Enhancing Cooperative Exploration and Planning: UAV-Legged Robot Synergy

摘要

Specialized robots, such as legged robots and unmanned aerial vehicles (UAVs), are commonly regarded as effective platforms for aiding in search and rescue (SAR) missions. However, existing approaches often decouple the tasks between UAVs and legged robots, for instance, using UAVs for mapping and legged robots for operation. Such feature hinders the realization of collaborative advantages and consequently reduces task completion efficiency. This letter proposes a perception-planning framework for collaborative SAR missions involving UAVs and legged robots. The framework primarily utilizes UAVs to efficiently search for target objects in unknown environments, while also determining a feasible path for the legged robots to reach the targets. More specifically, the UAV employs an optimized frontier-based exploration method to explore unknown environments. Simultaneously, a depth-first reachable search tree is utilized to swiftly assess the traversability of the guided path of the legged robot, thereby accelerating the exploration process. After locating the target, the method further constrains the exploration area based on permissible ellipses until an the shortest path is found. The proposed approach is evaluated through experiments in both simulation and real-world scenarios and the results demonstrate that our approach exhibits significantly higher exploration efficiency compared to state-of-the-art methods, reducing completion times in SAR tasks by over 18.3%.