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To boost productivity and efficiency, construction robots are anticipated to become increasingly prevalent in future construction workplaces. In such multi-robot environments, forming a well-composed robotic team is crucial for efficient task allocation and execution. However, the complexity of multi-robot dynamics, arising from the heterogeneity and varying scales of robotic teams, coupled with long-horizon tasks, presents challenges in estimating team utility and optimizing team composition. To tackle these challenges, a time- and utilization-guided simulation approach is proposed. Specifically, heterogeneous multi-robot dynamics is modeled as a cooperative multi-robot board game, and team utility is estimated using Monte Carlo tree search-based task scheduling. The optimal team composition minimizes completion time and maximizes robot utilization. Simulation results demonstrate the effectiveness of identifying the optimal team composition in three robot collaboration scenarios. This paper contributes to efficient and robust team utility estimation for robotic team formation in long-horizon construction tasks, addressing gaps in existing research. • Multi-robot team composition optimization for long-horizon tasks is presented. • Heterogeneous multi-robot construction is modeled as a cooperative board game. • Long-horizon task scheduling is approached based on Monte Carlo tree search. • Optimal team composition is determined by completion time and robot utilization. • Simulations of scaffolding assembly tasks demonstrate the robustness of the method. [ABSTRACT FROM AUTHOR]

Natural wood, renowned for its low carbon emissions, renewability, and impressive durability, is gaining prominence in the construction industry as a sustainable solution. However, the irregularities in size and shape of natural wood pose challenges in precise measurement and fabrication. Although innovative methods have emerged for materials like curved logs and small-diameter wood, tree forks have not been extensively investigated due to their complex shapes. This research introduces cooperative robotic fabrication to address the challenges associated with tree fork construction. An integrated workflow has been established, encompassing material scanning, co-design of fabrication tools and joint systems, and cooperative robotic fabrication. The proposed workflow was evaluated in the construction of a tree fork-based tower structure. This study provides a technological solution for the sustainable utilization of natural tree forks and holds potential for extending into the construction of other non-standard materials with geometric complexity. • This research presents an integrated workflow for natural tree fork construction. • Fabrication tools and connecting system are developed collaboratively. • A dual-robot cooperative fabrication system is established. • The workflow is evaluated through the construction of a case-study structure. [ABSTRACT FROM AUTHOR]

The transition to robotic construction requires advanced planning approaches to meet the specific requirements for successful robot deployment. Robots require precise information (e.g., position, velocity, force) to execute tasks reliably. Existing research focuses on robot-oriented construction processes but lacks real-time planning integration with building and construction information. To overcome these challenges, this paper proposes the Robot Construction Action Node (RCAN), serving as a middle layer system between BIM-based smart components and the Robot Operating System (ROS). RCAN breaks down construction planning into ordered activities for flexible adaptation and direct robot control. By assigning these construction activities to components, the macro construction process is linked to the micro construction methods. This procedure is tested and evaluated using a Linked Building Data (LBD) data modeling approach. The proposed solution is validated through three robot construction scenarios in an earlier built simulation environment. • An RCAN middle layer system is proposed for data linking and interaction between BIM and ROS. • A procedure converts IFC models and construction plannings into merged RDF graphs. • An RCAN-based framework allows interaction between smart components and robots. • A simulation presents execution of construction activities by a mobile manipulator using the RCAN system. [ABSTRACT FROM AUTHOR]

Human-robot collaboration (HRC) is expected to improve construction productivity by integrating robotic capabilities with human expertise. However, the effects of HRC on human emotions are unclear despite their critical impacts on human performance during HRC. This paper examined the inter-relationship between robots' parameters, like movement speed, and emotional responses, during HRC for a construction task. Eighteen participants were observed as they experience various conditions of robots' parameters during a bricklaying task with an arm-type collaborative robot in a laboratory setting. Their brainwaves (i.e., electroencephalogram (EEG) signals) were monitored using an EEG headset to identify emotional responses. Results showed significant emotional variations due to robots' parameters, which could affect the estimated productivity of human-robot teams. This paper demonstrated the emotional changes caused by the robots' parameters during HRC for construction tasks, highlighting the importance of considering emotions as a way to balance the emotional well-being of people and the high productivity of human-robot teams. • HRC for a bricklaying task was simulated in a lab environment with a real robot. • Emotional responses were analyzed by collecting EEG signals. • Emotions of every participant were significantly affected during HRC. • Almost every participant showed statistically unique emotional responses. • Considering emotions was influential to the estimated productivity of HRC. [ABSTRACT FROM AUTHOR]

Construction robots, as a collection of multidisciplinary technologies, have been extensively studied and implemented by scholars and engineers to boost construction efficiency and minimize the reliance on manual labor. However, the practical application of construction robots has been limited due to a lack of systematic research. The paper explores the future research direction of construction robots using a systematic literature review and a genealogical approach. Moreover, this paper qualitatively discusses the systematic classification of construction robots in terms of "timeline", "life cycle", and "functional features", forming three categories of construction robot genealogy. Moreover, future research directions are proposed from six perspectives: labor force structure, task division model, economic benefits, project management approach, research on the adaptability of construction robots to the site environment, and integration development. This paper provides new insights into the classification of construction robots and gives theoretical references for the subsequent development of construction robots. [Display omitted] • Propose the idea of genealogy to establish the classification of construction robots. • Categorize construction robots based on timeline, lifecycle, functional features. • 48 types of construction robots in the life cycle of construction are discussed. • Majority of construction robots on the market focus on the construction phase. • Discussion of further research directions for construction robots. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) and robotics research and implementation emerged in the architecture, engineering, and construction (AEC) industry to positively impact project efficiency and effectiveness concerns such as safety, productivity, and quality. This shift, however, warrants the need for ethical considerations of AI and robotics adoption due to its potential negative impacts on aspects such as job security, safety, and privacy. Nevertheless, this did not receive sufficient attention, particularly within the academic community. This research systematically reviews AI and robotics research through the lens of ethics in the AEC community for the past five years. It identifies nine key ethical issues namely job loss, data privacy, data security, data transparency, decision-making conflict, acceptance and trust, reliability and safety, fear of surveillance, and liability, by summarizing existing literature and filtering it further based on its AEC relevance. Furthermore, thirteen research topics along the process were identified based on existing AEC studies that had direct relevance to the theme of ethics in general and their parallels are further discussed. Finally, the current challenges and knowledge gaps are discussed and seven specific future research directions are recommended. This study not only signifies more stakeholder awareness of this important topic but also provides imminent steps towards safer and more efficient realization. [Display omitted] • Identifying ethical issues of AI and robotics. • Determining nine ethical issues of AI and robotics in the AEC industry. • Conducting a systematic review to discuss the ethics of AI and robotics in AEC. • Suggesting seven future research directions. [ABSTRACT FROM AUTHOR]

