Abstract
This paper proposes an improved rapidly-exploring random tree star (RRT*) based on the Harris corner detection algorithm to aid unmanned aerial vehicles (UAVs) in their autonomous path planning during flight tasks. Two stages comprise the proposed method (RRT*-Harris): (1) build the search tree trunk using Harris corner detection and Dijkstra algorithms; and (2) continue searching for and optimizing the exploring random tree using RRT*. In particular, RRT*-Harris incorporates corner detection and graph search algorithms into the traditional RRT* algorithm, thereby improving the problem of judging feasible solutions in path planning. Furthermore, the simulation results demonstrate that the proposed algorithm outperformed the conventional planning algorithm in terms of convergence precision, speed, and stability. Its efficiency is 80 ~ 95% higher than that of the traditional RRT* algorithm in both standard and special labyrinths. In addition, the algorithm is shown to be well adaptive to 2D trajectory optimization in complex environments.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China under Grant 62103451 and 61973327, in part by the Fundamental Research Funds for the Central Universities under Grant 22qntd0702.
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Chen, D., Yang, L., Zhu, X., Zhu, G., Wang, Y., Zhu, B. (2023). RRT*-Harris: An Efficient Two-Stage Approach for Autonomous Path Planning of UAVs. In: Yan, L., Duan, H., Deng, Y. (eds) Advances in Guidance, Navigation and Control. ICGNC 2022. Lecture Notes in Electrical Engineering, vol 845. Springer, Singapore. https://doi.org/10.1007/978-981-19-6613-2_279
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DOI: https://doi.org/10.1007/978-981-19-6613-2_279
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