Autonomous exploration is one of the important parts to achieve the fast autonomous mapping and target search. However, most of the existing methods are facing low-efficiency problems caused by low-quality trajectory or back-and-forth maneuvers. To improve the exploration efficiency in unknown environments, a fast autonomous exploration planner (FAEP) is proposed in this paper. Different from existing methods, we firstly design a novel frontiers exploration sequence generation method to obtain a more reasonable exploration path, which considers not only the flight-level but frontier-level factors in the asymmetric traveling salesman problem (ATSP). Then, according to the exploration sequence and the distribution of frontiers, an adaptive yaw planning method is proposed to cover more frontiers by yaw change during an exploration journey. In addition, to increase the speed and fluency of flight, a dynamic replanning strategy is also adopted. We present sufficient comparison and evaluation experiments in simulation environments. Experimental results show the proposed exploration planner has better performance in terms of flight time and flight distance compared to typical and state-of-the-art methods. Moreover, the effectiveness of the proposed method is further evaluated in real-world environments.

翻译：自主探索是实现快速自主地图构建与目标搜索的重要环节之一。然而，现有大多数方法因轨迹质量低或往复运动导致效率低下。为提高未知环境下的探索效率，本文提出一种快速自主探索规划器（FAEP）。与现有方法不同，我们首先设计了一种新颖的前沿探索序列生成方法以获得更合理的探索路径，该方法在非对称旅行商问题（ATSP）中不仅考虑飞行层级因素，还考虑了前沿层级因素。随后，根据探索序列与前沿分布，提出一种自适应偏航规划方法，通过在探索过程中改变偏航角来覆盖更多前沿区域。此外，为提升飞行速度与流畅性，还采用了动态重规划策略。我们在仿真环境中开展了充分的对比与评估实验。实验结果表明，与典型及最新方法相比，所提出的探索规划器在飞行时间与飞行距离方面均具有更优性能。同时，在真实环境中进一步验证了该方法有效性。