jointly computes a trajecto”
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Reported quantity · runs: 780 other · unique elapsed hours
“Across 780 randomized runs the method reaches every goal without collisions or deadlocks, and outperforms an optimization-based planner designed for the same domain, as well as a version of the proposed planner that does not account for the interaction. We further validate the planner on two scaled vessel prototypes in an outdoor environment, where planning and localization run onboard at 10 Hz, and head-on and cross”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalInteraction-Aware Sampling-Based Motion Planning for Autonomous Surface Vessels in Urban Waterways
This paper introduces a decentralized motion planner using model predictive path integral control for autonomous surface vessels in urban waterways. The method jointly plans trajectories for the ego vessel and predicts other vessels' paths to handle interactions, encoding traffic rules as discontinuous costs. Evaluated on real maps of Amsterdam canals with 780 randomized runs, it achieves collision-free goal reaching and outperforms optimization-based baselines. Physical validation on two scaled prototypes confirms onboard planning at 10 Hz successfully resolving head-on and crossing encounters without collisions or rule violations.
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