Construction robotics has emerged as a leading technology in the construction industry. This paper conducts an innovative dual-track quantitative comprehensive method to analyze the current literature and assess future trends. First, a bibliometric review of 955 journal articles published between 1974 and 2023 was performed, exploring keywords, journals, countries, and clusters. Furthermore, a neural topic model based on BERTopic addresses topic modeling repetition issues. The study identifies building information modeling (BIM), human–robot collaboration (HRC), and deep reinforcement learning (DRL) as "three pillars" in the field. Additionally, we systematically reviewed the relevant literature and nested symbiotic relationships. The outcome of this study is twofold: first, the findings provide quantitative and qualitative scientific guidance for future research on trends; second, the innovative dual-track quantitative analysis research methodology simultaneously stimulates critical thinking about the modeling of other similarly trending topics characterized to avoid high degree of homogeneity and corpus overlap. [Display omitted] • Previous AEC research has lacked a comprehensive macrolevel methodology. • A dual-track analysis method based on neural and SNA modeling is proposed. • The analysis of 995 journal papers revealed three pillars of construction robotic trends. • BIM, the HRC, and DRL were identified as the three crucial pillars in the field. • The outcome offers quantitative and qualitative assessments guiding future research. [ABSTRACT FROM AUTHOR]

Collective robotic construction (CRC) is an emerging approach to construction automation based on the collaboration among teams of small mobile robots. This paper enhances an existing modular CRC system, showcasing its capability to assemble full-scale in-plane timber structures. Utilizing strategies of co-design, the robotic actuators were updated to accommodate material tolerance in the passive building material, timber struts, which they use for locomotion and assemble into structures. A custom effector was also developed to establish architecturally relevant, structural connections between struts. The enhancements address two major challenges in CRC system development: material tolerance and connections. To showcase these research findings and compare with other construction automation systems, a reinforced slab structure was assembled. With a building envelop of 0.485 m3, this is one of the largest structures assembled by a CRC system. As such, the work highlights the potential applicability of CRC in real-world architectural projects. • Present research on assembly of in-plane timber structure with a novel CRC system. • Introduce co-design methods and mechanisms for dealing with material machining tolerances. • Present the development of an effector for execution of screwed connections by mobile robots. • Evaluate and compare the assembly processes of the presented CRC system with other robotic systems. [ABSTRACT FROM AUTHOR]

This comprehensive study delves into scientific publications, distinguishing general literature from formwork-specific findings and giving an overview of research methods and formwork systems for concrete construction. A shift towards reusable materials and the widespread use of robotics, particularly in 3D printing is noticeable. Structural research maintains a balanced focus, with little attention set on beams. While automation is attracting interest, full integration into formwork-related publications is still evolving. The need for practical implementation, applicability to mass production and sustainability aspects requires further exploration. Concluding, a list of future challenges is presented, serving as a guidance for subsequent research and developments in research and the concrete industry. [Display omitted] • This paper reviews formwork technologies regarding automation, structure, material, fabrication and test setups. • Shuttering systems are developed further in regard to sustainability, yet waste prevention and reuse are seldomly addressed. • Implementations considering mass production are low, setting a clear target for future research and development approaches. [ABSTRACT FROM AUTHOR]

With the increasing affordability of robot technologies and the reduction in size and weight of autonomous systems, the deployment of autonomous robotic systems on construction sites has gained significant attention. One major challenge faced by these systems is the accurate mapping and localization within an environment that constantly evolves on a daily basis. This computational problem, known as simultaneous localization and mapping (SLAM), has garnered substantial interest, leading to the proposal of various algorithms in recent years. This paper aims to provide a comprehensive overview of the SLAM algorithms by presenting the necessary background information, discussing the challenges involved in general, examining common approaches, and highlighting recent developments. Furthermore, the specific considerations related to deploying SLAM in construction environments are addressed. In order to provide practical insights into the advantages and use cases of state-of-the-art SLAM algorithms, a rigorous evaluation is conducted within a construction environment • Review of SLAM background, limitations, and approaches • Discussion of special considerations for localization of mobile robots in construction • Evaluation of three state-of-the-art LiDAR SLAM algorithms on a construction dataset • Experiments highlight challenges and the need for better LiDAR SLAM algorithms [ABSTRACT FROM AUTHOR]

The performance of post-installed fasteners in concrete can be affected by the chosen drilling and setting techniques. In the context of the development of a drilling robot for construction site implementation the impact of an automated setting process on the pull-out loads of fastening systems was evaluated. This article investigates the effect of two different fixing systems, drop-in expansion and chemical anchors, on the pull-out behaviour of manual and robotic drilling and installation. While for chemical anchors, the results indicate that robotic installation leads to similar pull-out forces with smaller deviation, expansion anchors in robotically drilled holes show slightly lower pull-out loads than those in manually drilled holes. Furthermore, a more uniform pull-out behaviour was observed for chemical anchors when set by the robot. In conclusion it can be shown that robotically set fasteners achieve a comparable performance, presenting a feasible automation approach for the construction industry. • The SDS chuck significantly impacts borehole accuracy • Manual set expansion anchors yield higher pull-out loads with less deviation • Robotic-installed chemical anchors offer more uniform pull-out behaviour • Robotic setting process shows less deviation in robotic-drilled holes [ABSTRACT FROM AUTHOR]

Recent advancements in three-dimensional (3D) scanning technology have broadened its applicability across fields like reverse engineering and autonomous robot navigation, which require a 3D representation of real-world objects. A key aspect of this digitalization process is autonomous view planning. This paper comprehensively reviews autonomous view planning techniques for 3D scanning of unknown or partially known geometries. The objective is to plan optimal scanning paths or views that supplement areas with insufficient or missing data. While the fields of graphics and robotics have approached automatic 3D scanning from different perspectives, they share a common goal of finding optimal views or paths to enhance an object's 3D digital model. This review examines the essential properties of the autonomous view planning process and classifies selected studies based on these aspects, with research trends visualized through a Sankey diagram. Finally, current limitations are discussed, and future directions necessary for practical implementation are explored. [Display omitted] • Conducts a comprehensive review of autonomous view planning in 3D scanning for a diverse range of applications. • Analyses methods and technologies used in autonomous view planning for unknown or partially known objects, and outlines research trends with Sankey diagrams. • Evaluates the performance of different manipulator types in 3D scanning, including ground mobile robots and drones. • Identifies and addresses key challenges and limitations in current autonomous 3D scanning techniques. • Proposes future research directions to overcome identified limitations and to tackle overlooked practical problems in autonomous 3D scanning. [ABSTRACT FROM AUTHOR]

