Line-of-Sight-Constrained Multi-Robot Mapless Navigation via Polygonal Visible Regions

2026-03-27

AI Summary

This work addresses the challenge of maintaining reliable line-of-sight (LoS) communication among multi-robot systems operating in unknown environments without access to a global map. To this end, the authors propose a distributed, map-free navigation framework in which each robot constructs a local polygonal visibility region from real-time LiDAR data. Robots share their visibility information to establish precise LoS constraints and integrate a topological optimization mechanism to prune redundant communication links. By eliminating the dependency on a global map, the approach significantly enhances both connectivity preservation and navigation efficiency. Experimental results demonstrate that, in complex obstacle-rich scenarios, the system consistently maintains LoS connectivity and achieves approximately 20% higher navigation efficiency compared to existing methods.

Abstract

Multi-robot systems rely on underlying connectivity to ensure reliable communication and timely coordination. This paper studies the line-of-sight (LoS) connectivity maintenance problem in multi-robot navigation with unknown obstacles. Prior works typically assume known environment maps to formulate LoS constraints between robots, which hinders their practical deployment. To overcome this limitation, we propose an inherently distributed approach where each robot only constructs an egocentric visible region based on its real-time LiDAR scans, instead of endeavoring to build a global map online. The individual visible regions are shared through distributed communication to establish inter-robot LoS constraints, which are then incorporated into a multi-robot navigation framework to ensure LoS-connectivity. Moreover, we enhance the robustness of connectivity maintenance by proposing a more accurate LoS-distance metric, which further enables flexible topology optimization that eliminates redundant and effort-demanding connections. The proposed framework is evaluated through extensive multi-robot navigation and exploration tasks in both simulation and real-world experiments. Results show that it reliably maintains LoS-connectivity between robots in challenging environments cluttered with obstacles, even under large visible ranges and fragile minimal topologies, where existing methods consistently fail. Ablation studies also reveal that topology optimization boosts navigation efficiency by around $20\%$, demonstrating the framework's potential for efficient navigation under connectivity constraints.

Problem

• multi-robot navigation
• line-of-sight connectivity
• mapless navigation
• unknown environments
• connectivity maintenance

Innovation

• mapless navigation
• line-of-sight connectivity
• distributed multi-robot system
• polygonal visible regions
• topology optimization