Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Deep learning technology for construction machinery and robotics
Abstract Construction machinery and robots are essential equipment for major infrastructure. The application of deep learning technology can improve the construction quality and alleviate the shortage of skilled workers, which is important for the future development of the construction industry. Deep learning has been recognized for its robustness in autonomous systems. A gap has been identified in the systematic analysis for deep learning-based technology for construction machinery and robotics applied in autonomous construction. To fill this gap, we conducted a systematic review from different perspectives, including: (1) perception; (2) navigation and planning; (3) control; and (4) human-robot interaction. On the basis of a systematic analysis, we identified the challenges applied to practice: (1) dataset limitation; (2) lack of interpretability; and (3) insufficient autonomous intelligence. Potential solutions and future outlook are as follows: (1) datasets with expert knowledge; (2) trustworthy artificial intelligence; (3) generative deep learning; and (4) extraterrestrial construction.
Highlights Research related to deep learning technology for construction machinery and robotics is critically reviewed and analyzed qualitatively. The mapping of knowledge domains is drawn by combining keyword co-concurrence and cluster-based topic analysis. Challenges applied to practice are analyzed for autonomous control of construction machinery and robotics. Future research directions and knowledge gap for high-level autonomous construction are discussed.
Deep learning technology for construction machinery and robotics
Abstract Construction machinery and robots are essential equipment for major infrastructure. The application of deep learning technology can improve the construction quality and alleviate the shortage of skilled workers, which is important for the future development of the construction industry. Deep learning has been recognized for its robustness in autonomous systems. A gap has been identified in the systematic analysis for deep learning-based technology for construction machinery and robotics applied in autonomous construction. To fill this gap, we conducted a systematic review from different perspectives, including: (1) perception; (2) navigation and planning; (3) control; and (4) human-robot interaction. On the basis of a systematic analysis, we identified the challenges applied to practice: (1) dataset limitation; (2) lack of interpretability; and (3) insufficient autonomous intelligence. Potential solutions and future outlook are as follows: (1) datasets with expert knowledge; (2) trustworthy artificial intelligence; (3) generative deep learning; and (4) extraterrestrial construction.
Highlights Research related to deep learning technology for construction machinery and robotics is critically reviewed and analyzed qualitatively. The mapping of knowledge domains is drawn by combining keyword co-concurrence and cluster-based topic analysis. Challenges applied to practice are analyzed for autonomous control of construction machinery and robotics. Future research directions and knowledge gap for high-level autonomous construction are discussed.
Deep learning technology for construction machinery and robotics
You, Ke (Autor:in) / Zhou, Cheng (Autor:in) / Ding, Lieyun (Autor:in)
21.03.2023
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
| Europäisches Patentamt | 2018
| British Library Online Contents | 2006