Browsing by Author "Afsari, Kereshmeh"

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    Construction inspection & monitoring with quadruped robots in future human-robot teaming: A preliminary study
    Halder, Srijeet; Afsari, Kereshmeh; Chiou, Erin; Patrick, Rafael; Hamed, Kaveh Akbari (Elsevier, 2023-04-15)
    Construction inspection and monitoring are key activities in construction projects. Automation of inspection tasks can address existing limitations and inefficiencies of the manual process to enable systematic and consistent construction inspection. However, there is a lack of an in-depth understanding of the process of construction inspection and monitoring and the tasks and sequences involved to provide the basis for task delegation in a human-technology partnership. The purpose of this research is to study the conventional process of inspection and monitoring of construction work currently implemented in construction projects and to develop an alternative process using a quadruped robot as an inspector assistant to overcome the limitations of the conventional process. This paper explores the use of quadruped robots for construction inspection and monitoring with an emphasis on a human-robot teaming approach. Technical development and testing of the robotic technology are not in the scope of this study. The results indicate how inspector assistant quadruped robots can enable a human-technology partnership in future construction inspection and monitoring tasks. The research was conducted through on-site experiments and observations of inspectors during construction inspection and monitoring followed by a semi-structured interview to develop a process map of the conventional construction inspection and monitoring process. The study also includes on-site robot training and experiments with the inspectors to develop an alternative process map to depict future construction inspection and monitoring work with the use of an inspector assistant quadruped robot. Both the conventional and alternative process maps were validated through interview surveys with industry experts against four criteria including, completeness, accuracy, generalizability, and comprehensibility. The findings suggest that the developed process maps reflect existing and future construction inspection and monitoring work.
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    Human-Interactions with Robotic Cyber-Physical Systems (CPS) for Facilitating Construction Progress Monitoring
    Halder, Srijeet (Virginia Tech, 2023-08-23)
    Progress monitoring in construction involves a set of inspection tasks with repetitive in-person observations on the site. The current manual inspection process is time-consuming, inefficient, inconsistent, and has many safety risks to project inspectors. Cyber-Physical Systems (CPS) are networks of integrated physical and cyber components, such as robots, sensors, actuators, cloud computing, artificial intelligence, and the building itself. Introducing CPS for construction progress monitoring can reduce risks involved in the process, improve efficiency, and enable remote progress monitoring. A robotic CPS uses a robot as the core component of the CPS. But human interaction with technology plays an important role in the successful implementation of any technology. This research studied the human-centered design of a CPS from a human-computer interaction perspective for facilitating construction progress monitoring that puts the needs and abilities of humans at the center of the development process. User experience and interactions play an important role in human-centered design. This study first develops a CPS framework to autonomously collect visual data and facilitate remote construction progress monitoring. The two types of interactions occur between the human and the CPS – the human provides input for the CPS to collect data referred to as mission planning, and CPS provides visual data to enable the human to perform progress analysis. The interaction may occur through different modalities, such as visual, tactile, auditory, and immersive. The goal of this research is to understand the role of human interactions with CPS for construction progress monitoring. The study answers five research questions – a) What robotic CPS framework can be applied in construction progress monitoring? b) To what extent is the proposed CPS framework acceptable as an alternative to traditional construction progress monitoring? c) How can natural interaction modalities like hand gestures and voice commands be used as human-CPS interaction modalities for the proposed CPS? d) How does the human interaction modality between the proposed CPS and its user affect the usability of the proposed CPS? e) How does the human interaction modality between CPS and its user affect the performance of the proposed CPS?. To answer the research questions, a mixed-method-based methodology is used in this study. First, a systematic literature review is performed on the use of robots in inspection and monitoring of the built environment. Second, a CPS framework for remote progress monitoring is developed and evaluated in lab conditions. Third, a set of industry experts experienced with construction progress monitoring are interviewed to measure their acceptance of the developed CPS and to collect feedback for the evaluation of the CPS. Fourth, two methodologies are developed to use hand gesture and voice command recognition for human-CPS interaction in progress monitoring. Fifth, the usability and performance of the CPS are measured for identified interaction modalities through a human subject study. The human subjects are also interviewed post-experiment to identify the challenges they faced in their interactions with the CPS. The study makes the following contributions to the body of knowledge – a) key research areas and gaps were identified for robots in inspection and monitoring of the built environment, b) a fundamental framework for a robotic CPS was developed to automate reality capture and visualization using quadruped robots to facilitate remote construction progress monitoring, c) factors affecting the acceptance of the proposed robotic CPS for construction progress monitoring were identified by interviewing construction experts, d) two methodologies for using hand gestures and voice commands were developed for human-CPS interaction in construction progress monitoring, e) the effect of human interaction modalities on the usability and performance of the proposed CPS was assessed in construction progress monitoring through user studies, f) factors affecting the usability and performance of the proposed CPS with different interaction modalities were identified by conducting semi-structured interviews with users.