Accurate robot localization in dynamic construction environments is vital for successful robotics implementation on jobsites. Dynamic Simultaneous Localization and Mapping (SLAM) methods show promise in addressing challenges by identifying and removing dynamic objects. However, existing studies struggle to accurately segment these objects, primarily due to handling unseen and temporarily static objects. This study presents a novel approach for robustly segmenting dynamic objects in construction environments. The approach captures potential objects using objectness masks and refines them by fusing masks with motion cues. The segmented information is propagated bi-directionally across frames. The method comprises four stages: objectness masks generation, motion saliency estimation, fusion of objectness masks and motion saliency, and bi-directional propagation of fused masks. Experimental results demonstrate the method's effectiveness in accurately segmenting dynamic objects and improving localization accuracy when integrated with SLAM structure, highlighting its potential for real-world applications in complex construction environments. • Unsupervised video object segmentation method for dynamic construction environments. • Fusion strategy combines objectness and motion cues for accurate segmentation. • Integration with ORB-SLAM3 improves localization accuracy in dynamic settings. • Evaluations on construction videos and SLAM datasets show competitive performance. [ABSTRACT FROM AUTHOR]

By shifting most of the site work to factories, manufacturing becomes a critical phase in modular construction. Human-robot collaboration (HRC) is a promising approach to enhancing modular construction manufacturing (MCM) productivity while maintaining necessary flexibility. However, little attention has been paid to understanding, in a holistic manner, the past, present, and future of HRC in MCM. To fill in this gap, this study presents a review of 78 relevant publications on HRC in MCM, focusing on tasks, human roles, and interaction levels. HRC solutions are found applicable to various MCM tasks whereas existing research primarily focuses on timber component production. It also reveals that humans can play diverse collaborative roles and interact with robots at varying levels. Potential opportunities, challenges, and future directions are further discussed. The review deepens the understanding of HRC in MCM and inspires future research. [Display omitted] • Review academic research on HRC for modular construction manufacturing (MCM). • Categorize the collected literature from tasks, human roles, and interaction levels. • Reveal a current research focus on timber component production. • Identify potential opportunities and challenges in HRC development for MCM. • Suggest future research to develop HRC solutions for building module manufacturing. [ABSTRACT FROM AUTHOR]

This paper introduces a multi-scale robotic approach for measuring the width, length, shape, and profile of hairline cracks in concrete structures. The approach uses a convolutional neural network to identify potential surface cracks, and then robotically navigates a high-resolution laser scanner to measure the detailed shape of the detected cracks. Finally, 3D point cloud registration techniques fuse the laser scans with LiDAR-based scan of the surrounding environment. The proposed method is validated with computer simulations and physical experiments on a concrete specimen. The results are compared against the state-of-the-art, vision-based methods as well as readings of a transparent crack width ruler. The comparison demonstrates the superiority and effectiveness of the proposed multi-scale robotic approach in measuring hairline cracks providing vital data for assessing the conditions of civil infrastructures. • Multi-scale measurement of hairline cracks in large concrete structures. • Integration of AI, robotics, and laser scanning for digital twinning. • Precise measurement of crack length, shape, width, and profile. [ABSTRACT FROM AUTHOR]

This paper proposes a trajectory planning and control method for heavy-duty manipulators based on inclination-displacement mapping. In this method, inclination sensors are employed to detect the excavator's orientation. By establishing a mapping relationship between joint inclination and cylinder displacement, the cylinder displacement is indirectly derived. Subsequently, considering the typical excavation operation process, the spatial trajectory of the excavator bucket is planned. The displacement of the driving cylinder is determined using kinematic inverse solutions and cubic polynomials. The cylinder displacement is then controlled using a position-velocity (PV) strategy. This method is tested on a 95-ton large excavator, and the results demonstrate several advantages, including easy installation, low maintenance requirements, smooth trajectory planning, and high tracking accuracy. These qualities make it suitable for the demands of intelligent operations in large excavators. Thus, this paper offers valuable insights for the intelligent transformation of heavy excavators. • The mapping relationship of joint inclination-cylinder displacement in larger robotic excavators is established. • The bucket spatial trajectories in a a typical work cycle are planned, and smooth planning trajectories are achieved. • Actual digging tests on a 95-ton robotic excavator are carried out, and accurate trajectory tracking are obtained. • The proposed trajectory planning and control method provides a new solution for automated excavation of large excavators. [ABSTRACT FROM AUTHOR]

Robotics holds great potential to improve productivity in construction, and coverage path planning (CPP) is an essential capability crucial to various applications, including floor cleaning and environmental monitoring. However, there is still a lack of a comprehensive CPP system that can handle complex indoor conditions and various robotic properties. An improved CPP (ICCP) system that leverages building information modeling (BIM) and robotic configurations for indoor robots is proposed in this study. Firstly, BIM is semantically enriched to generate semantic trapezoidal grid maps (TGMs); Next, a novel concept called "coverage bonus" is incorporated into coverage pattern analysis to enable farsighted decision making; Finally, the coverage sequence is optimized by solving the cluster generalized traveling salesman problem, resulting in routes that minimize both coverage distances and disruptions in indoor activities. Experimental validation shows that the ICPP system can not only attain optimal coverage performance with the highest coverage ratio (97.6%) but also ensure the adherence to indoor coverage rules. Future research will focus on enhancing coverage ratio through tunable hyperparameters, optimizing computation time in ICCP, and expanding the study to multi-robot scenarios. • Proposed an enriched BIM schema for indoor robotic coverage path planning. • Proposed an improved map decomposition method with hybrid formats. • Proposed the concept of coverage bonus for efficient coverage pattern analysis. • Optimized the coverage sequence by solving CGTSP for more practical values. • Validated the system on a laboratory and office floor with a 97.56% coverage ratio. [ABSTRACT FROM AUTHOR]

