Construction Robots–outdated or on the way?


Construction robots have become indispensable in various construction tasks, such as heavy component hoisting, laying, and installation. These robots are classified into two main categories: underactuated and full-actuated systems, each presenting unique challenges and requiring specialized control strategies. In this editorial, we delve into the complexities of underactuated robots, with a focus on their nonholonomic constraints and associated control methods. Additionally, we explore the benefits of full-actuated control systems and their applications within the construction industry. Underactuated construction robots, such as hoisting cranes, are characterized by nonholonomic constraints, making direct control of certain state variables challenging. This constraint, coupled with dynamic coupling complexities and uncertain configurations in state space, poses significant challenges for researchers and engineers. Tower cranes, gantry cranes, and bridge cranes are prime examples of underactuated systems commonly used in construction. To tackle these challenges, researchers have developed various control strategies for underactuated construction robots: firstly, the energy (Passive) Control Method where energy shaping control addresses actuator saturation, enabling precise positioning of lifting cranes while minimizing load swing. Coupling signals and antisway controllers, designed based on passivity analysis, enhance the relationships between system variables and improve robustness. Enhanced coupling control methods have been derived for 3D underactuated bridge cranes, significantly enhancing control performance. Secondly, motion planning method which involves designing controllers based on predefined reference trajectories. Offline trajectory planning and input-shaping methods are commonly employed to address complex coupling between crane motions. Real-time modified trajectory planning methods improve control performance and robustness.

In a recently published paper in the peer-reviewed  Journal of Field Robotics  titled “A review for control theory and condition monitoring on construction robots,” led by Huaitao Shi, Xiaotian Bai, Yixing Zhang, Linggang Min, Dong Wang, Xinyu Lu, Yang Yan from Shenyang Jianzhu University in collaboration with Ranran Li from Northeastern University and Yaguo Lei from Xi’an Jiaotong University, the authors discussed the control theory and condition monitoring of engineering robots. They emphasized the importance of control methods based on driving models and state monitoring in fault detection, intelligent maintenance, and fault tolerance control. This comprehensive review provided fresh perspectives and insights for future studies in Construction Robots (CR)  control models and state monitoring, contributing to the body of knowledge in construction industrialization and construction robots.

The authors discussed the Optimal Control Method and mentioned in their paper that linear models are often employed in motion planning, with optimal control algorithms introduced to handle disturbances and enhance system robustness. Pseudospectral time-optimal motion planning methods consider load swing suppression and transportation efficiency. Optimal control methods effectively restrain load swing during transmission, resulting in improved control effectiveness. On the other hand Intelligent Control Method, in their expert opinion fuzzy control, neural networks, and genetic algorithms are utilized to develop intelligent control strategies. These methods eliminate the need for precise system models, enhancing speed response and synchronization control. Challenges include theoretical stability proof and simulation-based validation, especially in the face of changing system parameters. Combining multiple control methods is a common approach to meet the increasing complexity of underactuated systems and their control requirements.

The researchers also discussed the Full-Actuated Control Systems in Construction Robots and they are distinguished by their high-order, nonlinear, and mechanically complex systems with multiple degrees of freedom. These systems offer precise control and find applications in various construction tasks, such as wall-laying robots, power-assisted robots for gypsum board installation, and floor tile laying robots.

According to the authors, control methods commonly used in full-actuated construction robots include PID-Based Control where Proportional-Integral-Derivative (PID) control ensures high-speed and stable control, guaranteeing robustness and reliability. Secondly Machine Vision (MV)-Based Control where MV systems aid in defect identification, object detection, and sorting, contributing to automation in construction. Both position-based and image-based visual servo systems control robot movements. Thirdly, Multisensor Information Fusion which combines cameras, sensors, and computing power to interpret images and make informed decisions. It is crucial for tasks involving complex and uncertain working environments.

Control strategies for construction robots vary depending on their actuation type. Underactuated robots contend with nonholonomic constraints, intricate dynamics, and uncertainties, necessitating energy-based, motion planning, optimal, and intelligent control methods for effective operation. In contrast, full-actuated systems offer precise control and find applications in various construction tasks, commonly employing PID-based control, machine vision, and multisensor information fusion. As construction robots continue to evolve and tackle more complex tasks, the integration of these control strategies and the development of new ones will play a pivotal role in enhancing their performance, reliability, and efficiency within the construction industry.

The construction industry has witnessed a gradual shift from traditional on-site construction to industrialization and off-site construction methods over the past decade. The authors believe advancements in construction industrialization technology have led to increased productivity and environmental sustainability, particularly in complex construction sites. This trend is expected to persist, despite challenges related to skill variations and collaboration issues across different construction processes. In this context, CR have emerged as a transformative solution in the high-tech sector, addressing skill disparities and cost challenges. CR has revolutionized construction by improving production efficiency, reducing costs, and enhancing worker safety.

