Autonomous exploration is grounded on target decision and trajectory planning, which is widely deployed on unmanned aerial vehicles. However, existing methods generally only focus on the exploration effect of target decision but neglect the environment information gained with trajectory planning during flight, resulting in redundant exploration trajectories and low exploration efficiency. This article proposes an upgraded method of trajectory planning for autonomous exploration work. We design a fresh cost term considering the frontier information in the part of trajectory optimization. Besides, yaw angles are planned independently to catch more environment information during flight. We present extensive simulations and real-world tests. The results show that our proposed method reduces the exploration cost time by 10–15% compared with the previous one.

自主探索基于目标决策和轨迹规划，广泛部署在无人机上。然而，现有方法普遍只关注目标决策的探索效果，而忽略了飞行过程中轨迹规划获得的环境信息，导致探索轨迹冗余，探索效率低下。本文提出了一种用于自主探索工作的轨迹规划升级方法。我们在轨迹优化部分考虑了前沿信息，设计了一个新的成本项。此外，偏航角是独立规划的，可以在飞行过程中捕捉更多的环境信息。我们提供了广泛的模拟和真实世界的测试。