增强型白鲸蝠鲼融合算法机械臂插值轨迹优化方法

高永新; 刘俊毅; 汪洋; 贾东

doi:10.12263/DZXB.20250355

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增强型白鲸蝠鲼融合算法机械臂插值轨迹优化方法

学术论文 | 更新时间：2025-12-27

- 增强型白鲸蝠鲼融合算法机械臂插值轨迹优化方法
- The Enhanced Whale Manta Ray Fusion Algorithm for Robotic Arm Trajectory Optimization Method
- 电子学报 2025年53卷第8期页码：2915-2935
- 作者机构：
  
  辽宁工程技术大学机械工程学院，辽宁阜新 123000
- 作者简介：
  
  [ "高永新男，1973年11月出生于辽宁省阜新市.现为辽宁工程技术大学机械工程学院副教授.主要研究方向为机电一体化.E-mail: gaoyx1125@126.com" ]
  [ "刘俊毅男，2001年6月出生于山东省潍坊市.现为辽宁工程技术大学机械工程学院硕士研究生.主要研究方向为机器人设计与轨迹优化.E-mail: 1312529551@qq.com" ]
  [ "汪洋男，2000年11月出生于江苏省无锡市.现为辽宁工程技术大学机械工程学院硕士研究生.主要研究方向为新能源汽车驱动系统. Email: 2356384131@qq.com" ]
  [ "贾东男，1995年5月出生于黑龙江省齐齐哈尔市.现为辽宁工程技术大学机械工程学院硕士研究生.主要研究方向为故障诊断. Email: 1332755792@qq.com" ]
- 基金信息：
  
  国家科学信息技术部研究中心“十四五”全国科学技术发展研究规划重点课题(KXJS71057);农业部“十四五”国家科技支撑计划重点课题(NYFZ51050);教育部“十四五”教育科研规划重点课题(JXKY24391)
- DOI：10.12263/DZXB.20250355
  中图分类号： TP242;TP18
- 收稿：2025-05-05，
  
  录用：2025-08-13，
  
  纸质出版：2025-08-25
- 稿件说明：
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高永新, 刘俊毅, 汪洋, 等. 增强型白鲸蝠鲼融合算法机械臂插值轨迹优化方法[J]. 电子学报, 2025, 53(08): 2915-2935.

GAO Yong-xin, LIU Jun-yi, WANG Yang, et al. The Enhanced Whale Manta Ray Fusion Algorithm for Robotic Arm Trajectory Optimization Method[J]. Acta Electronica Sinica, 2025, 53(08): 2915-2935.
高永新, 刘俊毅, 汪洋, 等. 增强型白鲸蝠鲼融合算法机械臂插值轨迹优化方法[J]. 电子学报, 2025, 53(08): 2915-2935. DOI：10.12263/DZXB.20250355

GAO Yong-xin, LIU Jun-yi, WANG Yang, et al. The Enhanced Whale Manta Ray Fusion Algorithm for Robotic Arm Trajectory Optimization Method[J]. Acta Electronica Sinica, 2025, 53(08): 2915-2935. DOI：10.12263/DZXB.20250355

摘要

为克服传统白鲸优化算法（Beluga Whale Optimization，BWO）在3-5-3多项式插值机械臂轨迹优化中存在的路径长、时间耗费高及易陷入局部最优的问题，本文提出了一种增强型白鲸-蝠鲼融合优化算法（Enhanced Beluga Whale and manta ray fusion Optimization algorithm，EBWO）.该算法以机械臂最优运动时间为目标，构建约束优化模型，并通过增广拉格朗日乘子法转化为无约束形式.首先，利用改进的对数非线性Halton混沌序列优化种群初始化，提高搜索多样性与质量；其次，设计多方向正余弦白鲸位置更新机制，增强开发阶段搜索能力；再次，在中期迭代阶段引入改进的蝠鲼旋风链式觅食策略，并结合Levy飞行机制构建新觅食因子，以强化局部开发与全局跳跃能力；最后，提出基于资源竞争耦合机制的自适应鲸落策略，并引入量子隧穿效应，以提升算法跳出局部最优的能力与收敛速度.实验结果表明：在3-5-3轨迹优化中，EBWO较于传统BWO将时间优化效果提升了8.69%，并且与未优化的轨迹相比，优化后的时间缩短了42.13%.这一结果验证了其在复杂优化任务时的有效性与实用性.

Abstract

To overcome the issues of path length

high time consumption

and the tendency to get trapped in local optima in the traditional beluga whale optimization algorithm (BWO) for 3-5-3 polynomial interpolation robotic arm trajectory optimization

this paper proposes an enhanced whale-manta ray fusion optimization algorithm (EBWO). The algorithm aims to optimize the robotic arm’s motion time

constructing a constrained optimization model

which is then converted into an unconstrained form using the augmented Lagrangian multiplier method. Firstly

an improved logarithmic nonlinear Halton chaotic sequence is used to optimize population initialization

enhancing search diversity and quality. Secondly

a multi-directional cosine whale position update mechanism is designed to strengthen the search ability in the exploitation phase. In the mid-iteration stage

an improved manta ray whirlwind chain hunting strategy is introduced

combined with a Levy flight mechanism to build a new hunting factor

enhancing both local exploitation and global jumping abilities. Lastly

an adaptive whale fall strategy based on a resource-competition coupling mechanism is proposed

incorporating quantum tunneling effects to improve the algorithm’s ability to escape local optima and convergence speed. Experimental results show that EBWO improves time optimization by 8.69% over traditional BWO in 3-5-3 trajectory optimization and reduces time by 42.13% compared to the non-optimized trajectory

demonstrating its effectiveness and practicality in complex optimization tasks.

关键词

Keywords

references

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