Bai, R., Yuan, S., Li, K., Guo, H., Yau, W.-Y., & Xie, L. (2025). Realm: Real-Time Line-of-Sight Maintenance in Multi-Robot Navigation with Unknown Obstacles. 2025 IEEE International Conference on Robotics and Automation (ICRA), 7363–7369. https://doi.org/10.1109/icra55743.2025.11128211
Abstract:
Multi-robot navigation in complex environments relies on inter-robot communication and mutual observation for 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 between robots, this paper eliminates such requirements by directly formulating the LoS constraints from real-time LiDAR scans, adopting techniques in point cloud visibility analysis. Based on that, we propose a novel LoS-distance metric to quantify both the urgency and sensitivity of losing LoS between robots considering their potential 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 behavior to maintain LoS. The team connectivity is guaranteed by encoding the LoS constraints into a potential function that preserves the positivity of the Fiedler eigenvalue of robots’ underlying graph. Finally, we establish a LoS constrained
exploration framework integrating the proposed connectivity controller. We showcase its applications in multirobot exploration in complex unknown environments, where robots can always maintain the LoS connectivity through distributed sensing and communication while collaboratively exploring unknown environments.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the National Research Foundation - Medium Sized Center for Advanced Robotics Technology Innovation (CARTIN)
Grant Reference no. : N.A.
This research / project is supported by the A*STAR - Robotics Horizontal Technology Coordinating Office
Grant Reference no. : M22NBK0109