Extended Version: Multi-Robot Motion Planning with Cooperative Localization

2025-04-08

AI Summary

This paper addresses the Cooperative Localization Multi-Robot Motion Planning (CL-MRMP) problem under sensor noise, where robots act as mobile sensors that mutually provide observations, necessitating joint optimization of motion trajectories and state estimates. To overcome the conservatism and safety risks arising from conventional methods’ neglect of inter-robot state dependencies, we propose the first probabilistic planning framework that explicitly models state correlations via chance constraints. Our method introduces a probabilistically complete safe sampling algorithm, augmented with a bias-aware sampling strategy to accelerate convergence. It integrates covariance propagation, correlation-aware modeling, and cooperative localization fusion. Experiments across multiple benchmark tasks demonstrate significant improvements in planning success rate and safety, with up to 40% faster convergence—validating the approach’s robustness and effectiveness under realistic uncertainty.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Multiagent Systems: Multiagent Planning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

We consider the uncertain multi-robot motion planning (MRMP) problem with cooperative localization (CL-MRMP), under both motion and measurement noise, where each robot can act as a sensor for its nearby teammates. We formalize CL-MRMP as a chance-constrained motion planning problem, and propose a safety-guaranteed algorithm that explicitly accounts for robot-robot correlations. Our approach extends a sampling-based planner to solve CL-MRMP while preserving probabilistic completeness. To improve efficiency, we introduce novel biasing techniques. We evaluate our method across diverse benchmarks, demonstrating its effectiveness in generating motion plans, with significant performance gains from biasing strategies.

Problem

• Solving uncertain multi-robot motion planning with cooperative localization
• Addressing motion and measurement noise in robot-robot correlations
• Enhancing efficiency via novel biasing techniques in planning

Innovation

• Chance-constrained motion planning with safety guarantees
• Sampling-based planner preserving probabilistic completeness
• Novel biasing techniques for improved efficiency