Constrained motion planning is a common but challenging problem in robotic manipulation. In recent years, data-driven constrained motion planning algorithms have shown impressive planning speed and success rate. Among them, the latent motion method based on manifold approximation is the most efficient planning algorithm. Due to errors in manifold approximation and the difficulty in accurately identifying collision conflicts within the latent space, time-consuming path validity checks and path replanning are required. In this paper, we propose a method that trains a neural network to predict the minimum distance between the robot and obstacles using latent vectors as inputs. The learned distance gradient is then used to calculate the direction of movement in the latent space to move the robot away from obstacles. Based on this, a local path optimization algorithm in the latent space is proposed, and it is integrated with the path validity checking process to reduce the time of replanning. The proposed method is compared with state-of-the-art algorithms in multiple planning scenarios, demonstrating the fastest planning speed

翻译：约束运动规划是机器人操作中常见但具有挑战性的问题。近年来，数据驱动的约束运动规划算法展现出令人印象深刻的规划速度与成功率。其中，基于流形近似的潜在运动方法是最为高效的规划算法。由于流形近似存在误差，且在潜在空间中难以精确识别碰撞冲突，需要耗时的路径有效性检查与路径重规划。本文提出一种方法，通过训练神经网络以潜在向量作为输入来预测机器人与障碍物的最小距离。随后利用学习得到的距离梯度计算潜在空间中的运动方向，使机器人远离障碍物。在此基础上，提出一种潜在空间局部路径优化算法，并将其与路径有效性检查流程相结合，以减少重规划时间。所提方法在多种规划场景中与前沿算法进行比较，展现出最快的规划速度。