Construction Of Robot Research Articles

Leveraging Building Material as Part of the In-Plane Robotic Kinematic System for Collective Construction.

Although collective robotic construction systems are beginning to showcase how multi‐robot systems can contribute to building construction by efficiently building low‐cost, sustainable structures, the majority of research utilizes non‐structural or highly customized materials. A modular collective robotic construction system based on a robotic actuator, which leverages timber struts for the assembly of architectural artifacts as well as part of the robot body for locomotion is presented. The system is co‐designed for in‐plane assembly from an architectural, robotic, and computer science perspective in order to integrate the various hardware and software constraints into a single workflow. The system is tested using five representative physical scenarios. These proof‐of‐concept demonstrations showcase three tasks required for construction assembly: the ability of the system to locomote, dynamically change the topology of connecting robotic actuators and timber struts, and collaborate to transport timber struts. As such, the groundwork for a future autonomous collective robotic construction system that could address collective construction assembly and even further increase the flexibility of on‐site construction robots through its modularity is laid.

Selecting a Suitable Sustainable Construction Method for Australian High-Rise Building: A Multi-Criteria Analysis

The evolution of innovative construction technology and automation has rapidly transformed the construction industry over the last few decades. However, selecting the most efficient and sustainable construction technology for high-rise building construction is a critical factor in completing the project successfully. This requires a multiple-judgment-decision process relevant to cost, time, environment, sustainability, quality, etc. Thus, this research aims to identify the most suitable sustainable construction method for high-rise building construction in Australia. Three construction methods (i.e., automated building construction, aluminium formwork construction, and off-site construction) and robotic construction technology are reviewed in terms of economic, equity and environmental performance. A detailed multi-criteria analysis is conducted concerning the weighting calculated for each construction method, which aids in recommending a sustainable and cost-effective method. The analytical hierarchy process (AHP) is used as a multi-attribute decision-making tool to determine the weighting factors. The results show that the off-site construction method and robotic construction technique significantly improve the construction performance of high-rise construction in Australia. However, the finding is based on data obtained from a limited number of experts. Thus, a detailed case study with a greater number of expert opinions is needed to ensure the significance of the finding. However, the AHP-based approach method can be used to select sustainable construction alternatives for high-rise buildings.

Construction site safety monitoring and excavator activity analysis system

With the recent advancements in deep learning and computer vision, the AI-powered construction machine such as autonomous excavator has made significant progress. Safety is the most important section in modern construction, where construction machines are more and more automated. In this paper, we propose a vision-based excavator perception, activity analysis, and safety monitoring system. Our perception system could detect multi-class construction machines and humans in real-time while estimating the poses and actions of the excavator. Then, we present a novel safety monitoring and excavator activity analysis system based on the perception result. To evaluate the performance of our method, we collect a dataset using the Autonomous Excavator System (AES) (Zhang et al., Sci Robot 6(55):eabc3164) including multi-class of objects in different lighting conditions with human annotations. We also evaluate our method on a benchmark construction dataset. The results showed our YOLO v5 multi-class objects detection model improved inference speed by 8 times (YOLO v5 x-large) to 34 times (YOLO v5 small) compared with Faster R-CNN/YOLO v3 model (Zhang et al., In Proceedings of the 38th International Symposium on Automation and Robotics in Construction 461 (ISARC), pp. 49–56. InternationalAssociation for Automation and Robotics in Construction (IAARC), Dubai, UAE (2021). https://doi.org/10.22260/ISARC2021/0009). Furthermore, the accuracy of YOLO v5 models is improved by 2.7% (YOLO v5 x-large) while model size is reduced by 63.9% (YOLO v5 x-large) to 93.9% (YOLO v5 small). The experimental results show that the proposed action recognition approach outperforms the state-of-the-art approaches on top-1 accuracy by about 5.18%. The proposed real-time safety monitoring system is not only designed for our Autonomous Excavator System (AES) in solid waste scenes, it can also be applied to general construction scenarios.

Safety, quality, schedule, and cost impacts of ten construction robots.

IntroductionRobots 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.ObjectivesGiven this new reality, this research aims to quantify the impacts of existing construction robots.MethodsThis 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.ResultsThe 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.ConclusionEven 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.

