Work

AI Agent-Driven Autonomous Operation of Robotic Tower Cranes

Year: 2026

Type: article

Abstract: Tower cranes are indispensable to building construction, but routine lifting still depends on the coordinated judgement of crane operators and signalpersons, which constrains automation and introduces human-factor variability. This study develops and formalizes an AI agent-driven framework for Level-4 (L4) robotic tower cranes with human supervision. The framework development comprises formal definitions of the digital signalperson agent and digital crane operator agent, a two-phase task–commit protocol, a supervisory finite-state machine, and a maximum-risk decision structure for task acceptance, degraded operation, minimum-risk stopping, and human takeover. Experimental validation was intentionally limited to three operational interfaces: terminal localization, autonomous positioning, and takeover-command latency. Under the tested site conditions, the terminal-localization error had a mean of 3.94 cm and a 90th percentile of 7.11 cm; the autonomous-positioning error averaged 48.2 cm, with 90% of observations within 80 cm; and the takeover-command latency remained below 500 ms, with a maximum of 458 ms. These measurements support the engineering feasibility of the tested supervised-autonomy interfaces, but they do not constitute full validation of the complete L4 safety case, including dynamic risk estimation, obstacle avoidance, adverse-weather operation, blind lifting, or multi-crane coordination.

Source: Buildings

DOI: 10.3390/buildings16173492

Authors: Yinhao Song, Junpeng Tian, Shanxu Liu, Hanbin Luo, Lieyun Ding

Institution: Huazhong University of Science and Technology

Language: English

Topic: Robot Manipulation and Learning

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,137