CONSORT: Uncertainty-Constrained Multi-Robot Planning for Cooperative Localization in GPS-Denied Environments
CONSORT: 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.
The paper presents a discrete-to-continuous planning pipeline validated in marine simulations with feedback control, achieving collision-free trajectories and terminal uncertainty within 6% of predictions across 50 randomized scenarios.
Source: arXiv Robotics — research abstracts · Read original article ↗
Loading article text…
What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
- Environment
- SimulationOpen source S4
Reported numbers
total randomized scenarios
50
Reported trials
View original evidence
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
Open source S4
- paper: not_reported
- code: not_reported
- weights: not_reported
- dataset: not_reported
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
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
Open source S4
independently developed factor-graph estimate, the trajectories remain collision-free and their terminal uncertainty falls within 6% of the planner's prediction.
Open source S5
agent must reach a goal in minimum time subject to a chance-constrained collision-avoidance requirement and a terminal bound on localization uncertainty. We obtain a tractable surrogate of the underlying belief-space problem by optimizing over the mean-state trajectories of all agents and propagating covariance for terminal constraint evaluation only. We propose CONSORT (Cooperative Navigation with Support-Optimized
Open source S3
Source：arXiv Robotics — research abstracts · arxiv.org