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基于改进遗传粒子群混合算法的机械臂时间最优轨迹规划

Time-optimal trajectory planning for manipulator based on improved genetic particle swarm optimization hybrid algorithm

  • 摘要: 针对机械臂时间最优轨迹规划问题,提出了一种基于改进遗传粒子群混合算法的时间最优轨迹规划方法。以六自由度机械臂运动参数为约束条件,在关节空间中利用3次均匀B样条曲线构造了机械臂的轨迹。首先引入罚函数优化适应度函数来改进遗传算法,进而引入自适应权重因子和柯西变异算子来改进粒子群算法,并将改进后的粒子群作为交叉算子融合到遗传算法中。结果表明:改进遗传粒子群混合算法相较于遗传算法和粒子群算法,其求解精度更高,优化效率相较于单算法有了显著提高,验证了该方法应用于机械臂轨迹规划的有效性。

     

    Abstract: Aimed at the time-optimal trajectory planning problem of manipulator, in the report, an optimal trajectory planning method based on the improved genetic particle swarm optimization hybrid algorithm was proposed. The motion parameters of a six degree of freedom manipulator were used as the constraint conditions, a cubic uniform B-spline curve was used to construct the trajectory of the robotic arm. A penalty function was introduced to optimize the fitness function to improve the genetic algorithm in joint space. And an adaptive weight factor and Cauchy mutation operator were introduced to improve the particle swarm algorithm, and the improved particle swarm was integrated into the genetic algorithm as a crossover operator. The results showed that the solving accuracy of the improved genetic particle swarm optimization hybrid algorithm is higher than that of genetic algorithm and particle swarm algorithm, and its optimization efficiency has significantly improved compared to a single algorithm, algorithm, which validate the effectiveness of this method applied to trajectory planning of manipulator.

     

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