Multi-objective optimization can enhance design quality through performance simulation. However, ease or efficiency of construction is also important, and optimization may lead to difficult-to-build designs. Early quantification of constructability would allow designers to balance performance and construction issues. While it is difficult to quantify all factors that influence constructability, robotic construction simulations offer rich datasets to compare potential outcomes. This paper integrates constructability knowledge into early-stage design and examines the impact on multi-objective optimization. It evaluates robotic material delivery systems in constructing a standalone classroom optimized for structural, daylighting, and energy goals. When considering robotic pick and place time, the optimized designs differ, offering the opportunity to change design directions based on construction knowledge. Broader implications also become observable, as incorporating robots with higher carrying capacities reduces the structural elements and embodied carbon of optimal designs. This paper thus demonstrates benefits of incorporating robotic constructability simulation into early design optimization. • A time-based robotic construction metric is demonstrated for early design. • Simplified robotic material delivery paths are used to assess potential designs. • Classroom is optimized for embodied carbon, constructability, daylight, and energy. • Optimal geometries depend on priorities but are influenced by construction metric. • Larger robots reduce embodied carbon, showing value of early construction evaluation. [ABSTRACT FROM AUTHOR]

Excavation on jobsites is a collaborative effort, with a spotter serving as an extra set of eyes for the operator to ensure safety. However, current training methods using simulators are limited in immersion and primarily designed for individual operators to practice basic skills. This paper presents a multi-user teleoperation system featured with hybrid-immersive interface and between-user communication. The effectiveness of the system is evaluated through human subject experiments. The study's findings indicate that operators experienced greater immersion, a stronger sense of presence, and improved user interaction when using an immersive interface. This paper has important implications for the future of excavation training, with potential benefits including reduced utility strike damage and the opportunity to investigate human-robot collaboration in jobsites with multiple workers performing various roles within a highly immersive operational environment. • A multi-user excavator simulation system composed of robotic control, motion capture, and hybrid-immersive interfaces. • Human subject experiments with 57 subjects to evaluate the proposed system. • The effectiveness of the system is validated by achieving higher performance metrics than the monitor-based system. • Task completion time, accuracy, and safety are significantly improved under collaborative operation with a spotter. • Enhanced human factors study of the multi-user interaction with multi-roles in the human-robot collaboration in jobsites. [ABSTRACT FROM AUTHOR]

Single-objective optimal trajectory cannot adapt to the complex requirements of excavator construction. A comprehensive optimal trajectory planning method is proposed to optimize the working time, energy consumption, and operational impact of robotic excavators. Without fusing any performance indexes, a normalized multi-objective function and an improved particle swarm optimization algorithm are established to achieve a comprehensive optimization of multiple objectives, while considering joint angle, velocity, acceleration, and quadratic acceleration constraints. Typical deep pit excavation simulation and experimental results show that the multi-objective optimization method is feasible, can balance multi-objective constraints, and can avoid falling into extremely long working times or large impacts. This method offers a more efficient and effective solution for multi-objective trajectory planning and provides a method for planning excavation trajectories based on different operating scenarios and objectives. • Working time, energy consumption and operation impact are considered in trajectory planning. • A normalized multi-objective function is established to achieve a comprehensive optimization. • An improved particle swarm optimization algorithm is proposed to obtain the optimal solution. • Effectiveness of the trajectory planning method is validated by simulations and experiments. • The multi-objective optimization method can meet the actual construction requirements. [ABSTRACT FROM AUTHOR]

Building construction is one application of multiple robot systems (MRS) that is seeing a rise in research and is known as collective robotic construction (CRC). CRC specifically introduces a new set of research directions to those that already exist in the field of MRS, one of which is that of architectural design. As architectural design is essential to the process of construction, this paper presents a review of research related to architectural design in CRC. The reviewed literature is categorized based on three dimensions: design description, goal specification and execution. This categorization is utilized to detail approaches to architectural design and understand the implications of different approaches on the design process when considering CRC. By enabling architects with the ability to understand how to design architectural structures built by CRC systems and the implications thereof, this review aims to reduce the barrier for working with such novel construction automation systems. [Display omitted] • Review of collective robotic construction (CRC) based on architectural design. • Analysis of 122 peer-review publications. • Explanation of categorization of CRC based on approach to architectural design. • Discussion of trends and further research directions for architectural design in CRC. [ABSTRACT FROM AUTHOR]

Until the last decade, robots for thermal monitoring of buildings were limited to measuring ambient temperature. Recent research has focused on obtaining detailed thermal information about the structural components of buildings since the mid-2010s. This paper presents the methodology and results of the MoPAD2 (Mobile Platform for Autonomous Digitization) robot, an autonomous, low-cost, small-sized mobile robot equipped with a thermal scanning platform. The platform generates 3D thermal models of building interiors over time and conducts repeated data acquisition sessions, saving energy during waiting intervals. The paper explains how a dense thermal point cloud is obtained from different locations in the scene and processed to create a single 3D thermal model, and two case studies are presented. This robotics system has the potential to revolutionize thermal monitoring of buildings and enable energy characterization through thermal models. • The system generates omnidirectional point clouds of the environment. No precedents address the creation of omnidirectional thermal clouds. • The system extends the scope of current thermal scanning platforms in indoor settings, since it is able to work at a building floor level. • The small size and weight of MoPAD2 signifies that it has no restrictions as regards navigation in apartments, houses and office buildings. • The system works autonomously in multisession mode. No other robotic temporary thermal scanning system currently exists. • The system has an easy-to-use GUI, which allows non experts to manage MoPAD2 and to customise thermal scanning sessions. [ABSTRACT FROM AUTHOR]

In the face of climate change, it is essential to rethink how we build to reduce the environmental impact of the construction industry. Timber construction can be a solution to reduce the use of concrete or steel, whose manufacture emits much CO 2. This paper proposes a method to reinterpret tenon and mortise joints for construction. Friction-based assemblies, particularly mortise and tenon wooden joints, have been studied for centuries in Asia and Europe and may offer promising constructive solutions, such as allowing disassembling rather than demolishing a structure. These assemblies can be complex; they initially required a great deal of craftsmanship, which robotics can now replace to manufacture and assemble them. Nevertheless, it is necessary to develop methods to produce these robotic joints. Our method uses non-sequential assemblies, which are by definition, opposed to sequential assemblies. An assembly is said to be sequential when the elements that compose it are assembled one after the other, in a defined order or not. For a non-sequential assembly, all the elements that compose it are in motion and assembled simultaneously. This assembly method has the particularity of being locked only by its kinematics, which makes it possible to easily (dis)assemble the elements by only knowing their motion. After an introduction and defining the terms and hypotheses of our research, we discuss the related word. In the fourth section, the developed method is explained. Then, we introduce the different parameters to control the assembly's design and kinematics. After that, we extend the previous method to the third dimension. Finally, a construction prototype demonstrates the method's efficiency. [Display omitted] • Generality: our method works for pure translation, in 2D or 3D. • Exhaustivity: any kinematic can be generate thanks to parallel transformation. • Control: calculations and parameters are given to choose the kinematics according to external factors. [ABSTRACT FROM AUTHOR]

