A Hybrid Planning Method for 3D Autonomous Exploration in Unknown Environments With a UAV

Xuning Chen, Jianying Zheng, Qinglei Hu

Abstract: This article investigates the autonomous exploration problem of an unmanned aerial vehicle (UAV) in a fully unknown three-dimensional (3D) space, subject to the constraints of collision avoidance, energy-saving, and computation consumption. To tackle this problem, a hybrid planning algorithm named FSHP is proposed. The algorithm consists of a novel local planner designed to explore unknown space within the onboard camera’s field of view (FoV) faster and less computationally. The local planner is a combination of the frontier-based and sampling-based methods, overcoming the bottlenecks of high computational time for the former and non-heuristics for the latter. Furthermore, the algorithm incorporates a global planner based on historical information to enhance performance in larger and more complex scenarios. The global planner includes a historical road map (HRM) using the rapidly-exploring random tree (RRT) and a historical tree (HST) based on the k-dimension (k-d) tree, built simultaneously. When no informative viewpoints are nearby, the planner replans trajectories globally to unexplored space. Finally, the proposed approach is evaluated in both simulations and real-world experiments, demonstrating the effectiveness and efficiency of the FSHP. Note to Practitioners—The motivation of this paper stems from the need to develop a fast and efficient autonomous exploration algorithm for a UAV for practical applications such as 3D reconstruction, search-and-rescue and military reconnaissance. Frontier-based and sampling-based methods are widely used to solve this problem due to their heuristics and low computational effort, respectively. However, either method can not meet the requirements related to exploration efficiency arising from increasingly complex and diverse tasks. To speed up the exploration process, reduce the exploration time and shorten the exploration path length, we propose this new method FSHP. It combines the advantages of global exploration (frontier-based methods) and local exploration (sampling-based methods) with random sampling in the frontiers. Furthermore, the replanning target selection and waypoints optimization schemes helps in reducing the path. Overall, this novel framework, FSHP, enables efficient and effective autonomous exploration tasks.