PccDiffuser：连续体机器人的多解运动规划
PccDiffuser: Multi-solution Motion Planning for Continuum Robots
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中文总结 AI 辅助
提出PccDiffuser条件扩散框架，学习连续体机器人配置空间路径的多模态分布，并行采样多解并考虑执行器约束，结合图神经网络和解析运动学，在混合测试集上达到91%成功率，优于现有基准并实现全身避障。
中文摘要 AI 辅助
我们提出了PccDiffuser，一个用于连续体机器人的条件扩散框架，该框架学习完整配置空间路径上的多模态分布，并并行采样多个候选解，随后通过考虑执行器约束的时间分配将这些候选解转换为可执行的轨迹。在分段常曲率模型下，我们使用指数坐标描述机器人运动学，并使用图神经网络编码可变数量的环境障碍物。在去噪过程中融入了解析微分运动学，以提高末端精度和全身避障能力。在一个包含零到四个障碍物的工作空间的混合测试集上，PccDiffuser达到了91%的成功率。与现有的基于采样和优化的基准相比，它同时实现了更高的成功率和更高的计算效率，且当采样更多候选解时，后者的优势更为显著。在三段腱驱动连续体机器人上的实验进一步展示了连续规划、多解规划和全身避障能力。
英文摘要
We present the PccDiffuser, a conditional diffusion framework for continuum robots that learns a multimodal distribution over complete configuration-space paths and samples multiple candidate solutions in parallel, which are subsequently converted into an executable trajectory by time allocation considering actuator constraints. Under the piecewise constant-curvature model, we use exponential co-ordinates to describe the robot kinematics, and use graph neural network to encode a variable number of environment obstacles. Analytical differential kinematics is incorporated in the denoising process to improve terminal accuracy and whole-body clearance. On a mixed test set comprising workspace with zero to four obstacles, PccDiffuser achieved a success rate of 91\%. Compared with existing sampling- and optimisation-based benchmarks, it delivered both a higher success rate and greater computational efficiency, with the latter advantage becoming more substantial when sampling more candidate solutions. Experiments on a three-section tendon-driven continuum robot further demonstrate consecutive planning, multi-solution planning, and whole-body obstacle avoidance.
发表机构
- Zhejiang University（浙江大学）
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