This paper introduces a novel motion planning algorithm, incrementally stochastic and accelerated gradient information mixed optimization (iSAGO), for robotic manipulators in a narrow workspace. Primarily, we propose the overall scheme of iSAGO integrating the accelerated and stochastic gradient information for an efficient constrained optimization based on the penalty method. In the stochastic part, we generate the adaptive stochastic moment via the random selection of collision checkboxes, interval time-series, and penalty factor 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. Finally, we tune the key parameters and benchmark iSAGO against other planners (CHOMP, GPMP2, TrajOpt, STOMP, and RRT-Connect) on LBR-iiwa in a bookshelf and AUBO-i5 in a storage shelf. The result shows the highest success rate and moderate solving efficiency of iSAGO.

翻译：本文介绍了一个新的运动规划算法(iSAGO),该算法是针对狭小工作空间的机器人操纵者的一种渐进式和加速梯度信息混合优化(iSAGO),主要建议采用iSAGO将加速和随机梯度信息整合为基于罚款方法的高效限制优化的总体计划。在随机选择碰撞检查箱、间隔时间序列和惩罚因素(iSAGO),通过随机选择碰撞检查箱、间隔时间序列和随机选择,产生适应性随机随机随机随机随机随机随机随机随机随机随机随机随机随机随机的随机随机随机随机随机随机随机随机随机随机随机随机随机随机随机随机随机随机随机随机随机随机随机随机随机随机随机随机随机随机随机抽查,根据适应性动力(Adam)方法解决身体障碍卡住案件。由于加速梯度整合部分的缓慢,我们整合加速梯度下降(AGD)以提高规划效率。此外,我们采用巴伊西亚树的推断(BTI)将整个轨迹优化(SA)转变为子轨迹优化(iSA)改进计算效率和成功率。