标题： 用于机械臂运动规划的增量随机与加速梯度信息混合优化 显示英文标题

标题： Incrementally Stochastic and Accelerated Gradient Information mixed Optimization for Manipulator Motion Planning

摘要： 本文介绍了一种新的运动规划器，即混合了增量随机性和加速梯度信息的优化（iSAGO），用于狭窄工作空间中的机械臂。 首先，我们提出了基于惩罚方法的高效约束优化的iSAGO整体方案，该方案受到混合动量的启发。 在随机部分，我们通过基于自适应动量（Adam）方法的子泛函随机选择生成自适应随机动量，以解决肢体-障碍物卡住的情况。 由于随机部分收敛较慢，我们集成了加速梯度下降（AGD）以提高规划效率。 此外，我们采用贝叶斯树推断（BTI）将整个轨迹优化（SAGO）转换为增量子轨迹优化（iSAGO），从而进一步提高计算效率和成功率。 最后，我们调整了关键参数，并在书架上的LBR-iiwa和储物架上的AUBO-i5上与其他5种规划器进行了基准测试。 结果表明，iSAGO具有最高的成功率和适中的求解效率。 显示英文摘要

摘要： This paper introduces a novel motion planner, incrementally stochastic and accelerated gradient information mixed optimization (iSAGO), for robotic manipulators in a narrow workspace. Primarily, we propose the overall scheme of iSAGO informed by the mixed momenta for an efficient constrained optimization based on the penalty method. In the stochastic part, we generate the adaptive stochastic momenta via the random selection of sub-functionals based on the adaptive momentum (Adam) method to solve the body-obstacle stuck case. Due to the slow convergence of the stochastic part, we integrate the accelerated gradient descent (AGD) to improve the planning efficiency. Moreover, we adopt the Bayesian tree inference (BTI) to transform the whole trajectory optimization (SAGO) into an incremental sub-trajectory optimization (iSAGO), which improves the computation efficiency and success rate further. Finally, we tune the key parameters and benchmark iSAGO against the other 5 planners on LBR-iiwa in a bookshelf and AUBO-i5 on a storage shelf. The result shows the highest success rate and moderate solving efficiency of iSAGO.