Unmanned Aerial Vehicle Path Planning Method Based on Search Rule and Cross Entropy Optimization

HU Lei; ZHAO Hui; NAN Yi; YI Guoxing; WANG Hao; CAO Zhihui

doi:10.11999/JEIT220579

Volume 45 Issue 6

Jun. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(6): 2144-2152

HU Lei, ZHAO Hui, NAN Yi, YI Guoxing, WANG Hao, CAO Zhihui. Unmanned Aerial Vehicle Path Planning Method Based on Search Rule and Cross Entropy Optimization[J]. Journal of Electronics & Information Technology, 2023, 45(6): 2144-2152. doi: 10.11999/JEIT220579

Citation:

HU Lei, ZHAO Hui, NAN Yi, YI Guoxing, WANG Hao, CAO Zhihui. Unmanned Aerial Vehicle Path Planning Method Based on Search Rule and Cross Entropy Optimization[J]. Journal of Electronics & Information Technology, 2023, 45(6): 2144-2152. doi: 10.11999/JEIT220579

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Unmanned Aerial Vehicle Path Planning Method Based on Search Rule and Cross Entropy Optimization

doi: 10.11999/JEIT220579

School of Astronautics, Harbin Institute of Technology, Harbin 150000, China

Received Date: 2022-05-10
Rev Recd Date: 2022-10-26

Available Online: 2022-11-03

Publish Date: 2023-06-10

Abstract

Abstract

The Rapidly-exploring Random Tree (RRT) algorithm has some shortcomings, including low computation efficiency and non-asymptotic optimality. An Improved RRT (IRRT) algorithm based on search rules and cross entropy optimization is presented in this paper. In the path search process, according to the current node position and search rules, the search step size and search direction are adjusted to achieve efficient and rapid initial path planning. Then, the cross entropy theory is applied to optimize the initial path, so that the path has the characteristic of asymptotic optimality. The simulation results of experiment 1 show the effectiveness and convergence of the proposed method, in the second simulation experiment, the proposed algorithm is compared with several variant RRT algorithms, and the results show that the proposed algorithm can ensure the computational efficiency and make the path has the characteristic of asymptotic optimality.
- Unmanned Aerial Vehicle (UAV),
- Path planning,
- Improved Rapidly-exploring Random Tree (IRRT) algorithm,
- Path optimization,
- Cross entropy

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References(18)

References

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