让吊车自动精准搬砖，解决摆动难题。
Autonomous Block Assembly for Boom Cranes with Passive Joint Dynamics: Integrated Vision MPC Control
- 视觉+路径规划+模型预测控制联动，实时调整吊臂动作。
- 摆动衰减使稳定时间缩短超10倍，实测可自主堆叠与避障。
- 适合建筑自动化、智能施工装备研发人员参考。
本文提出一种用于装配式建筑环境中伸缩臂吊车自主块体装配的控制框架。核心挑战在于被动关节动力学导致类似摆动的晃动，影响构件精准定位。所提方法融合实时视觉姿态估计、碰撞感知B样条路径规划与非线性模型预测控制（NMPC），实现自主抓取、放置及避障装配。在实验室尺度测试平台验证，该平台模拟吊车运动学与被动动力学特性，支持快速实验。碰撞感知规划器在普通CPU上实时生成可行的B样条轨迹，具备任意时间性能；NMPC控制器在持续视觉反馈下主动抑制被动关节摆动并跟踪规划轨迹。实验结果表明，系统可实现自主堆叠与避障装配，摆动抑制使稳定时间相比无控被动动力学减少一个数量级以上，证实了该集成方法在施工自动化中的实时可行性。
原文摘要 · Abstract (English)
This paper presents an autonomous control framework for articulated boom cranes performing prefabricated block assembly in construction environments. The key challenge addressed is precise placement control under passive joint dynamics that cause pendulum-like sway, complicating the accurate positioning of building components. Our integrated approach combines real-time vision-based pose estimation of building blocks, collision-aware B-spline path planning, and nonlinear model predictive control (NMPC) to achieve autonomous pickup, placement, and obstacle-avoidance assembly operations. The framework is validated on a laboratory-scale testbed that emulates crane kinematics and passive dynamics while enabling rapid experimentation. The collision-aware planner generates feasible B-spline references in real-time on CPU hardware with anytime performance, while the NMPC controller actively suppresses passive joint sway and tracks the planned trajectory under continuous vision feedback. Experimental results demonstrate autonomous block stacking and obstacle-avoidance assembly, with sway damping reducing settling times by more than an order of magnitude compared to uncontrolled passive dynamics, confirming the real-time feasibility of the integrated approach for construction automation.
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。