Construction robotics最新文献

Introduction: Robots have increased productivity, quality, and safety in structured manufacturing environments while lowering production costs. In the last decade, advances in computing and sensing have started to enable robots in unstructured environments such as construction.

Objectives: Given this new reality, this research aims to quantify the impacts of existing construction robots.

Methods: This study evaluates the Safety, Quality, Schedule, and Cost impacts of ten on-site construction robots for 12 construction projects spanning 11 contractors from Europe, Asia, South America, and the United States.

Results: The robots showed the potential to reduce repetitive site work between 25 and 90% and reduce time spent on hazardous tasks by 72% on average. On average, accuracy was improved by 55%, and rework was reduced by over 50%. Robots reduced the schedule on average 2.3 times with a median of 1.4x. The cost was reduced by 13%, with six cases that reduced it but four that increased the total costs. The comparative results also highlight under what project conditions (Product, Organization, and Process) could the robot perform better than the traditional method.

Conclusion: Even at this relatively early stage of robot deployment worldwide, the consistent evaluation of ten examples showed how promising the technology already is for a range of robot types, mobility, autonomy, scale, business models, and locations. Future work will expand the number of robot case studies utilizing the same comparison method.

This research investigates robotically fabricated polychromatic float glass for architectural applications. Polychromatic glass elements usually require labor-intensive processes or are limited to film applications of secondary materials onto the glass. Previous research employs computer numerical control (CNC) based multi-channel granule deposition to manufacture polychromatic relief glass; however, it is limited in motion, channel control, and design space. To expand the design and fabrication space for the manufacture of mono-material polychromatic glass elements, this paper presents further advancements using a UR robotic arm with an advanced multi-channel dispenser, linear and curved-paths granule deposition, customized color pattern design approaches, and a computational tool for the prediction and rendering of outcomes. A large-scale demonstrator serves as a case study for upscaling. Robotic multi-channel deposition and tailored computational design tools are employed to facilitate a full-scale installation consisting of eighteen large glass panels. Novel optical properties include locally varying color, opacity, and texture filter light and view. The resulting product constructs sublime architectural experiences through light refraction, reflection, color, opacity - beyond mere transparency.

The objective of autonomous robotic additive manufacturing for construction in the architectural scale is currently being investigated in parts both within the research communities of computational design and robotic fabrication (CDRF) and deep reinforcement learning (DRL) in robotics. The presented study summarizes the relevant state of the art in both research areas and lays out how their respective accomplishments can be combined to achieve higher degrees of autonomy in robotic construction within the Architecture, Engineering and Construction (AEC) industry. A distributed control and communication infrastructure for agent training and task execution is presented, that leverages the potentials of combining tools, standards and algorithms of both fields. It is geared towards industrial CDRF applications. Using this framework, a robotic agent is trained to autonomously plan and build structures using two model-free DRL algorithms (TD3, SAC) in two case studies: robotic block stacking and sensor-adaptive 3D printing. The first case study serves to demonstrate the general applicability of computational design environments for DRL training and the comparative learning success of the utilized algorithms. Case study two highlights the benefit of our setup in terms of tool path planning, geometric state reconstruction, the incorporation of fabrication constraints and action evaluation as part of the training and execution process through parametric modeling routines. The study benefits from highly efficient geometry compression based on convolutional autoencoders (CAE) and signed distance fields (SDF), real-time physics simulation in CAD, industry-grade hardware control and distinct action complementation through geometric scripting. Most of the developed code is provided open source.

Supplementary information: The online version contains supplementary material available at 10.1007/s41693-022-00069-0.

The Reference Construction Site Aachen West, located in Aachen, Germany, is a campus-based living lab for research and university education in construction. To ensure and improve the education quality, this paper researches a new study tool in the form of a digital twin framework powered by the Unreal Engine. By implementing MQTT, an IoT communication protocol, the digital twin realizes synchronicity of cyber-physical bi-directional data flows. Representing the virtual Reference Construction Site, an online multiplayer application was developed as a use case to answer the research questions: (1) how to design the digital twin and structure in an extendable and accessible development framework for different users and (2) how to support education in construction with this digital twin. To validate the concept, a group of students were invited online to explore the application and provide user feedback. Results show positive effects of the digital twin on enhancing the quality of the online education in construction with its real-time interaction and data from on-site machinery and processes. The digital twin is planned to be transferred to research and construction projects.