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

TL;DR

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.

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.

Figures (9)

• Figure 1: Motivation: articulated boom crane with concrete block (left) and developed laboratory-scale setup (right) as a testbed for autonomous assembly.
• Figure 2: System overview. Left: schematic of the laboratory setup with robot, passive joint, gripper, blocks, and reference frames ${0}$ (base), $\mathcal{E}$ (end-effector), $\mathcal{T}$ (tool center), and $\mathcal{C}$ (camera). Right: block diagram of the integrated framework, illustrating the interconnection of perception, path planning, MPC control, and the environment model. The task sequencer executes pickup and placement routines based on a configuration file specifying the schedule.
• Figure 3: Evolution of the stochastic ensemble (15 candidates) with an obstacle wall of three stacked blocks. Candidates start with wide variability (\ref{['fig:ensemble_init']}) and contract toward a feasible collision-free region (\ref{['fig:ensemble_iter10']}).
• Figure 4: Block pickup and placement. In (\ref{['fig:exp_pickup']}), a randomly placed block is grasped using vision-based pose estimation feedback. In (\ref{['fig:exp_placement']}), the block is placed on top of an existing block at a predefined goal, consistent with nominal assembly targets.
• Figure 5: Reference tracking and error during block pickup and placement (see \ref{['fig:experiment_pickup_placement']}). Vertical dashed lines indicate replanning events (new path initialization), while dotted lines mark vision updates (goal corrections from pose estimation). From top to bottom: Cartesian position $\boldsymbol{p}$, yaw angle $\psi$, Cartesian position errors $\tilde{ \boldsymbol{p} }$ and yaw angle error $\tilde{\psi}$.