Researchers at Chongqing University have developed a trajectory planning approach that enables an unmanned ground vehicle (UGV) to actively enhance its environmental perception using an accompanying unmanned aerial vehicle (UAV) equipped with a depth camera. The method treats the UAV as an agile sensing platform that is dispatched ahead of the UGV to explore areas where the vehicle's perception is limited, such as regions occluded by obstacles in urban or forested environments.
Key takeaways
- The approach clusters unknown voxels around the UGV path into 'edge clusters' that guide fine-grained planning, focusing UAV exploration on perception-critical areas.
- A hierarchical dispatcher first solves a maximum node coverage path problem with time windows using mixed-integer linear programming (MILP) to generate a coarse global path, then optimizes locally using viewpoints derived from edge clusters.
- Discrete navigation points are refined into continuous trajectories under smoothness, safety, and dynamic feasibility constraints.
- Simulation and real-world experiments confirm that the UAV-UGV framework significantly improves the vehicle's environmental awareness, enabling more informed navigation decisions.
This work addresses a common limitation in existing UAV-assisted UGV systems, where UAVs are often flown at fixed high altitudes with conservative strategies, limiting their flexibility and coordination. By actively dispatching the UAV based on the vehicle's motion intent, the proposed method maximizes the use of the UAV's dynamic capabilities, offering a practical solution for enhancing situational awareness in complex, obstacle-rich environments.
Source: 《机器人》期刊 (robot.sia.cn) · Published 2026-05-12 · “面向车辆主动周视增强的伴随无人机前探轨迹规划方法”