Coordinated multi-robot navigation is an essential ability for a team of robots operating in diverse environments. Robot teams often need to maintain specific formations, such as wedge formations, to enhance visibility, positioning, and efficiency during fast movement. However, complex environments such as narrow corridors challenge rigid team formations, which makes effective formation control difficult in real-world environments. To address this challenge, we introduce a novel Adaptive Formation with Oscillation Reduction (AFOR) approach to improve coordinated multi-robot navigation. We develop AFOR under the theoretical framework of hierarchical learning and integrate a spring-damper model with hierarchical learning to enable both team coordination and individual robot control. At the upper level, a graph neural network facilitates formation adaptation and information sharing among the robots. At the lower level, reinforcement learning enables each robot to navigate and avoid obstacles while maintaining the formations. We conducted extensive experiments using Gazebo in the Robot Operating System (ROS), a high-fidelity Unity3D simulator with ROS, and real robot teams. Results demonstrate that AFOR enables smooth navigation with formation adaptation in complex scenarios and outperforms previous methods. More details of this work are provided on the project website: https://hcrlab.gitlab.io/project/afor.

Correspondence identification (CoID) is an essential capability in multi-robot collaborative perception, which enables a group of robots to consistently refer to the same objects within their respective fields of view. In real-world applications, such as connected autonomous driving, vehicles face challenges in directly sharing raw observations due to limited communication bandwidth. In order to address this challenge, we propose a novel approach for bandwidth-adaptive spatiotemporal CoID in collaborative perception. This approach allows robots to progressively select partial spatiotemporal observations and share with others, while adapting to communication constraints that dynamically change over time. We evaluate our approach across various scenarios in connected autonomous driving simulations. Experimental results validate that our approach enables CoID and adapts to dynamic communication bandwidth changes. In addition, our approach achieves 8%-56% overall improvements in terms of covisible object retrieval for CoID and data sharing efficiency, which outperforms previous techniques and achieves the state-of-the-art performance. More information is available at: https://gaopeng5.github.io/acoid.

Coordinating heterogeneous robots is essential for autonomous multi-robot teaming. To execute a set of dependent tasks as quickly as possible, and to complete tasks that cannot be addressed by individual robots, it is necessary to form subteams that can collaboratively finish the tasks. It is also advantageous for robots to wait for teammates and tasks to become available in order to form better subteams or reduce the overall completion time. To enable both abilities, we introduce a new graph learning approach that formulates heterogeneous collaborative scheduling as a bipartite matching problem that maximizes a reward matrix learned via imitation learning. We design a novel graph attention transformer network (GATN) that represents the problem of collaborative scheduling as a bipartite graph, and integrates both local and global graph information to estimate the reward matrix using graph attention networks and transformers. By relaxing the constraint of one-to-one correspondence in bipartite matching, our approach allows multiple robots to address the same task as a subteam. Our approach also enables voluntary waiting by introducing an idle task that the robots can select to wait. Experimental results have shown that our approach well addresses heterogeneous collaborative scheduling with dynamic subteam formation and voluntary waiting, and outperforms the previous and baseline methods.