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Trajectory Optimization With Particle Swarm Optimization for Manipulator Motion Planning

机译：粒子群算法在机器人运动规划中的轨迹优化

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Optimization-based methods have been recently proposed to solve motion planning problems with complex constraints. Previous methods have used optimization methods that may converge to a local minimum. In this study, particle swarm optimization (PSO) is proposed for trajectory optimization. PSO is a population-based stochastic global optimization method inspired by group behaviors in wildlife, and has the advantages of simplicity and fast convergence. Trajectory modifications are encoded in particles that are optimized with PSO. The normalized step cost (NSC) concept is used for the initialization of the particles in PSO. A method for reusing previously optimized parameters is also developed. The optimized parameters are stored together with corresponding NSC vectors, and when a constraint violation occurs, the parameters associated with an NSC vector that is similar to the query NSC vector are selected. The selected vector is used for initializing the particles. The reuse of the previously optimized parameters improves the convergence of the PSO in motion planning. The effectiveness of these methods is shown with simulations and an experiment using a three-dimensional problem with constraints. The proposed algorithm successfully optimized a trajectory while satisfying the constraints and is less likely to converge to a local minimum.
机译：最近提出了基于优化的方法来解决具有复杂约束的运动计划问题。先前的方法已使用可能收敛到局部最小值的优化方法。在这项研究中，提出了粒子群优化（PSO）进行轨迹优化。 PSO是一种基于种群的随机全局优化方法，受野生动物群体行为的启发，具有简单和快速收敛的优点。轨迹修改编码在使用PSO优化的粒子中。标准化步骤成本（NSC）概念用于PSO中粒子的初始化。还开发了一种重用先前优化的参数的方法。将优化的参数与相应的NSC向量一起存储，并且当发生约束冲突时，选择与与查询NSC向量相似的NSC向量相关联的参数。选择的矢量用于初始化粒子。先前优化参数的重用改善了运动规划中PSO的收敛性。这些方法的有效性通过仿真和使用带有约束的三维问题的实验得到了证明。所提出的算法在满足约束的同时成功地优化了轨迹，并且不太可能收敛到局部最小值。

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