Autonomous exploration is crucial and highly challenging for various intelligent operations of unmanned aerial vehicles. However, low-efficiency problems caused by low-quality trajectory or back-and-forth maneuvers (BFMs) still exist. To improve the exploration efficiency in unknown environments, a fast autonomous exploration planner is proposed in this article. We first design a novel frontier exploration sequence generation method to significantly reduce BFM during the exploration by considering multilevel factors in an asymmetric traveling salesman problem. 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 fluency of flight, a dynamic replanning strategy is also adopted. The main technical contributions of the proposed approach are to provide a comprehensive global coverage path with less BFM and use adaptive yaw planning to generate more reasonable yaw trajectory. We present extensive comparison and real-world experiments to validate the effectiveness and correctness of our method. Experimental results show that the proposed approach has better performance compared to typical and state-of-the-art methods in these flat scenarios without lots of scattered obstacles.

Path planning is one of the key parts of unmanned aerial vehicle (UAV) fast autonomous flight in an unknown cluttered environment. However, real-time and stability remain a significant challenge in the field of path planning. To improve the robustness and efficiency of the path planning method in complex environments, this paper presents RETRBG, a robust and efficient trajectory replanning method based on the guiding path. Firstly, a safe guiding path is generated by using an improved A* and path pruning method, which is used to perceive the narrow space in its surrounding environment. Secondly, under the guidance of the path, a guided kinodynamic path searching method (GKPS) is devised to generate a safe, kinodynamically feasible and minimum-time initial path. Finally, an adaptive optimization function with two modes is proposed to improve the optimization quality in complex environments, which selects the optimization mode to optimize the smoothness and safety of the path according to the perception results of the guiding path. The experimental results demonstrate that the proposed method achieved good performance both in different obstacle densities and different resolutions. Compared with the other state-of-the-art methods, the quality and success rate of the planning result are significantly improved.

Unmanned aerial vehicles (UAVs) are widely employed across diverse fields due to their flexibility and scalability. However, achieving full autonomy remains a challenge as human intervention is still required in most scenarios. Motion planning, a cornerstone of UAV autonomous navigation, has garnered extensive attention, with numerous advanced algorithms having been proposed in recent years. This paper provides a comprehensive overview of UAV motion planning frameworks, systematically addressing three key components: map representation, path planning, and trajectory optimization. Map representation establishes environmental awareness, path planning balances efficiency and safety in path generation, and trajectory optimization refines paths into feasible, energy-efficient motions. Unlike prior reviews focused on specific techniques, this study offers an integrated perspective, helping researchers understand the overall framework and recent advancements in UAV motion planning. Additionally, emerging trends and potential strategies are discussed to improve the efficiency, adaptability, and robustness of UAVs to meet increasingly complex mission requirements.

LiDAR is a crucial sensor for 3D environment perception. However, limited by the field of view of the LiDAR, it is sometimes difficult to achieve complete coverage of the environment with a single LiDAR. In this paper, we designed a spinning actuated LiDAR mapping system that is compatible with both UAV and backpack platforms and propose a tightly coupled laser–inertial SLAM algorithm for it. In our algorithm, edge and plane features in the point cloud are first extracted. Then, for the significant changes in the distribution of point cloud features between two adjacent scans caused by the continuous rotation of the LiDAR, we employed an adaptive scan accumulation method to improve the stability and accuracy of point cloud registration. After feature matching, the LiDAR feature factors and IMU pre-integration factor are added to the factor graph and jointly optimized to output the trajectory. In addition, an improved loop closure detection algorithm based on the Cartographer algorithm is used to reduce the drift. We conducted exhaustive experiments to evaluate the performance of the proposed algorithm in complex indoor and outdoor scenarios. The results showed that our algorithm is more accurate than the state-of-the-art algorithms LIO-SAM and FAST-LIO2 for the spinning actuated LiDAR system, and it can achieve real-time performance.

Path planning is one of the key parts of unmanned aerial vehicle (UAV) fast autonomous flight in cluttered environments. However, it remains a challenge to efficiently generate a high-quality trajectory for flight tasks with a high success rate. In this paper, a robust planning framework is proposed, which can stably support autonomous flight tasks in complex unknown environments with limited onboard computing resources. Firstly, we propose the directed frontier point information structure (DFP), which can roughly capture the frontier information of the explored environment. The planning direction of a local planner can be evaluated and rectified efficiently based on the DFP to avoid falling into traps with limited cost. Secondly, an adaptive fusion replanning method is designed to generate a high-quality trajectory efficiently by incorporating two optimization methods with different characteristics, which can both take advantage of different optimization methods while avoiding disadvantages as much as possible, but also adjust the focus of the optimization according to the actual situation to improve the success rate of the planning method. Finally, sufficient comparison and evaluation experiments in simulation environments are presented. Experimental results show the proposed method has better performance, especially in terms of adaptability and robustness, compared to typical and state-of-the-art methods in unknown complex scenarios. Moreover, the proposed system is integrated into a fully autonomous quadrotor, and the effectiveness of the proposed method is further evaluated by using the quadrotor in real-world environments.