Abstract In this paper, a vision-based real-time extrusion quality monitoring system is developed for robotic construction. Initially, the details related to the extrusion system, data acquisition and processing unit, and the proposed algorithm are discussed. The reliability and responsiveness of the developed system is then evaluated on six determined levels of material variation. To demonstrate its application, the vision algorithm is used to develop an innovative closed-loop extrusion system. This system is able to automatically adjust the extrusion rate based on the vision system feedback. Such self-regulating extrusion system would be able to continuously print layers of acceptable dimensions using any printable mixture, without the need for prior calibration and despite some mixture rheology variations. All in all, the high precision and responsiveness of the developed system demonstrates the great potential for computer vision as a real-time quality monitoring and control tool for robotic construction. Highlights • A real-time extrusion quality monitoring technique is proposed. • Responsiveness and repeatability of the developed vision system are studied. • Feasibility of using computer vision for extrusion quality monitoring is proven. • The algorithm is used to develop an innovative closed-loop extrusion system. [ABSTRACT FROM AUTHOR]

Abstract Waste management scene is in urgent need of robotic waste sorter. Nails and screws, as part of the construction waste scene, are hard to be found and can therefore, cause damage to the site's construction safety and increase the material loss. This paper presents a construction waste recycling robot. In order to complete the recycling tasks, robots are expected to inspect the entire working environment and identify the target objects. This research uses neural network technology to assist the robot patrol in an unknown work environment and to use faster R-CNN methods to find scattered nails and screws in real time, so that the robot can automatically recycle nails and screws. This study introduces computer vision technology and a full-coverage path-planning algorithm into the field of construction waste management and proposes a novel construction waste recycling approach. Based on this robot, we can continue our study of construction waste recycling robots that can automatically sort and recycle most construction waste in the future. Highlights • An automatic robotic system for material recycling on construction sites was developed. • The recycling robot aims to acquire and sort construction waste on site. • Software-controlled patrol, detection and recycling at high automation level. • High efficiency and effectiveness with low computational burden. [ABSTRACT FROM AUTHOR]

Abstract Advances in robotics now permit humans to work collaboratively with robots. However, humans often feel unsafe working alongside robots. Our knowledge of how to help humans overcome this issue is limited by two challenges. One, it is difficult, expensive and time-consuming to prototype robots and set up various work situations needed to conduct studies in this area. Two, we lack strong theoretical models to predict and explain perceived safety and its influence on human–robot work collaboration (HRWC). To address these issues, we introduce the Robot Acceptance Safety Model (RASM) and employ immersive virtual environments (IVEs) to examine perceived safety of working on tasks alongside a robot. Results from a between-subjects experiment done in an IVE show that separation of work areas between robots and humans increases perceived safety by promoting team identification and trust in the robot. In addition, the more participants felt it was safe to work with the robot, the more willing they were to work alongside the robot in the future. Highlights • This study introduces a theoretical framework of Robot Acceptance Safety Model (RASM). • An Immersive Virtual Environment (IVE) is used as an experimental setting for a human-robot collaborative construction. • Separating robots from human work areas increases perceived safety by promoting team identification and trust in the robot. • Perceived safety promotes humans' intention to work with the robot in the future. [ABSTRACT FROM AUTHOR]

Abstract This article proposes a new computational method for the form-finding of nexorades, also called reciprocal frames in the literature. The method is based on the translations of members forming the initial layout. It is shown that the two geometrical quantities defining nexorades - eccentricity and engagement length - depend linearly on the transformation parameters. The method introduced in this article is thus based on linear algebra, so that fitting problems can be formulated as simple quadratic optimisation problems under linear constraints. The proposed method is therefore fast, simple to implement, robust and can be applied to various grid patterns. Furthermore, the proposed framework preserves planar facets. This paper proposes thus a new structural system where the nexorade is braced by planar facets. The feasibility of this structural system and of the computational framework introduced in this article is demonstrated by the fabrication of a 50 m2 timber pavilion. Highlights • A new form finding approach for nexorades is proposed. • The form finding is based on translation of members. • The influence of design parameters is discussed with numerous examples. • The resulting structures can be covered with planar panels which can be used as bracing components. • An experimental prototype demonstrates the potential of the form finding technique. [ABSTRACT FROM AUTHOR]

Scan-to-BIM technology can help construction stakeholders to obtain accurate BIM models for building information management and construction progress monitoring. The current practice of scan-to-BIM relies on laser scanning to capture a building's as-built condition, followed by processing scanned point clouds to generate BIM, which is laborious and time-consuming. This study presents a new robot-assisted mobile laser scanning approach using a legged robot and a solid-state LiDAR sensor, for automated 3D reconstruction and point cloud semantic segmentation. The proposed new approach involves integrating the Simultaneous Localisation and Mapping (SLAM) algorithm with robot motion control and pathfinding for 3D mapping to obtain the complete point cloud data. To ensure the completeness and density of scanned point clouds, new scanning fitness metrics and a grid-based direction-by-direction algorithm are designed for robot pathfinding. Then, an enhanced dynamic window approach is established to integrate the motion control of the legged robot with SLAM for reconstructing dense point clouds. Lastly, semantic segmentation is performed using ResPointNet++ to segment architectural and structural components. Experimental results show that the mobile scanning approach achieves a decent performance in point cloud completeness and density, with an overall mIoU of 81.75% for semantic segmentation. The ablation test demonstrates that the proposed approach is superior to the method that does not integrate the robot motion with SLAM. The proposed approach contributes to the efficient collection of high-quality point clouds for building interiors, with an aim to boost building information management. • Present a robot-assisted mobile scanning approach for 3D reconstruction and point cloud segmentation • Formulate new scanning fitness metrics integrating the point cloud completeness and density • Develop a grid-based direction-by-direction algorithm to compute the optimal scan positions • Propose an enhanced dynamic window approach to integrate robot control with SLAM to improve 3D mapping performance • Scanned point clouds are segmented using ResPointNet++ to recognise building components [ABSTRACT FROM AUTHOR]

