Autonomous Block Assembly for Boom Cranes with Passive Joint Dynamics: Integrated Vision MPC Control

TL;DR

This paper introduces an integrated vision-based MPC control system for autonomous crane block assembly, effectively managing passive joint sway and obstacle avoidance in real-time, validated on a laboratory testbed.

Contribution

It presents a novel combined approach of vision-based pose estimation, collision-aware path planning, and nonlinear MPC for autonomous crane assembly with passive dynamics.

Findings

Sway damping reduces settling times by over tenfold.

Real-time collision-aware B-spline path planning is feasible on CPU.

The system successfully performs autonomous block stacking and obstacle avoidance.

Abstract

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.