Abstract

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.

Highlights

  • We extend the work published by [39] and propose a robust and efficient trajectory replanning method based on the guiding path (RETRBG) for unknown environments

  • Aiming to reduce the spatial information loss caused by discrete control space and improve the quality of the initial path, this paper proposes a guided kinodynamic path searching (GKPS) method based on the guiding path, which retains the advantages of the kinodynamic path searching and improves the safety with the help of the guiding path

  • 4, we propose a can generate a safe, dynamically feasible and purposeful path that is minimal with respect guided kinodynamic path searching method (GKPS) according to the guiding path, which togenerate time duration control cost in a and voxel grid map.path

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Summary

Introduction

Some researchers have studied the path planning algorithm of UAVs to ensure they fly under the interference of external environmental factors [13,14], and some researchers have proposed some path planning algorithms for the case of hardware failure and other emergencies [14,15,16]. All these efforts are devoted to improving the safety of UAVs in special environment, to improve the intelligence of UAVs and ensure they can fly safely and quickly in unknown cluttered environments autonomously. According to the different types of algorithms, the existing algorithms can be mainly divided into hard-constrained methods [17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33] and gradient-based optimization methods [34,35,36,37,38,39,40,41,42,43,44]

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