The inefficiency, labor-intensity, and possibility of automation of the current practice of HVAC system inspections, which heavily relies on human inspectors, are being questioned. This paper describes a lightweight and reproducible robotic system dubbed AcTEA-bot, that automatically detects and locates thermal leaks in ceiling environments while self-navigating. AcTEA-bot is designed and built upon three modules, including an active visual SLAM module, a thermal leak detection module, and a thermal leak registration module. To evaluate the effectiveness of AcTEA-bot, a true-to-scale ceiling environment containing five thermal leaks is built, and 30 experiments are conducted where AcTEA-bot starts with different poses and conducts the inspection task. Results demonstrate that thermal leaks of multiple types in ceiling environments can be accurately detected and located. • Designed a miniaturized robotic system to navigate in cramped spaces. • Developed an exploration-based SLAM algorithm to allow navigation. • Developed a computer vision based method to identify leaks in ducts. • Developed an algorithm to register detected leaks on a point cloud. • Empirically showed robot's ability of detecting leaks in in-lab experiments. [ABSTRACT FROM AUTHOR]

Despite the widespread usage of Building Information Modeling (BIM) based simulation in the construction industry, robot construction simulation environments are built on particular modeling tools and can only be simulated with certain robot models. This not only limits construction robot development and also makes it difficult to directly share building information with robots, which impedes the study of robotic construction. To overcome this problem, we propose a new framework for robotic construction simulation which integrates open standard formats and tools to achieve a commonly used robot simulation environment. This provides a new approach to automatically convert IFC to simulation description format (SDF) and generate building components as smart components, which eliminates platform-specific limitations on robotic construction simulation, which further expands the utilization of BIM models. • An approach is proposed to the application of IFC-based BIM model in robotic simulation. • An automated ETL procedure is proposed for converting IFC models to SDF models. • Construction robot execution is simulated using a smart component-oriented construction planning system. • The robot simulation framework is tested to demonstrate the process of on-site robot construction simulation. • Additional types of robots and buildings can be integrated in the IFC-based robot construction simulator. [ABSTRACT FROM AUTHOR]

Building engineers need access to real-time data to make retrofit decisions or improve the energy performance of the building occupants. Such data is typically gathered with the help of fixed wired and/or wireless sensors. Such a process consumes a significant amount of time, effort, and financial resources, particularly in existing buildings which do not have a Building Automation Systems (BAS) installed. This paper introduces a framework that uses autonomous mobile indoor robots for gathering actionable building information in real-time, and discusses how this information can be further utilized for various analyses and critical decision-making. The navigation and drift correction algorithms developed for autonomous robot operation are described in detail. Experimental results demonstrate the feasibility and applicability of the proposed method in large areas using only a sparse set of sensors mounted on mobile indoor robots. This research also developed a generic framework for making informed retrofit decisions with the help of robot collected data. A case study is performed to demonstrate the informed retrofit decision making process with the help of temperature data collected by a robot and subsequently used in an EnergyPlus simulation. Simulated annual energy savings of 3% were obtained by slightly modifying the R-values (20% to 45% improvement) of one of the external wall assemblies. These savings are expected to be larger if whole building retrofit and upgrade of material is performed. Thus, the proposed framework offers promise in improving the energy efficiency by extending the approach to other combinations of building materials (e.g. doors and windows). [ABSTRACT FROM AUTHOR]

Automated interpretation of sewer CCTV inspection videos could improve the speed, accuracy, and consistency of sewer defect reporting. Previous research has attempted to use computer vision, namely feature extraction methods for automated classification of defects in sewer CCTV images. However, feature extraction methods use pre-engineered features for classifying images, leading to poor generalization capabilities. Due to large variations in sewer images arising from differing pipe diameters, in-situ conditions (e.g., fog and grease), etc., previous automated methods suffer from poor classification performance when applied to sewer CCTV videos. This paper presents a framework that uses deep convoluted neural networks (CNNs) to classify multiple defects in sewer CCTV images. A prototype system was developed to classify root intrusions, deposits, and cracks. The CNNs were trained and tested using 12,000 images collected from over 200 pipelines. The average testing accuracy, precision and recall were 86.2%, 87.7% and 90.6%, respectively, demonstrating the viability of this approach in the automated interpretation of sewer CCTV videos. [ABSTRACT FROM AUTHOR]

This paper introduces a robotic excavator with various hardware and software modules including a task planner (work command generator), work environment sensors, GPS and other sensors to acquire an excavator's status, as well as a robotic controller, electronic valves and other mechanisms. Both wired and wireless connections between these modules are required because of the remotely located control center and sensors. The modules of this robotic excavator also need to form an efficient and error-free data communication network considering the operational characteristics of the automated excavation work. Data communication methods, procedures and protocols of the excavator are addressed. A Data Communication Manager (DCM) is developed using TCP/IP, socket connection, and FTP for macro level communication between the modules, and a data communication protocol for each excavation process is established. Through simulation and field testing, the robustness of the data communication and networking were verified. [ABSTRACT FROM AUTHOR]

Robotic construction through concrete 3D printing is an in-progress revolution in construction industry. While significant progress is made on hardware related challenges, software and information related issues of such innovative system are less discussed. In this paper, a software platform is proposed for data retrieval and analysis from BIM models and utilizing it efficiently during various stages of the process. While the general idea of using BIM for automated construction has been mentioned before, the implementation details (the focus of this paper) were not previously discussed. To this aim, a framework is proposed to integrate BIM into an automated construction system. A Planning and Operations Control Software for Automated Construction (POCSAC) is developed as a major enabler for a seamless integration of BIM and Contour Crafting. The interoperation between different components of the construction system and BIM platform are designed to maximize the realized benefits through synergy of the two technologies. [ABSTRACT FROM AUTHOR]

In the course of frequently altering energy saving regulations, numerous buildings have to be comprehensively refurbished to meet the rising energy-efficiency standards in order to protect the global environment and to save resources. However, available materials as well as adaptable design concepts for additional energy-saving insulation layers are not yet convincing in terms of their long term recyclability or variation of shape. Therefore, we investigate the application of foam concrete onto bare walls of existing buildings to gain a façade finish which is highly insulating, easily recyclable and at the same time promises to be individually designable due to the properties of the raw material mixture. To ensure controllable as well as reproducible application and to react to changing working methods in architecture and construction, the research focuses on the automatized application of foam concrete using a robotic setup. We analyzed manual application strategies of foam concrete, considering parameters of handcraft, used tools as well as the reaction on varying material characteristics during application. Based on the analysis results, we present a concept for the robotic application of foam concrete, including suggestions regarding end effectors, robot programming and surface design planning. [ABSTRACT FROM AUTHOR]