In summary, the authors offered an excellent comprehensive overview of CR technology, its development trajectory, and the challenges it faces. It underscores the ongoing refinement of CR’s actuator control, position compensation, and state monitoring methods as crucial for advancing its applicability in construction industrialization, ultimately enhancing its benefits for the industry’s evolution.

Construction Robots--outdated or on the way? - Advances in Engineering Construction Robots--outdated or on the way? - Advances in Engineering Construction Robots--outdated or on the way? - Advances in Engineering

About the author

Huaitao Shi was born in Fuyang, Anhui Province, China in 1982. He received the B.S. degree in control engineering from Northeastern University, Shenyang, Liaoning in 2001, and M.S. and PhD degrees in control engineering from Northeastern University, Shenyang, Liaoning in 2005 and 2012.

Prof.Shi has been a professor in faculty of Mechanical Engineering, Shenyang Jianzhu University since 2013. From 2014 to 2022, he served as the Vice Dean of the School of Mechanical Engineering at Shenyang Jianzhu University, and has been the Executive Vice Dean since 2023. He was also the vice chairman of the Liaoning Society of Vibration Engineering.

Professor Shi has been awarded honors such as the National “Ten Thousand Talents Plan” Youth Top Talents of the Central Organization Department, the “Xingliao Talent” Youth Top Talents of Liaoning Province, and the “Hundred Thousand Talents Project” Hundred Talents Level of Liaoning Province. He was the recipient of the Liaoning Science and Technology Award and the Liaoning Natural Science Achievement Award, and was one of the participants of the prize for scientific and technological progress given by the Ministry of Education. He is the author of over 90 articles, and 19 patents. His current research interests include Mechanical system fault diagnosis and intelligent operation and maintenance, industrial robot intelligent control and operation and maintenance. His research findings has been applied in several bearing enterprises, and great economic and social benefits were created thereby.

About the author

Xiaotian Bai was born in Fushun, Liaoning, China in 1989. He received the B.S. degree in School of Mechanical Engineering from Dalian University of Technology in 2011, and got the PhD degree in Shenyang University of Technology in 2016.

Prof. Bai has been an associate professor in faculty of Mechanical Engineering, Shenyang Jianzhu University since 2019. He carried out his postdoctoral work in Shenyang Jianzhu University from 2016 to 2018, and worked as a visiting scholar in Transilvania University of Brasov for 3 months to help develop the international joint laboratory.

Prof. Bai was awarded the Youth Science and Technology award of Liaoning Province in 2023, and was also the recipient of the Science and Technology Progress Award given by the China Society of Mechanical Engineering. Prof. Bai was the deputy secretary-general and managing director of Liaoning Society of Vibration Engineering, and has been the reviewer for Journal of Sound and Vibration and Mechanical Systems Signal Processing since 2019.

Prof. Bai’s current research interest includes vibration and sound radiation of rotary systems. So far he has been the author of over 30 papers and 10 patents. His findings were widely cited by scholars with relative topics. He also took part in the diagnosis and maintenance of rotary machines, and the effects proved to be satisfactory.

About the author

Ranran Li received the B.S. degree in mechanical engineering and automation and the Ph.D. degree in control theory and control engineering from Northeastern University, Shenyang, China, in 2005 and 2012, respectively. During 2008 and 2010, he was a visiting student in the department of Electrical and Computer Engineering of Michigan State University, USA, funded by the Chinese Scholarship Council. From 2013 to 2015, he worked as a Postdoctoral Fellow with The Chinese University of Hong Kong, China and Hong Kong University of Science and Technology, China.

He is currently an Associate Professor and a Ph.D supervisor with the College of Information Science and Engineering in Northeastern University, Shenyang, China. As first author or corresponding author, he has published more than 20 SCI papers in world renowned journals. His current research interests include distributed convex and nonconvex optimization, stochastic approximation, machine learning, and their applications in robotics and power systems.

About the author

Yaguo Lei received the B.S. and Ph.D. degrees in mechanical engineering from Xi’an Jiaotong University, P.R. China, in 2002 and 2007, respectively. He is currently a Full Professor of mechanical engineering at Xi’an Jiaotong University. Prior to joining Xi’an Jiaotong University in 2010, he was a Postdoctoral Research Fellow with the University of Alberta, Canada. He was also an Alexander von Humboldt Fellow with the University of Duisburg-Essen, Germany. His research interests intelligent fault diagnosis and remaining useful life prediction.

Prof. Lei is a Fellow of ASME, IET, and ISEAM. He is currently an Associate Editor or Editorial Board member of more than ten journals, including IEEE Transactions on Industrial Electronics and Mechanical Systems and Signal Processing.


Huaitao Shi, Ranran Li, Xiaotian Bai, Yixing Zhang, Linggang Min, Dong Wang, Xinyu Lu, Yang Yan, Yaguo Lei. (2023)  A review for control theory and condition monitoring on construction robots. Journal of Field Robotics Volume 40, Issue4, Pages 934-954 

Go to Journal of Field Robotics

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