Abstract: This paper proposes an optimal time trajectory planning method for a robotic arm based on an improved tuna swarm optimization algorithm.It aims to enable the robotic arm to complete tasks quickly while satisfying kinematic constraints during its movement.The improved tuna swarm optimization algorithm is an optimization of the standard tuna swarm optimization (TSO) algorithm,which employs tent chaotic population initialization and Levy flight.It introduces an adaptive threshold to improve the performance of the algorithm.The method focuses on a 6-degree-of-freedom serial manipulator as the research subject.It establishes a mathematical model for time optimization objectives and employs a 3-5-3 blended polynomial interpolation function as the foundation for trajectory planning.Experimental results show that the improved tuna swarm optimization algorithm has higher optimization accuracy and a more robust ability to escape local optimal solutions than the original algorithms.The optimized robotic arm's displacement,velocity,and acceleration curves are smooth and free from abrupt changes.This indicates that the improved tuna swarm optimization algorithm can effectively achieve optimal time trajectory planning for the robotic arm.

Key words: improved tuna swarm optimization algorithm, chaotic population initialization, levy flight, adaptive threshold, trajectory planning

摘要： 本文针对机械臂在满足运动学约束的前提下,以最短的时间完成工作任务问题,提出一种基于改进金枪鱼群算法的机械臂最优时间轨迹规划。本文在标准的金枪鱼群算法（TSO）上对其进行优化,采用tent混沌种群初始化和莱维飞行等方法进行改进,并引入自适应阈值来提高算法的性能。该方法以6自由度串联机械臂为研究对象,建立时间优化目标数学模型,以3-5-3混合多项式插值函数为基础对其进行轨迹规划。实验结果表明,改进的金枪鱼群算法相比原始算法具有更高的寻优精度和更强的跳出局部最优解能力,其优化后得到的机械臂的位移、速度、加速度曲线平滑,无突变,可以表明改进的金枪鱼群算法能有效地实现机械臂时间最优轨迹规划。

关键词: 改进金枪鱼群算法, 混沌种群初始化, 莱维飞行, 自适应阈值, 轨迹规划