This paper presents a novel robotic system that applies spray foam insulation in underfloor voids in order to improve the energy efficiency of buildings. The work focuses on solving the mapping and localization problems in such environments, since they are a key factor in the autonomy of the robot. Solving these tasks in underfloor voids is especially challenging because the terrain is extremely uneven due to the presence of stones, bricks and sand. Within these environments, the robot should be able to localize itself and apply the insulation foam to the underside of the floor. The robot is equipped with a 2D laser sensor which permits building point clouds from several positions of the underfloor environment. The localization process is solved by estimating the position of the robot with respect to previously known positions. For this purpose, the alignment between point clouds is calculated. This paper describes two algorithms to robustly obtain the alignment between two positions. The proposed algorithms are tested with a set of point clouds captured with a laser scan in several environments under real working conditions. The results show that the localization problem can be solved in such challenging underfloor voids by using depth information. [ABSTRACT FROM AUTHOR]

This paper presents the ROBO-SPECT European FP7 project, funded under the ICT-2013.2.2 programme on Robotics use cases & Accompanying measures, a robotized alternative to manual tunnel structural inspection and assessment of cracks and other defects. Physical developments include the design and implementation of a multi-degree-of-freedom robotic system, composed by a mobile vehicle, an extended crane, and a high precision robotic arm. A semi-supervised computer vision system to detect tunnel defects, and a ultrasonic sensor (US) robotic tool to measure width and depth of detected cracks have been also developed. An overview of defect detection methods, as well as the fundamental aspects of the project architecture and design, will be presented. In addition, the developed and implemented arm tip positioning algorithm for the US robotic tool shall be detailed. Finally, experimental evidence and details on validation in a real motorway tunnel with ongoing traffic will be provided. [ABSTRACT FROM AUTHOR]

Construction is an inherently less safe sector than other sectors because it exposes workers to harsh and dangerous working environments. The nature of the construction industry results in a comparatively high incidence of serious injuries and death caused by falls from a height, musculoskeletal disorders and being struck by objects. This paper presents a new concept that can tackle this problem in the future. The central hypothesis of this study is that it is possible to eliminate injuries if we move the human construction worker off-site and remotely link his/her motions to a Robotic Construction Worker (RCW) on-site. As a first steppingstone towards this ultimate goal, two systems essential for the RCW were developed in this study. First, a novel system that combines 3D body and hand position tracking was developed to capture the movements of human construction worker. This combination of tracking enables the capture of changes in the orientations and articulations of the entire human body. Second, a real-time simulation system that connects a human construction worker off-site to a virtual RCW was developed to demonstrate the proposed concept in a variety of construction scenarios. The simulation results demonstrate the future viability of the RCW concept and indicate the promise of this system for eliminating the health and safety risks faced by human construction workers. [ABSTRACT FROM AUTHOR]

In the present paper, a study is conducted on building systems associated with concrete extrusion-based additive manufacturing techniques. Specific parameters are highlighted - concerning scale, environment, support, and assembly strategies - and a classification method is introduced. The objective is to explicitly characterise construction systems based on such printing processes. A cartography of the different approaches and subsequent robotic complexity is proposed. The state of the art gathered from the literature is mapped thanks to this classification. It appears that the disruption potential brought by concrete 3D printing has not been fully embraced yet. [ABSTRACT FROM AUTHOR]

Prefabrication has the advantages of simplicity, speed and economy but has been inflexible to changes in design which is a primary reason behind its limited market share in the construction industry. To tackle this drawback, this study presents a Robotic Prefabrication System (RPS) which employs a new concept called “re-fabrication”: the automatic disassembly of a prefabricated structure and its reconstruction according to a new design. The RPS consists of a software module and a hardware module. First, the software employs the 3D model of a prefabricated structure as input, and returns motor control command output to the hardware. There are two underlying algorithms developed in the software module. First, a novel algorithm automatically compares the old and new models and identifies the components which the two models do not have in common in order to enable disassembly of the original structure and its refabrication into the new design. In addition, an additional novel algorithm computes the optimal refabrication sequence to transform one model into another according to the differences identified. Meanwhile, the hardware module takes the motor control commands as input and executes the appropriate assembly/disassembly operations, and returns the desired refabricated structure in real-time. Validation tests on two lab-scaled prefabricated structures demonstrate that the system successfully generated the desired refabrication sequences and performed all assembly operations with acceptable placement precision. [ABSTRACT FROM AUTHOR]

Unlike manufacturing robots, whose kinematics are pre-programmed based on robust metrology, tight tolerances, and rigid workpieces, construction robots operate under conditions of imperfect metrology, loose tolerances, and large workpiece uncertainties. Despite having access to a designed Building Information Model (BIM), construction robots must sense and model their actual environment, and adapt their kinematic plan to compensate for deviations from the expected. This research investigates methods to enable the autonomous sensing and modeling of construction objects so construction robots can ultimately adapt to unexpected circumstances and perform quality work. To that end, two construction component model fitting techniques are presented, namely the Clustering and Iterative Closest Point (CICP) construction component model fitting technique and the Generalized Resolution Correlative Scan Matching (GRCSM) construction component model fitting technique. The GRCSM construction component model fitting technique employs the presented GRCSM search algorithm, which is a modified version of the existing Multi-Resolution Correlative Scan Matching (MRCSM) search algorithm. Three experiments are presented to evaluate the ability of the CICP and GRCSM construction component model fitting techniques to model construction features. It was found that the CICP and GRCSM construction component model fitting techniques are capable of estimating the pose and geometry of arbitrarily shaped objects and construction joints, but are susceptible to modeling error. Despite their limitations, the CICP and GRCSM construction component model fitting techniques appear to be promising tools for the geometric estimation of construction features, especially for situations involving full automation, detailed construction work, incomplete sensor data, and complex object geometry. [ABSTRACT FROM AUTHOR]

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. • 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. [ABSTRACT FROM AUTHOR]

Despite the increased interest in construction robotics both in academia and the industry, insufficient attention has been given to aspects related to Human-Robot Interaction (HRI). Characterizing HRI for construction tasks can help researchers organize knowledge in a structured manner that allows for classifying construction robotics applications and comparing and benchmarking different studies. This paper builds upon existing taxonomies and empirical studies in HRI in various industries (e.g., construction, manufacturing, and military, among others) to propose a multidimensional taxonomy to characterize HRI applications in the construction industry. The taxonomy design followed a systematic literature review in which common themes were identified and grouped into 16 categories. The proposed taxonomy can be used as a foundation for systematic reviews and meta-analyses of HRI applications in construction and can benefit the construction industry by informing the design of collaborative tasks performed by human-robot teams. • Construction automation and robotics have drawn increasing attention in the past decades. • Existing Human-Robot Interaction taxonomies are limited for construction studies. • The taxonomy includes team, task, and environment factors in construction robotics. • The taxonomy more appropriately characterizes construction robotics applications. [ABSTRACT FROM AUTHOR]