An Overview of Network Robot System and Its Applications

A robot capable of performing multiple tasks is a multi-purpose device with one or more arms and joints. Robots are similar to humans, but industrial robots do not resemble humans. Any machine that operates automatically that modifies human effort is a robot that does not look or resemble humans in appearance or perform human-like functions. In a nutshell, The full layout of ROBOT consists of a movable body system, a motor, which controls all of these organs is the "brain". Basically, the engineering department handles construction and operation. Reflect the behavior of humans and animals. Robotics, layout, production and use of machines (robots) to perform responsibilities traditionally carried out by humans. Robots are extensively used in industries which include automotive manufacturing, and in industries that require them to perform easy obligations and work in hazardous environments. The six most common kinds of robots are self sustaining cellular robots (AMRs), automatic guided motors (AGVs), exposed robots, human figures, robots and hybrids. Are robots used to power performance, speed up procedures, enhance safety, and enhance experience in more than one industry. One of the key factors controlling how an industrial robot moves and its workplace is its robot structure. There are six main types of robot structures: Cartesian, cylindrical, spherical, and selectively compatible articulated robotic arm (SCARA). Articulate, and the delta (parallel) is a human-like machine and the machine performs routine tasks according to command. A person who acts mechanically, routinely and responsibly, usually subject to the will of another; Automatic. Robots are often controlled wirelessly or autonomously using tether (wire). The easiest way to control a robot is to use a hand-held controller that is physically attached to the robot using wires or cable. What are robotics and robotics? A robot is Any device that transforms human effort, although it no longer appears like someone in look or perform the same capabilities as someone. As a discipline, Robotics offers the layout, construction and operation of robots.

Can robots replace human beings? —Assessment on the developmental potential of construction robot

Construction robots are a powerful means of addressing labor shortages and productivity inefficiencies. Studies discussed the application of construction robots on the level of economic and technical feasibility. However, the substitution potential of different construction robots has not been specifically explored in the context of the current situation of construction machine substitution. Task competence is the main reason for “machine substitution.” Thus, this research focuses on task competence attributes and builds an analytical framework for discussing human–task–machine in the construction field based on the basic framework of task-technology fit theory. Based on this analytical framework, this research sorts out the task processing logic of construction workers and robots. Moreover, this research establishes a measure of the substitution potential of “machine substitution” in the construction field. Taking the construction market of Mainland China as an example, this research analyzed the development potential of different construction robots, considering the current situation of “machine substitution” in the construction field. Results show that there is a shortage of construction robot types at present. For existing construction robots, differences exist in terms of how construction robots perform tasks competently and the degree to which they can replace construction workers. In general, the construction field is in a phase of human–machine collaboration and will remain so for a long time to come. Results can provide guidance for the application promotion and decision-making of construction robots.

МАГНИТОМЕТРИЧЕСКИЕ СЕНСОРНЫЕ СИСТЕМЫ ДЛЯ МОРСКИХ МОБИЛЬНЫХ РОБОТОВ

The paper proposes the use of marine mobile robots to counter mining, detection, classificationand localization of mines. In accordance with the purpose, search and reconnaissance deviceshave been identified for the survey of water areas that are capable of operating autonomously orin remote control mode with decision-making support. The analysis of existing types of sensorsystems for the survey of water areas is carried out. The main results of theoretical and experimentalstudies of ways to improve magnetometric sensor systems of marine robots are presented.It is proposed, based on the criterion of ensuring the greatest capabilities of marine robots fordetecting and the pace of searching in offline mode with known weight and size limitations, theconstruction of a magnetometric sensor system with automated recognition of explosive objects.For the purposes of automated classification of search objects, it is proposed to take advantage ofneural networks, which, unlike traditional machine learning, provide the possibility of high-levelabstract expression of the semantics of internal connections between data by choosing architecturalsolutions. The structure of the neural network is obtained based on the linear classificationof explosive objects according to two parameters of the training sample. Based on a proven trainingsample and a classifying function by two parameters, for a multichannel magnetometric system,an implementation of the neural network structure has been developed that takes into account,in addition to the ferromagnetic mass and depth of occurrence, the parameters of the geometricshape of real explosive objects. The directions of improving and increasing the range offerrosonde sensors as the most suitable for the construction of magnetometric detection systemsfor marine mobile robots are determined. A method is proposed to increase the sensitivity offerrosonde magnetometric sensors of marine robots through the use of new magnetic materialsand circuit solutions. To create highly sensitive ferrosonde magnetometric sensors, the use ofcores made of amorphous cobalt-based alloys of the AMAG-170 type is proposed, providing apotential opportunity to increase the conversion coefficient (sensitivity) of the sensor system byincreasing the excitation frequency of the core of the ferrosonde. The functional diagram of thelayout of the developed magnetometric ferrosonde sensor system based on two rod cores made ofamorphous alloy AMAG–170 is presented.

