Graph-Based RL for Sequential Rendezvous Planning with Multiple Patrolling USVs
Overview
This paper introduces a framework combining grid-based path planning, surrogate travel-time prediction, and graph-based reinforcement learning to optimize sequential rendezvous operations for multiple unmanned surface vehicles (USVs). The proposed method utilizes a Feasible Transition Graph (FTG) to model spatiotemporal relationships and employs a surrogate model to predict travel times efficiently. Evaluation results indicate statistically significant average objective reductions of 10.5%, 3.5%, and 1.5% compared to greedy, MLP-RL, and 2-step lookahead greedy baselines, respectively, while requiring approximately 1/35 of the computation time of the latter.
Generated from attributed reports · Updated 6 hours ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Environment: simulation
“ority-dependent due dates. We propose a framework combining grid-based path planning, surrogate travel-time prediction, and graph-based reinforcement learning. The path planner accounts for ocean currents and approximates turn-rate constraints through multi-stage maneuvers, with path feasibility empirically validated through trajectory-tracking simulations. For rendezvous sequencing, a Feasible Transition Graph (FTG)”
Exact source · revision 1Source owner not reported
Report timeline
Follow attributed reports and material updates.
- arXiv Robotics — research abstractsSignalGraph-Based RL for Sequential Rendezvous Planning with Multiple Patrolling USVs
This paper introduces a framework combining grid-based path planning, surrogate travel-time prediction, and graph-based reinforcement learning to optimize sequential rendezvous operations for multiple unmanned surface vehicles (USVs). The proposed method utilizes a Feasible Transition Graph (FTG) to model spatiotemporal relationships and employs a surrogate model to predict travel times efficiently. Evaluation results indicate statistically significant average objective reductions of 10.5%, 3.5%, and 1.5% compared to greedy, MLP-RL, and 2-step lookahead greedy baselines, respectively, while requiring approximately 1/35 of the computation time of the latter.
Event coverage history
There is not enough continuous observation data to show a trend.
Timezone · UTC
Article dates follow your selected timezone. Briefing editions use Hong Kong time (UTC+8).