CONSORT: Uncertainty-Constrained Multi-Robot Planning for Cooperative Localization in GPS-Denied Environments
Overview
This research introduces CONSORT, a method for centralized multi-agent path planning that achieves cooperative localization in GPS-denied environments. By optimizing mean-state trajectories and propagating covariance only for terminal constraints, the approach handles uncertainty dynamics more tractably than prior belief-space formulations. Evaluated across 50 randomized scenarios, CONSORT solves 80% of cases where a strong baseline solves 54%, while maintaining comparable path lengths. Marine simulations using independently developed factor-graph estimates confirm that executed trajectories remain collision-free and terminal uncertainty stays within 6% of the planner's prediction.
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Environment: simulation
“Routing and Trajectories), a discrete-to-continuous pipeline combining hexagonal-grid joint search with spline-based trajectory refinement. Across 50 randomized scenarios, the planners separate as the terminal uncertainty bound tightens, with CONSORT solving 80% of scenarios where the strongest baseline solves 54%, at comparable path length. In marine simulations, with the plans executed under feedback control on an”
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Reported quantity · total randomized scenarios: 50 other · reported trials
“Routing and Trajectories), a discrete-to-continuous pipeline combining hexagonal-grid joint search with spline-based trajectory refinement. Across 50 randomized scenarios, the planners separate as the terminal uncertainty bound tightens, with CONSORT solving 80% of scenarios where the strongest baseline solves 54%, at comparable path length. In marine simulations, with the plans executed under feedback control on an”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalCONSORT: Uncertainty-Constrained Multi-Robot Planning for Cooperative Localization in GPS-Denied Environments
This research introduces CONSORT, a method for centralized multi-agent path planning that achieves cooperative localization in GPS-denied environments. By optimizing mean-state trajectories and propagating covariance only for terminal constraints, the approach handles uncertainty dynamics more tractably than prior belief-space formulations. Evaluated across 50 randomized scenarios, CONSORT solves 80% of cases where a strong baseline solves 54%, while maintaining comparable path lengths. Marine simulations using independently developed factor-graph estimates confirm that executed trajectories remain collision-free and terminal uncertainty stays within 6% of the planner's prediction.
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