Identifying the risk factors and their interactions of human–robot collaboration implementation during engineering project construction: evidence from China

PurposeHuman–robot collaboration (HRC) is an emerging research field for the construction industry along with construction robot adoption, but its implementation remains limited in construction sites. This paper aims to identify critical risk factors and their interactions of HRC implementation during engineering project construction.Design/methodology/approachLiterature research, expert interviews, a questionnaire survey and a social network analysis (SNA) method were used. First, literature research and expert interviews were employed to identify risk factors of HRC implementation and preliminarily understand factor interactions. Second, a questionnaire survey was conducted to determine the degree of interactions between risk factors. Third, based on the data collected from the questionnaire survey, SNA metrics were used to find critical risk factors and critical interactions.FindingsThe critical risk factors consist of robot technology reliability, robot-perceived level, conflict between designers and users of construction robots, organisational culture, organisational strength, project cost requirements, changeability of project construction, project quality requirements and project safety requirements. The interactions between risk factors are strong and complex. Robot technology risk factors were relatively fundamental risk factors, and project risk factors had a direct influence on the risk of HRC implementation. The implementation cost of HRC was not identified as a critical risk factor. Individual risk factors could be mitigated by improving technical and organisational factors.Originality/valueThis paper contributes to the body of knowledge in the field of both HRC behaviours and its risk management in construction project management. Identifying the critical risk factors and their interactions of HRC implementation in the construction industry and introducing social network theory to the research on critical risk factors are the innovations of this paper. The findings and proposed suggestions could help construction professionals to better understand the HRC risk factors and to manage the risk of HRC implementation more effectively.

Impact of VR-Based Training on Human–Robot Interaction for Remote Operating Construction Robots

Impact of VR-Based Training on Human–Robot Interaction for Remote Operating Construction Robots

Research focus for construction robotics and human-robot teams towards resilience in construction: scientometric review

PurposeRapid urbanisation and recent shock events have reiterated the need for resilient infrastructure, as seen in the pandemic. Yet, knowledge gaps in construction robotics and human–robot teams (HRTs) research limit maximising these emerging technologies’ potentials. This paper aims to review the state of the art of research in this area to identify future research directions in HRTs able to aid the resilience and responsiveness of the architecture, engineering and construction (AEC) sector.Design/methodology/approachA total of 71 peer-reviewed journal articles centred on robotics and HRTs were reviewed through a quantitative approach using scientometric techniques using Gephi and VOSviewer. Research focus deductions were made through bibliometric analysis and co-occurrence analysis of reviewed publications.FindingsThis study revealed sparse and small research output in this area, indicating immense research potential. Existing clusters signifying the need for further studies are on automation in construction, human–robot teaming, safety in robotics and robotic designs. Key publication outlets and construction robotics contribution towards the built environment’s resilience are discussed.Practical implicationsThe identified gaps in the thematic areas illustrate priorities for future research focus. It raises awareness on human factors in collaborative robots and potential design needs for construction resilience.Originality/valueRapid urbanisation and recent shock events have reiterated the need for resilient infrastructure, as seen in the pandemic. Yet, knowledge gaps in construction robotics and HRTs research limit maximising these emerging technologies’ potentials. This paper aims to review the state of the art of research in this area to identify future research directions in HRTs able to aid the resilience and responsiveness of the AEC sector.

Research on the Application of Mobile Robot in Timber Structure Architecture

The involvement of robots in building construction is already a global trend. Compared with the current stage of construction in which a large number of people are involved, the stability of the robot construction process will greatly affect the construction efficiency and construction accuracy, thus (1) reducing the impact on the environment, hence saving natural resources with other obvious advantages of natural environmental benefits, and (2) reducing construction costs, therefore reducing the economic and environmental benefits of artificial use. This paper proposes a wooden building construction method using a mobile robot, explores the assembly of continuous building components that exceed the robot’s static workspace, and completes a simulated construction experiment of a wooden building using this construction method. The experiment was used as a basis to address (1) innovations in the way a wooden building is erected that satisfy the construction logic of the mobile robot, and (2) the ability of the mobile robot to accurately assemble building components in space, including the ability to align them with existing components on site. Ultimately, the completion of this experiment and its construction evaluation demonstrated the superiority of mobile robot construction over manual construction in terms of reduced manual use and increased construction efficiency.

