Lin, Q., Lu, W., Meng, L., Li, C., & Liang, B. (2024). Efficient Collaborative Navigation via Perception Fusion for Multi-Robots in Unknown Environments. arXiv preprint arXiv:2411.01274.
This paper aims to address the challenge of real-time navigation for multi-robot systems in unknown environments, particularly in scenarios where creating a global map is time-consuming or unnecessary. The authors propose a novel method that leverages the local perception capabilities of multiple robots for efficient collaborative path planning.
The proposed method employs a hierarchical architecture consisting of a foundational planner (DHbug algorithm) and a graph neural network (GIWT). The DHbug algorithm ensures reliable exploration towards the target by generating precise speed and angular velocity commands based on local obstacle detection. The GIWT network enhances the DHbug algorithm by intelligently selecting search directions at critical decision points, leveraging the fused perception data from the target robot and its teammates. The authors designed an expert data generation scheme to train the GIWT network and validated their method through simulations and real-world experiments.
The proposed hierarchical collaborative path planning method effectively combines the advantages of a rule-based planner (DHbug) and a learning-based optimizer (GIWT) to achieve efficient and reliable multi-robot navigation in unknown environments. The method's effectiveness is demonstrated through extensive simulations and real-world experiments.
This research contributes to the field of multi-robot systems by presenting a practical and efficient solution for collaborative navigation in unknown environments. The proposed method has potential applications in various domains, including exploration, search and rescue, and agriculture.
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by Qingquan Lin... at arxiv.org 11-05-2024
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