发表机构
Georgia Institute of Technology（佐治亚理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出CAST框架，利用单一因子图同时进行轨迹估计与规划，处理施工多机器人系统中的碰撞避免和缆绳约束，并在柱梁结构建造中验证了其有效性。
AI 中文摘要
多机器人系统在施工中日益显示出可行性，因为它们能够执行高精度操作，同时减少人类暴露于危险任务的风险。然而，这些环境具有高维配置空间，并包含大量碰撞避免约束，这些约束涉及其他机器人、装配物体和工作空间边界。我们利用一个单一因子图进行轨迹估计与规划，该因子图融合了测量的机器人状态以及显式碰撞约束和学习到的缆绳约束。这支持工作空间的变更，并能在考虑重型机器人系统的刚性、振动引起的不确定性的同时，实现同步的高维机器人运动规划。我们通过使用一个机器人手臂作为木材夹持器，另一个机器人作为钉子紧固器，在柱梁结构的施工中展示了我们框架的成功。
英文摘要
Multi-robot systems have shown increasing viability in construction due to their ability to execute high-precision actions while reducing human exposure to hazardous tasks. However, these environments have high-dimensional configuration spaces and possess substantial collision-avoidance constraints, which include other robots, assembly objects, and workspace boundaries. We utilize a single factor graph for trajectory estimation and planning that incorporates measured robot states together with explicit collision and learned cable constraints. This supports changing workspaces and enables synchronized, high-dimensional robot motion planning while accounting for the stiff, vibration-induced uncertainty of heavy robotic systems. We demonstrate the success of our framework on the construction of a post-and-lintel structure using one robot arm as a timber gripper, and a second robot as a nail-fastener.
