开源代码: https://github.com/NKU-MobFly-Robotics/HighStar

       无人机在自主探索中得到了广泛应用，但由于运动时间代价评估不准确以及高计算成本，其无人机的运动速度未充分开发。现有方法要么未能同时考虑无人机的运动趋势和环境，要么无法在大型三维环境中确保实时规划。

本文提出了一种高速且高效的在线自主无人机探索方法。首先，提出了一种运动基元激活的图搜索方法，以充分利用无人机当前的速度和加速度。然后，优化了在速度凸包约束的非零终端速度实现连续探索。最后，进一步优化了探索路径周围未知空间的 SE(3) 覆盖轨迹。所提出方法的无人机速度提高了 18.6% - 116.1%，计算成本降低了 25.6% - 90.9%。探索效率比最先进的方法高出 13.8% - 49.5%。

       Q. Dong, X. Zhang*, S. Zhang, Z. Wang, Z. Ma, T, Li and H. Xi. HIGHSTAR: High-Speed and Efficient Online Autonomous UAV Exploration. IEEE Transactions on Automation Science and Engineering (accepted).

Abstract

       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.