用图像直接规划机械臂路径，无需模型或传感器。
Image-Based Roadmaps for Vision-Only Planning and Control of Robotic Manipulators
- 在图像空间构建道路图，基于视觉特征采样与避障。
- 学习距离度量的路径规划成功率达100%，预设度量响应更快但成功率较低。
- 适合无模型、无编码器的视觉控制场景，如移动机器人避障。
本文提出一种仅依赖视觉的机械臂运动规划框架，直接在图像空间生成无碰撞路径，并通过视觉反馈控制实现跟踪，无需显式机器人模型或本体感知。核心在于完全在图像空间构建道路图，采样、近邻选择和碰撞检测均基于视觉特征而非几何模型。首先通过在工作空间内移动机械臂，采集不同构型下的身体关键点图像作为道路图节点；随后使用学习或预定义的距离度量构建道路图。运行时，直接在图像空间生成无碰撞路径，无需机器人模型或关节编码器。实验验证表明，采用学习距离度量的道路图路径控制收敛成功率达100%；而预定义距离度量虽响应更迅速，但收敛成功率较低。
原文摘要 · Abstract (English)
This work presents a motion planning framework for robotic manipulators that computes collision-free paths directly in image space. The generated paths can then be tracked using vision-based control, eliminating the need for an explicit robot model or proprioceptive sensing. At the core of our approach is the construction of a roadmap entirely in image space. To achieve this, we explicitly define sampling, nearest-neighbor selection, and collision checking based on visual features rather than geometric models. We first collect a set of image-space samples by moving the robot within its workspace, capturing keypoints along its body at different configurations. These samples serve as nodes in the roadmap, which we construct using either learned or predefined distance metrics. At runtime, the roadmap generates collision-free paths directly in image space, removing the need for a robot model or joint encoders. We validate our approach through an experimental study in which a robotic arm follows planned paths using an adaptive vision-based control scheme to avoid obstacles. The results show that paths generated with the learned-distance roadmap achieved 100% success in control convergence, whereas the predefined image-space distance roadmap enabled faster transient responses but had a lower success rate in convergence.
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