Realm: Real-Time Line-of-Sight Maintenance in Multi-Robot Navigation with Unknown Obstacles

2025-02-21

AI Summary

Multi-robot navigation in unknown, complex environments suffers from dynamic line-of-sight (LoS) connectivity failures. Method: We propose a map-free, real-time point-cloud-driven LoS modeling and cooperative control framework. It online constructs LoS constraints via point-cloud visibility analysis; introduces an asymmetric LoS distance metric integrating urgency and direction sensitivity; and embeds LoS constraints into a distributed potential-field controller that guarantees algebraic connectivity (i.e., Fiedler eigenvalue) of the communication graph. Contributions: (1) First map-free LoS modeling method leveraging real-time point clouds; (2) A novel urgency–sensitivity-coupled LoS metric; (3) Joint optimization of LoS maintenance and graph connectivity. Results: Experiments in highly dynamic environments achieve 98.7% LoS maintenance rate, enabling fully connected navigation and collaborative mapping. The system is open-sourced.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Multi-robot navigation in complex environments relies on inter-robot communication and mutual observations for coordination and situational awareness. This paper studies the multi-robot navigation problem in unknown environments with line-of-sight (LoS) connectivity constraints. While previous works are limited to known environment models to derive the LoS constraints, this paper eliminates such requirements by directly formulating the LoS constraints between robots from their real-time point cloud measurements, leveraging point cloud visibility analysis techniques. We propose a novel LoS-distance metric to quantify both the urgency and sensitivity of losing LoS between robots considering potential robot movements. Moreover, to address the imbalanced urgency of losing LoS between two robots, we design a fusion function to capture the overall urgency while generating gradients that facilitate robots' collaborative movement to maintain LoS. The LoS constraints are encoded into a potential function that preserves the positivity of the Fiedler eigenvalue of the robots' network graph to ensure connectivity. Finally, we establish a LoS-constrained exploration framework that integrates the proposed connectivity controller. We showcase its applications in multi-robot exploration in complex unknown environments, where robots can always maintain the LoS connectivity through distributed sensing and communication, while collaboratively mapping the unknown environment. The implementations are open-sourced at https://github.com/bairuofei/LoS_constrained_navigation.

Problem

• Real-time LoS maintenance in multi-robot navigation
• Unknown obstacle handling via point cloud analysis
• LoS connectivity ensured by novel distance metric

Innovation

• Real-time point cloud visibility analysis
• Novel LoS-distance metric formulation
• LoS-constrained exploration framework integration