A strategy for integrating hillside dwelling design and automated construction techniques

PurposeSloping topographies in urban areas are often under-utilised due to complex designs and difficult access, resulting in low construction productivity and high cost. Automated construction techniques are usually limited to flat sites or lab spaces. This research combines concepts for automated and prefabricated construction with hillside dwelling design. It proposes a strategy to integrate both aspects and to equally inform design process and design output. The aims are to turn hillside access and construction automation into design generators, improve productivity and use more affordable hillside sites.Design/methodology/approachAnalysis of typologies for hillside housing and automated construction techniques is used to derive principles and parameters to inform a strategy and generative script for setting out, volumetric disposition and access and using the topography as a design-generator. The output from the generative script and tool can then form the basis of a high-density, low-rise dwelling development suited for serial, automation-assisted construction. The strategy is tested on a case study site.FindingsThe typological analysis helps devising strategies for integrating construction robotics and design criteria for hillside housing. The generative script illustrates how a strategy is implemented and used in a design tool able to absorb varying input data, including topographies. This generates innovative, site-specific design outcomes, suited for a process that adapts contemporary construction automation techniques and allows for more efficient use of hillside sites.Originality/valueThis research builds on construction automation methods and proposes novel combinations and adaptations for use on hillside sites. It demonstrates how robotics and generative tools can inform early design stages.

Learning Robotic Manipulation of Natural Materials With Variable Properties for Construction Tasks

The introduction of robotics and machine learning to architectural construction is leading to more efficient construction practices. So far, robotic construction has largely been implemented on standardized materials, conducting simple, predictable, and repetitive tasks. We present a novel mobile robotic system and corresponding learning approach that takes a step towards assembly of natural materials with anisotropic mechanical properties for more sustainable architectural construction. Through experiments both in simulation and in the real world, we demonstrate a dynamically adjusted curriculum and randomization approach for the problem of learning manipulation tasks involving materials with biological variability, namely bamboo. Using our approach, robots are able to transport bamboo bundles and reach to goal-positions during the assembly of bamboo structures.

An Occupancy Information Grid Model for Path Planning of Intelligent Robots

Commonly used robot map models include occupancy grid maps, topological maps, and semantic maps. Among these, an occupancy grid map is mainly represented as a quadrilateral grid. This paper proposes an occupancy information grid for intelligent robots by exploiting the advantages of the occupancy grid map and spatial information grid. In terms of geometric structure, a regular hexagonal grid is used instead of a regular quadrilateral grid. In terms of attribute structure, the single obstacle attribute is replaced by the grid terrain characteristics, grid element attributes, and grid edge attributes. Thus, the occupancy information grid model is transformed into a new data structure describing the spatial environment, and it can be effectively applied to map construction and path planning of intelligent robots. For the map construction application of intelligent robots, this paper describes the basic process of laser sensor-based grid model construction. For the path planning application of intelligent robots, this paper extends the A* algorithm based on a regular hexagonal grid. Additionally, map construction and path planning applications for intelligent robots are experimentally verified. Several experimental results were obtained. First, the experimental results confirmed the theoretical conclusion that the minimum sampling density of the hexagonal structure was 13.4% lower than that of the quadrilateral structure. Second, the regular hexagonal grid is clearly more advantageous in describing environmental scenes, which can ameliorate the "undercompleteness" phenomenon. Third, there were large differences in the planning paths based on two types of grids, as shown by the fact that the distance of the planning paths obtained by the regular hexagonal grid was reduced by at least 10.8% and at most 15.6% compared with the regular quadrilateral grid.

BIM-based simulation of construction robotics in the assembly process of wood frames

One main challenge in the development/implementation of construction robotics is the need of accurate and explicit information as input for the robots to reliably perform the designated tasks. Building information modeling (BIM) can fill this gap by providing information to the construction phase that utilizes automated fabrication. However, current BIM authoring tools and construction workflows do not directly support robotic simulation and are not designed to be compatible with robotic technologies. Thus, the authors developed a simulation-based methodology to evaluate the automated assembly of wood frames. Results showed that the robotic assembly process was successfully simulated/analyzed on three test cases. Compared to manually creating such robotic simulations, the proposed approach was on average 39 times faster and is expected to dramatically reduce errors from design to build. The proposed approach opens a new door for practitioners to analyze a building design related to the use of robotics for its construction.

