Robotic Tower Crane Modeling and Control (RTCMC) with LQR-DRO and LQR-LEIC for Linear and Nonlinear Payload Swing Minimization

Win, Thein Moe; Hesketh, T.; Eaton, Raymond

doi:10.15866/ireaco.v9i2.8431

Cited by 2 publications

(1 citation statement)

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“…According to world statistics, 23% of crane accidents are caused by wind interference and 38% are operational errors [2]. Using the method of system thinking and case analysis, Zhang [11] confirmed that the key causes of tower crane accidents include insufficient safety inspection and other factors, and put forward a series of suggestions on the improvement of tower crane safety management and accident prevention. Sadeghi [12] produced a comprehensive analysis of the main crane risk factors studied in the existing literature, pointed out eight risk factors that scholars paid limited or no attention to, and found that the risk factors related to crane equipment are a hotspot, since they are involved in half of the research studies.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Tower Crane Safety: A UAV-Based Intelligent Inspection Approach

Jiao,

Wu,

Zhang

et al. 2024

Buildings

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Tower cranes play a crucial role in the construction industry, facilitating the vertical and horizontal movement of materials and aiding in building construction, especially for high-rise structures. However, tower crane accidents can lead to severe consequences, highlighting the importance of effective safety management and inspection. This paper presents a novel approach to tower crane safety inspections using Unmanned Aerial Vehicles (UAVs) equipped with high-definition cameras and an intelligent inspection APP system. The system utilizes real-time kinematic (RTK) positioning and digital image processing to perform efficient and comprehensive inspections, reducing the reliance on manual labor and associated risks. A case study demonstrated the method’s practicality and effectiveness, with the UAV inspections capable of identifying 76.3% of major hazards, 64.8% of significant hazards, and 76.2% of general hazards within a 30-minute timeframe. Preliminary identification rates were also promising. Despite the initial requirement for manual drone piloting and the current manual review of images, the approach shows significant potential for enhancing safety in the construction industry. Future work will focus on integrating AI for hazard recognition and automating the inspection process further. The proposed method marks a step forward in tower crane safety management, offering a more efficient and accurate alternative to traditional inspection methods.

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Section: Introductionmentioning

confidence: 99%