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An Efficient Heuristic Rapidly-Exploring Random Tree for Unmanned Aerial Vehicle

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Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD 2022)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 153))

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Abstract

Autonomous unmanned aerial vehicle (UAV) can be utilized to replace humans to do hard work or work in dangerous environment. Path planning is one of the crucial technologies for intelligent flight of UAV. The Rapidly-exploring Random Tree (RRT) has a wide range of applications in path planning with the advantage of the sampling-based path planning which avoids complex construction of the configuration space. However, these methods perform a uniform random sampling and thus do not lead to the most efficient solution. In this paper, in order to improve the efficiency of the traditional RRT, a heuristic strategy with goal-bias was exploited and incorporated in the traditional RRT. A simulation platform was built to assess the performance of the improved algorithm and analyze the influences of the parameters, and a flight test platform was established based on the DJI Matrice 100 to carry out the flight experiments of UAV path planning. The simulations proved that the heuristic RRT is much higher efficient than the standard version. The experiments validated the feasibility to apply the new RRT in the navigation for UAVs in reality.

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References

  1. Matlekovic, L., Juric, F., Schneider-Kamp, P.: Microservices for autonomous UAV inspection with UAV simulation as a service. Simul. Model. Pract. Theory 119, 102548 (2022)

    Article  Google Scholar 

  2. Shi, K.J., Wu, P., Liu, M.S.: Research on path planning method of forging handling robot based on combined strategy. In: 2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA), pp. 292–295. IEEE (2021)

    Google Scholar 

  3. Yuan, C.R., Zhang, W.Q., Liu, G.F., et al.: A heuristic rapidly-exploring random trees method for manipulator motion planning. IEEE Access 8, 900–910 (2020)

    Article  Google Scholar 

  4. Zhao, Y.Q., Liu, K., Lu, G.H., et al.: Path planning of UAV delivery based on improved APF-RRT* algorithm. J. Phys: Conf. Ser. 1624(4), 042004 (2020)

    Google Scholar 

  5. Alexopoulos, C., Griffin, P.M.: Path planning for a mobile robot. IEEE Trans. Syst. Man Cybern. 22(2), 318–322 (1992)

    Article  Google Scholar 

  6. LaValle, S.M.: Rapidly-exploring random trees: a new tool for path planning. Iowa State University (1998)

    Google Scholar 

  7. LaValle, S.M., Kuffner, J.J.: Rapidly-exploring random trees: progress and prospects. In: 4th International Workshop on the Algorithmic Foundations of Robotics (WAFR), pp. 293–308. A K Peters, Ltd., Dartmouth College, Hanover, New Hampshire, USA (2001)

    Google Scholar 

  8. Lindemann, S.R., LaValle, S.M.: Current issues in sampling-based motion planning. pp. 36–54. Springer, Berlin, Heidelberg (2005)

    Google Scholar 

  9. Tsardoulias, E.G., Iliakopoulou, A., Kargakos, A., Petrou, L.: A review of global path planning methods for occupancy grid maps regardless of obstacle density. J. Intell. Rob. Syst. 84(1–4), 829–858 (2016). https://doi.org/10.1007/s10846-016-0362-z

    Article  Google Scholar 

  10. Xue, Y., Zhang, X., Jia, S., et al.: Hybrid bidirectional rapidly-exploring random trees algorithm with heuristic target graviton. In: 2017 Chinese Automation Congress (CAC), pp. 4357–4361 (2017)

    Google Scholar 

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Acknowledgement

This work was supported by the NNSF of China under Grant 81401405.

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Authors and Affiliations

  1. School of Aerospace Engineering, Xiamen University, Xiamen, 361102, China

    Chunping Yin & Qun Liu

  2. Department of Marine Physics and Engineering, Xiamen University, Xiamen, 361102, China

    Meijin Lin & Hongmei Zhu

Authors
  1. Chunping Yin
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  2. Meijin Lin
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  3. Qun Liu
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  4. Hongmei Zhu
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Corresponding author

Correspondence to Meijin Lin .

Editor information

Editors and Affiliations

  1. Division of Intelligent Future Technologies, Mälardalen University, Västerås, Västmanlands Län, Sweden

    Ning Xiong

  2. Department of Electronic and Computer Engineering, Brunel University London, Uxbridge, Middlesex, UK

    Maozhen Li

  3. School of Information Science and Technology, Hunan University, Changsha, Hunan, China

    Kenli Li

  4. School of Information Science and Technology, Hunan University, Changsha, Hunan, China

    Zheng Xiao

  5. College of Computer and Data Science, Fuzhou University, Fuzhou, Fujian, China

    Longlong Liao

  6. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Lipo Wang

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Yin, C., Lin, M., Liu, Q., Zhu, H. (2023). An Efficient Heuristic Rapidly-Exploring Random Tree for Unmanned Aerial Vehicle. In: Xiong, N., Li, M., Li, K., Xiao, Z., Liao, L., Wang, L. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 153. Springer, Cham. https://doi.org/10.1007/978-3-031-20738-9_85

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