A study of children’s learning and play using an underwater robot construction kit

A study of children’s learning and play using an underwater robot construction kit

How human-robot collaboration impacts construction productivity: An agent-based multi-fidelity modeling approach

Though construction robots have drawn attention in research and practice for decades, human-robot collaboration (HRC) remains important to conduct complex construction tasks. Considering its complexity and uniqueness, it is still unclear how HRC process will impact construction productivity, which is difficult to handle with conventional methods such as field tests, mathematical modeling and physical simulation approaches. To this end, an agent-based (AB) multi-fidelity modeling approach is introduced to simulate and evaluate how HRC influences construction productivity. A high-fidelity model is first proposed for a scenario with one robot. Then, a low-fidelity model is established to extract key parameters that capture the inner relationship among scenarios. The multi-fidelity models work together to simulate complex scenarios. Based on the simulation model, the twofold influence of HRC on productivity, namely the supplement strategy on the worker side, and the design for proactive interaction on the robot side, are fully investigated. Experimental results show that: 1) the proposed approach is feasible and flexible for simulation of complex HRC processes, and can cover multiple collaboration and interaction modes; 2) the influence of the supplement strategy is simple when there is only one robot, where lower Check Interval (CI) and higher Supplement Limit (SL) will improve productivity. But the influence becomes much more complicated when there are more robots due to the internal competition among robots for the limited time of workers; 3) HRC has a scale effect on productivity per robot, which means the productivity improves if there are more robots and workers, even if the human-robot ratio remains the same; 4) introducing proactive interaction between robots and workers could improve productivity significantly, up to 22% in our experiments, which further depends on the supplement strategy and the human-robot ratio. Overall, this research contributes an integrated approach to simulate and evaluate HRC’s impacts on productivity as well as valuable insights on how to optimize HRC for better performance and occupational health. The proposed approach is also useful for the evaluation and development of new robots.

Perancangan Mobil Remot Kontrol Arduino dengan Bluetooth Via Android

The development of science and technology in today's world is very rapid, technological progress is something that cannot be avoided in this life because technological progress will run in accordance with scientific advances. The concept behind the manufacture is the development of an unmanned vehicle control system or commonly called a drone, a vehicle that can be operated without a crew and controlled remotely using Bluetooth installed on a smartphone. Mobile Robot is a robot construction whose characteristic is that it has an actuator in the form of a wheel to move the entire robot body, so that the robot can move positions from one point to another. This Mobile Robot is designed to move using an Android Smartphone controller that has applications that are suitable for robot movement. This robot uses Arduino Uno as a Bluetooth robot control system, the module for the tool made this time uses a Bluetooth module as a data receiver sent via an android smartphone which will then be processed by Arduino and then forwarded to the L298N motor driver which will provide output to move the DC motor according to the command provided wirelessly. The method used is an experimental method, the research carried out will focus on communication between Smartphone devices and the Arduino Uno microcontroller via Bluetooth to control robotic devices. In its implementation it can be applied to reach locations that are difficult or dangerous enough for humans to enter, so that with technology like this it will make it easier for humans to reach these areas without having to enter the location but only by controlling the device remotely.Keywords: Adruino Microcontroller, L298N DC Motor, Bluetooth, Smartphone.

Autonomous robotic additive manufacturing through distributed model‐free deep reinforcement learning in computational design environments

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.

Trajectory-Based Skill Learning for Overhead Construction Robots Using Generalized Cylinders with Orientation

Overhead work involving the construction and maintenance of civil infrastructure (e.g., tunnels, overpasses, and buildings) is strenuous and fatigue-inducing for human workers and is particularly well-suited for co-robotization. Such work is typically quasi-repetitive, and on-site robots must adapt to unexpected workface conditions. Methods such as learning from demonstration can leverage human experts’ demonstration to let robots directly learn new skills to perform tasks. This paper proposes a generalized cylinders with orientation approach to teach robots how to perform quasi-repetitive overhead construction tasks from human demonstration. The demonstration trajectories are first used to construct a generalized cylinder and generate the robot trajectory. To ensure that the construction component (e.g., tunnel lining segment, building ceiling tile) being installed can satisfy the geometric constraints of the workspace, orientation constraints need to be determined, and the robot must follow such constraints. A trajectory adaptation and human-in-the-loop refinement approach are developed to refine the robot trajectory. The proposed method was evaluated in a robot simulator with variable workspace. The results showed that the proposed approach achieves an improved success rate (82.0%) compared to that demonstrated in previous work (71.3%) and enables overhead construction robots to readily adapt to new worksite conditions.