HIGHSTAR: High-Speed and Efficient Online Autonomous UAV Exploration
Qianli Dong, Xuebo Zhang, Shiyong Zhang, Ziyu Wang, Zhe Ma, Tianyi Li, Haobo Xi · IEEE Transactions on Automation Science and Engineering · 2025
Unmanned aerial vehicles (UAVs) are widely used in autonomous exploration, but their motion speed is underutilized due to inaccurate motion time cost evaluation and high computational cost. Existing methods either fail to consider UAV’s motion tendency and environment simultaneously or can’t ensure real-time planning in large 3-D environments. This paper presents a consistent, high-speed, and efficient online autonomous UAV exploration method. First, a motion primitive activated graph search method is proposed to fully take advantage of the UAV’s current velocity and acceleration. It improves motion time cost evaluation by simulating short-term motion tendencies with motion primitives and reduces the computational cost by searching on a voxel graph with a dynamic upper bound. Then, a minimum time trajectory to the optimal viewpoint with a non-zero terminal velocity constraint in a convex hull is optimized. Finally, an SE(3) coverage trajectory for unknown space around the exploration path is further optimized. Simulations in various environments with different speed settings show that the proposed method’s average UAV velocity is 18.6%−116.1% faster and its exploration efficiency is 13.8%−49.5% higher than state-of-the-art methods. Real-world tests verify its effectiveness. The source code of our method will be released at: https://github.com/NKU-MobFly-Robotics/HighStar.