AI and Robotics to Transform Industrialised Building Manufacturing
Industrialised building manufacturing (IBM) has been a game-changer in recent years, shifting construction away from traditional labour-intensive methods to a model that thrives on prefabrication, off-site assembly, and on-site installation.
The appeal is obvious: faster builds, better cost control, and more efficient use of resources. Yet, despite its promise, IBM is still grappling with bottlenecks. Inconsistent technical standards, underutilised factory space, and a shortage of skilled labour have left room for improvement. This is where Artificial Intelligence (AI) and robotics are stepping in to rewrite the rulebook.
A recent study entitled AI-based robots in industrialised building manufacturing, conducted by researchers Mengjun Wang, Jiannan Cai, Da Hu, Yuqing Hu, Zhu Han, and Shuai Li from institutions such as the University of Tennessee, the University of Texas at San Antonio, and Kennesaw State University, dives into exactly how AI-driven robotics can address these pain points. Published in Frontiers of Engineering Management in 2025, their review analyses research spanning a decade and provides a framework for how AI-powered robots are reshaping IBM.
Four pillars of AI-robotic integration
The study breaks down the role of AI-based robots (AIRs) into four interconnected modules: Cognition, Communication, Control, and Collaboration & Coordination. These pillars underpin how robotics can not only automate tasks but also integrate seamlessly into complex, multi-stage building processes.
Cognition: Perception and decision-making: At the heart of any robotic system is the ability to perceive and understand its environment. The cognition module explores how sensors, machine vision, and AI algorithms allow robots to detect materials, recognise components, and even interpret human gestures or safety signals. This is crucial in IBM, where precision is non-negotiable.
The review points out that advances in machine learning now enable robots to handle variable conditions in factories, from imperfect materials to unpredictable human interaction. “Environmental perception and human-related cognition are key to enabling robots to operate effectively in dynamic assembly lines” the authors note.
Communication: Speaking the same language: While robots can perceive, they also need to communicate. In IBM, this goes beyond machine-to-machine chatter. It involves standardised fieldbus systems, wireless networks, and increasingly intuitive human-robot interfaces.
The study highlights how improvements in communication protocols are helping robots coordinate seamlessly with central control systems, while also making collaboration with human workers more natural. Gesture recognition, voice commands, and augmented reality interfaces are being explored to bridge the gap between operator and machine.
Control: From precision to adaptability: Control systems are what allow robots to move with accuracy and adapt when conditions change. The authors categorise control into two levels: low-level adaptive control for handling precision assembly and high-level planning for motion and pathfinding.
In practice, this means a robotic arm can place heavy panels with millimetre accuracy, while an autonomous guided vehicle (AGV) can adjust its route in real time if another machine crosses its path. This kind of flexibility is central to scaling up industrialised building.
Collaboration & coordination: Humans and robots side by side: Perhaps the most transformative module is collaboration. IBM processes often require teams of machines and people to work together. From multi-robot coordination frameworks to hybrid assembly teams where humans handle complex decisions and robots take care of repetitive tasks, collaboration ensures the line keeps moving efficiently.
“Different levels of human-robot collaboration are vital for improving productivity while maintaining safety” the researchers explain. As factories embrace Industry 4.0, balancing human creativity with robotic precision is proving to be the sweet spot.
Applications across the IBM process
The review goes beyond theory, mapping AIRs to the five stages of IBM: component preparation, sub-assembly, main assembly, finishing tasks, and quality control.
- Component preparation: AI-driven robots can cut, weld, and shape prefabricated components faster and with greater accuracy than human labour alone. Sensors reduce waste by ensuring materials are used efficiently.
- Sub-assembly: Automated systems streamline repetitive tasks such as fastening, drilling, or basic joining, freeing up skilled workers for oversight.
- Main assembly: Robotic cranes and AGVs are already being deployed to move large prefabricated elements. AI improves path optimisation, reducing downtime.
- Finishing tasks: From automated painting to sealant application, AIRs are bringing consistency to the finishing stages, where human error traditionally creates costly defects.
- Quality control: Machine vision systems, powered by AI, are raising the bar for quality assurance by detecting micro-defects invisible to the human eye.
The challenges holding back adoption
Despite the promise, there are obstacles. Industrialised building is still fragmented, with inconsistent standards across regions and limited cross-industry collaboration. Many factories struggle with underutilised space, making the integration of large robotic systems a logistical headache.
The skills gap also looms large. Operating and maintaining advanced robotics requires a new breed of technician – one that blends construction knowledge with coding and AI expertise. Without targeted training programmes, the industry risks bottlenecks that could undermine the benefits of automation.
Where research is heading
The study identifies several key areas for future exploration. One is the development of unified technical guidance for AI-robot integration, something that would standardise deployment and reduce costs. Another is advancing collaborative frameworks that allow robots and humans to work together more intuitively.
Emerging technologies such as digital twins and generative AI also hold promise. By creating virtual replicas of factories and assembly lines, researchers can test AI-robot workflows before they’re deployed in real-world settings, cutting down trial-and-error costs.
There’s also growing interest in integrating sustainability into the AI-robot equation. Smarter material usage, predictive maintenance, and energy-efficient robots could help IBM deliver not only faster and cheaper builds but greener ones too.
A sector on the cusp of transformation
The integration of AI and robotics into industrialised building manufacturing isn’t just about automation. It’s about reshaping how we think of construction. As robots learn to perceive, communicate, and collaborate, factories are evolving into smart ecosystems where human and machine strengths complement one another.
The paper by Wang, Cai, Hu, Han, and Li makes it clear that the construction sector is standing on the brink of a revolution. With research accelerating and pilot projects already showing tangible benefits, the shift to AI-powered IBM is no longer a question of if but when.
