用神经网络建模连续体机器人形状，实现高效避障运动规划。
Neural Configuration Distance Function for Continuum Robot Control
- 通过学习各段距离场并融合运动学链，构建神经距离函数。
- 在有动态障碍的复杂环境中实现快速碰撞检测与安全路径生成。
- 适合多节连续体机器人在点云观测下的实时避障控制。
本文提出一种新型方法，将连续体机器人的形状建模为神经配置欧氏距离函数（N-CEDF）。通过为每一段独立学习距离场，并借助运动学链进行组合，所学习的N-CEDF能够提供精确且计算高效的机器人形状表示。距离函数表示的关键优势在于，即使在动态和杂乱环境中，也能实现基于点云观测的高效碰撞检查，适用于运动规划。我们将N-CEDF集成到模型预测路径积分（MPPI）控制器中，为多段连续体机器人生成安全轨迹。该方法在包含静态与动态障碍物的多个模拟环境中，对不同段数的连续体机器人进行了验证。
原文摘要 · Abstract (English)
This paper presents a novel method for modeling the shape of a continuum robot as a Neural Configuration Euclidean Distance Function (N-CEDF). By learning separate distance fields for each link and combining them through the kinematics chain, the learned N-CEDF provides an accurate and computationally efficient representation of the robot's shape. The key advantage of a distance function representation of a continuum robot is that it enables efficient collision checking for motion planning in dynamic and cluttered environments, even with point-cloud observations. We integrate the N-CEDF into a Model Predictive Path Integral (MPPI) controller to generate safe trajectories for multi-segment continuum robots. The proposed approach is validated for continuum robots with various links in several simulated environments with static and dynamic obstacles.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。