The rapid development of robotic technology makes transforming from manual activities to human–robot collaboration (HRC) possible in construction. An HRC system can improve construction productivity and safety by matching robot intelligence with human skills. However, research exploring the status, challenges, and emerging research trends of HRC in the construction domain is lacking. Aiming to bridge the knowledge gap, a systematic review integrating quantitative and critical analysis is conducted using 110 relevant articles. Specifically, HRC studies are quantitatively analyzed from the following perspectives: robot types, construction activities, and interaction roles. Moreover, three significant HRC research themes are identified based on the critical review: adaptive robot programming, human–robot interaction interface, and safety issues. Finally, future research trends are introduced to inspire more discussions. This review provides new insights into HRC and supports the future research and development of HRC in the construction industry. [Display omitted] • Existing literature related to HRC in on-site construction was systemically reviewed. • Quantitative and critical analysis is conducted using 110 relevant HRC articles. • Potential construction activities and human-robot relationship in HRC was identified. • Existing methods and interface to support HRC on-site application was discussed. • Systemic challenges and research trends of the HRC in construction were highlighted. [ABSTRACT FROM AUTHOR]

Technological interoperability is a driver for seamless data and information exchange between project team members in the Architecture, Engineering, and Construction (AEC) industry. It is defined as the ability of different systems to exchange information with minimum loss. Therefore, interoperability lack is often a barrier in modern construction applications, such as robotics. Construction and robotics, seen from their respective areas, are highly divergent in context, organization, procedures, and technologies. However, both paradigms use computation, which gives computational systems the potential to enable construction robotics. This research is based on the Design Science Research (DSR) methodology and aims to develop a framework for operationalizing industrial robots in construction. To this end, it uses Computational Design (CD) driven by Building Information Modeling (BIM) for Robotic Manufacturing (RM) within Off-Site Construction (OSC) systems. This technological alignment allowed the development of an integrated Design-to-Manufacturing (DtM) framework, validated by 16 evaluators. • Proposed a framework for a Design-to-Manufacturing (DtM) approach in construction. • The DtM approach is based on BIM and computational design technological integration. • Used computational design as a bridge to integrate BIM and robotic manufacturing. • Presented BIM as a medium for using computational design in off-site construction. • Demonstrated robotic manufacturing in prefabricated and modular construction. [ABSTRACT FROM AUTHOR]

This article presents the development and validation of a design-to-fabrication framework aiming to improve the efficiency of fabricating reinforced concrete building components for housing projects in the Kingdom of Saudi Arabia by incorporating 3D concrete printing technology. In particular, the research presents an algorithmic framework to mass customise a typical Saudi Arabian free standing house by utilising parametric modelling, topology optimization (TO), finite element analysis (FEA), and robotic 3D printing tools and techniques. The framework was validated by the fabrication of optimised reinforced concrete columns and by testing their structural performance under the Saudi Building Code (SBC 304). The findings demonstrate the benefits and drawbacks of the proposed framework and compare it to current Saudi conventional construction approaches. The paper also addresses the need for mass customisation in the construction industry of the Kingdom of Saudi Arabia • Design to fabrication of concrete building components. • Mass customisation of 3D-printing reinforced concrete frame structure. • Topology optimisation of 3D-printing reinforced concrete frame structure. • Validating structural performance of topology optimised column subjected to axial load. • Illustrating the efficiency of 3D printing optimised structural building components, mitigating labour and material waste. [ABSTRACT FROM AUTHOR]

Fire curtains (or dampers), as found in HVAC ducts, are drop-down thermally triggered fire-resistant barriers designed to block the spread of fire and smoke. Regulations stipulate regular mechanical drop-down testing as a visual inspection may not identify rust or jamming which may prevent a curtain from falling. Currently it is difficult to test these curtains as many HVAC ducts are small (340 mm × 330 mm) and access hatches often don’t allow direct external access to the thermal fuse. Hence complete testing may require the temporary (and expensive) removal of plaster walls, floors or ceilings. This paper describes a pair of tele-operated robots which are designed to mechanically test the operation and serviceability of the fire curtain. Significant experimentation has demonstrated that the testing procedure requires a skilled operator and can be performed typically within a 15 minute timeframe without the expense of renovation and remediation procedures. [ABSTRACT FROM AUTHOR]

As modern methods of construction progressively incorporate more facets of manufacturing, design optimization tools used in manufacturing can be adopted into construction to solve complex challenges. The specification and control of dimensions and geometry of construction assemblies is one such challenge that can be solved using tools from manufacturing. Even with building information models (BIM) to assist with clash detection for identifying potential dimensional problems, or the use of tolerances to control critical features in an assembly, dimensional variability is still a complex challenge to address in construction. This paper explores the use of a dimensional variation analysis (DVA), which is a design optimization tool from the manufacturing industry. This paper presents a DVA approach which is based on kinematics theory in robotics to define the assembly equation (how components are dimensionally related to each other). A case study is used to validate the proposed framework through two distinct approaches: (1) an as-designed (model-based) DVA and (2) an as-built (laser-based) DVA. Comparison of these two methods resulted in a percent difference less than 1% which demonstrates the reliability of using the model-based method for designing critical construction components. [ABSTRACT FROM AUTHOR]

In this paper a novel methodology is proposed for digital workflow and fabrication of a freeform shell made from foam materials utilizing a robot hot-wire cutting tool. The major issues of using freeform foam structures pertain to building complex shape constructions in contemporary architecture and fabrication of structural, insulated panels for double-curved walls. In order to provide an efficient method of freeform surface panelization, two different tessellation approaches are presented - a quadrilateral shaped panel and a puzzle shaped panel. A puzzle shaped panel is chosen for fabrication purposes, in order to test the benefits that come with it, including friction fit connections, and assemblability. Digital workflow based on the graphical algorithm editor Grasshopper is used for the development of complex panel shapes derived by tessellation procedures. The proposed method is tested through fabrication of a full scale architectural prototype. [ABSTRACT FROM AUTHOR]

This paper deals with research into advanced hierarchical graph structures which have been developed for knowledge representation in computer-aided design and robotics. Complex real-life engineering problems still need useful graph-based models. A new approach to represent models preserving the intrinsic nature of problem structures using graphs with many hierarchies is proposed. Different types of hierarchical dependencies (geometrical, functional, etc.) between design parts can be expressed in one structure. Moreover, the possibility of selecting only problem-related hierarchies reduces the number of data to be analysed in the process of reasoning about considered designs. This approach is illustrated by applying multi-hierarchical graph structures to represent complex data used for robot control algorithms. [ABSTRACT FROM AUTHOR]