AGCoTrack: A Communication-Efficient Independent Reinforcement Learning Method for Aerial-Ground Collaborative Tracking

In the realm of the Internet of Things, a multitude of interconnected and intelligent devices enable profound interaction with the physical world. Within this context, a combination of aerial and ground robots for active target tracking aims to leverage their complementary capabilities to improve task efficiency in practice. However, most previous works rely on homogeneous and centralized methods among heterogeneous robots, which often leads to serious communication challenges that may undermine multirobot collaboration. At the same time, in nature, crows and wolves effectively collaborate in predation by utilizing their complementary abilities and communication mechanisms. Inspired by this phenomenon, we propose aerial-ground collaborative tracking (AGCoTrack), an independent reinforcement learning framework integrated with an efficient communication module for AGCoTrack. Empowered by this module, aerial and ground robots can dynamically determine the timing and content of communication. We conduct experiments in various environments, and the results demonstrate the ability of our method to reduce both communication frequency and bandwidth in comparison with baselines.