AI Agent-Driven Autonomous Operation of Robotic Tower Cranes

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.