Redundant manipulators, with their higher Degrees of Freedom (DoFs), offer enhanced kinematic performance and versatility, making them suitable for applications like manufacturing, surgical robotics, and human-robot collaboration. However, motion planning for these manipulators is challenging due to increased DoFs and complex, dynamic environments. While traditional motion planning algorithms struggle with high-dimensional spaces, deep learning-based methods often face instability and inefficiency in complex tasks. This paper introduces RobotDiffuse, a diffusion model-based approach for motion planning in redundant manipulators. By integrating physical constraints with a point cloud encoder and replacing the U-Net structure with an encoder-only transformer, RobotDiffuse improves the model's ability to capture temporal dependencies and generate smoother, more coherent motion plans. We validate the approach using a complex simulator and release a new dataset, Robot-obtalcles-panda (ROP), with 35M robot poses and 0.14M obstacle avoidance scenarios. The highest overall score obtained in the experiment demonstrates the effectiveness of RobotDiffuse and the promise of diffusion models for motion planning tasks. The dataset can be accessed at https://github.com/ACRoboT-buaa/RobotDiffuse.

翻译：冗余机械臂凭借其更高的自由度（DoFs），提供了增强的运动学性能和多功能性，使其适用于制造、手术机器人及人机协作等应用。然而，由于自由度增加以及复杂动态环境的存在，这类机械臂的运动规划具有挑战性。传统运动规划算法难以应对高维空间，而基于深度学习的方法在复杂任务中常面临不稳定和效率低下的问题。本文提出RobotDiffuse，一种基于扩散模型的冗余机械臂运动规划方法。通过将物理约束与点云编码器相结合，并用仅含编码器的Transformer结构替代U-Net，RobotDiffuse提升了模型捕捉时间依赖性的能力，并能生成更平滑、更连贯的运动轨迹。我们在复杂仿真环境中验证了该方法，并发布了一个包含3500万机器人位姿和14万个避障场景的新数据集——Robot-obstacles-panda（ROP）。实验获得的最高综合评分证明了RobotDiffuse的有效性，以及扩散模型在运动规划任务中的潜力。该数据集可通过 https://github.com/ACRoboT-buaa/RobotDiffuse 访问。