Efficient Collaborative Navigation through Perception Fusion for Multi-Robots in Unknown Environments

TL;DR

This paper introduces a novel multi-robot collaborative navigation method using perception fusion and a graph attention architecture to improve exploration efficiency in unknown environments, validated through simulations and real-world tests.

Contribution

It presents a new perception-based collaborative planning approach with GIWT architecture for multi-robot navigation in unknown environments, enhancing efficiency and path optimization.

Findings

Achieves approximately 82% accuracy on expert dataset.

Reduces average path length by about 8% and 6% in simulations.

Reduces path length by over 6% in real-world experiments.

Abstract

For tasks conducted in unknown environments with efficiency requirements, real-time navigation of multi-robot systems remains challenging due to unfamiliarity with surroundings.In this paper, we propose a novel multi-robot collaborative planning method that leverages the perception of different robots to intelligently select search directions and improve planning efficiency. Specifically, a foundational planner is employed to ensure reliable exploration towards targets in unknown environments and we introduce Graph Attention Architecture with Information Gain Weight(GIWT) to synthesizes the information from the target robot and its teammates to facilitate effective navigation around obstacles.In GIWT, after regionally encoding the relative positions of the robots along with their perceptual features, we compute the shared attention scores and incorporate the information gain obtained from neighboring robots as a supplementary weight. We design a corresponding expert data generation scheme to simulate real-world decision-making conditions for network training. Simulation experiments and real robot tests demonstrates that the proposed method significantly improves efficiency and enables collaborative planning for multiple robots. Our method achieves approximately 82% accuracy on the expert dataset and reduces the average path length by about 8% and 6% across two types of tasks compared to the fundamental planner in ROS tests, and a path length reduction of over 6% in real-world experiments.