Application of Improved Gray Wolf Algorithm in Trajectory Planning of Pallet Handling Robot

ZHANG Pan; LIU Yuhan; ZHANG Wei

doi:10.13433/j.cnki.1003-8728.20220286

Volume 43 Issue 3

Mar. 2024

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Article Navigation > Mechanical Science and Technology for Aerospace Engineering > 2024 > 43(3): 394-401

ZHANG Pan, LIU Yuhan, ZHANG Wei. Application of Improved Gray Wolf Algorithm in Trajectory Planning of Pallet Handling Robot[J]. Mechanical Science and Technology for Aerospace Engineering, 2024, 43(3): 394-401. doi: 10.13433/j.cnki.1003-8728.20220286

Citation:

ZHANG Pan, LIU Yuhan, ZHANG Wei. Application of Improved Gray Wolf Algorithm in Trajectory Planning of Pallet Handling Robot[J]. Mechanical Science and Technology for Aerospace Engineering, 2024, 43(3): 394-401. doi: 10.13433/j.cnki.1003-8728.20220286

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Application of Improved Gray Wolf Algorithm in Trajectory Planning of Pallet Handling Robot

doi: 10.13433/j.cnki.1003-8728.20220286

ZHANG Pan^{1, 2
,},
LIU Yuhan³,
ZHANG Wei^{1, 2, 4
,
,}

1.
College of Aeronautical Engineering, Civil Aviation University of China, Tianjin 300300, China
2.
Civil Aviation Key Laboratory of Artificial Intelligence for Airlines, Guangzhou 510470, China
3.
College of Safety Science and Engineering, Civil Aviation University of China, Tianjin 300300, China
4.
CAAC Aviation Ground Special Equipment Research Base, Tianjin 300300, China

Received Date: 2022-03-29
Publish Date: 2024-03-25

Abstract

Abstract

In order to improve the running stability of the pallet handling robot, an optimal trajectory planning method for robot acceleration based on the improved gray wolf algorithm is proposed. Aiming at the problems of local convergence and insufficient optimization performance of gray wolf algorithm, the Logistic-Tent chaotic map is introduced to optimize the initial population; the differential optimization algorithm is introduced to improve the global search ability; the elimination evolution mechanism is introduced to optimize the population structure and improve the optimization performance in all-round way. Compared with the standard gray wolf algorithm and the particle swarm algorithm, simulation results show that improved gray wolf algorithm has better convergence speed and algorithm accuracy in different types of test functions. In the application of the trajectory planning of the handling robot, after the optimization of the algorithm, the maximum joint angular acceleration of the robot is reduced by 44.11%, which greatly improves the running stability.
- handling robot,
- trajectory planning,
- acceleration,
- gray wolf algorithm

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References(20)

References

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