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    Impact of Interactive Holographic Learning Environment for bridging Technical Skill Gaps of Future Smart Construction Engineering and Management Students
    Ogunseiju, Omobolanle Ruth (Virginia Tech, 2022-07-25)
    The growth in the adoption of sensing technologies in the construction industry has triggered the need for graduating construction engineering students equipped with the necessary skills for deploying the technologies. For construction engineering students to acquire technical skills for implementing sensing technologies, it is pertinent to engage them in hands-on learning with the technologies. However, limited opportunities for hands-on learning experiences on construction sites and in some cases, high upfront costs of acquiring sensing technologies are encumbrances to equipping construction engineering students with the required technical skills. Inspired by opportunities offered by mixed reality, this study presents an interactive holographic learning environment that can afford learners an experiential opportunity to acquire competencies for implementing sensing systems on construction projects. Firstly, this study explores the required competencies for deploying sensing technologies on construction projects. The current state of sensing technologies in the industry and sensing technology education in construction engineering and management programs were investigated. The learning contents of the holographic learning environment were then driven by the identified competencies. Afterwards, a learnability study was conducted with industry practitioners already adopting sensing technologies to assess the learning environment. Feedback from the learnability study was implemented to further improve the learning environment after which a usability evaluation was conducted. To investigate the pedagogical value of the learning environment in construction education, a summative evaluation was conducted with construction engineering students. This research contributes to the definition of the domain-specific skills required of the future workforce for implementing sensing technologies in the construction industry and how such skills can be developed and enhanced within a mixed reality learning environment. Through concise outline and sequential design of the user interface, this study further revealed that knowledge scaffolding can improve task performance in a holographic learning environment. This study contributes to the body of knowledge by advancing immersive experiential learning discourses previously confined by technology. It opens a new avenue for both researchers and practitioners to further investigate the opportunities offered by mixed reality for future workforce development.
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    Leveraging Artificial Intelligence for Improving Students' Noticing of Practice during Virtual Site Visits
    Olayiwola, Johnson Tumininu (Virginia Tech, 2023-01-11)
    Complementing the theoretical concepts taught in the classroom with practice has been known to enhance students' contextual understanding of the subject matter. Exposing students to practical knowledge is crucial as employers are expressing discontent with the skills of newly hired graduates. In construction education, site visits have been identified as one of the most effective tools to support theory with practice. While site visits allow students to observe construction projects and engage with field personnel, numerous barriers limit its use as an effective educational tool. For instance, there are safety, cost, schedule, and weather constraints, in addition to the logistics of accommodating large class sizes. As a result, instructors employ videos of construction projects as an alternative to physical site visits. However, videos alone are insufficient to draw students' attention to essential practice concepts. Annotations can be used to attract students' attention to practical knowledge while reducing distractions and assumptions. Leveraging on the recent progress in computer vision techniques, this study presents an AI-annotated video learning tool that instructors can utilize to equip students with practice knowledge when there is limited access to physical construction sites. First, this study investigated the construction practice concepts that industry practitioners would want students to know when engaging them in site visits. Afterward, the design and development of the AI-annotated learning tool were guided by the identified practice concepts, cognitive theory of multimedia learning, and dual coding theory. To determine if the learning tool can call students' attention to annotated practice concepts in videos, a usability evaluation was conducted. Finally, this research investigated the influence of individual differences that could contribute to how learners notice practice concepts in videos. This study contributes to the body of knowledge by identifying what construction professionals notice about their work and what they would like students to notice about construction practice. This study reveals that annotations of learning contents in construction videos can direct students' focus to the annotated contents, thereby contributing to the cognitive theory of multimedia learning and dual coding theory. By leveraging machine learning classification algorithms, this research identified the extent to which individual differences such as gender, academic program, and cognitive load can be detected from the ways students notice information in construction videos. Results from this research provide opportunities for researchers to further advance the potential of annotated videos in the construction domain and other fields that employ video as a learning tool.
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    Real-Time and Remote Construction Progress Monitoring with a Quadruped Robot Using Augmented Reality
    Halder, Srijeet; Afsari, Kereshmeh; Serdakowski, John; DeVito, Stephen; Ensafi, Mahnaz; Thabet, Walid (MDPI, 2022-11-19)
    Construction progress monitoring involves a set of inspection tasks with repetitive in-person observations on the site. The current manual inspection process in construction is time-consuming, inefficient and inconsistent mainly due to human limitations in the ability to persistently and accurately walkthrough the job site and observe the as-built status of which robots are considerably better. Enabling the process of visual inspection with a real-time and remote inspection capability using robots can provide more frequent and accessible construction progress data for inspectors to improve the quality of inspection and monitoring. Also, integrating remote inspection with an Augmented Reality (AR) platform can help the inspector to verify as-planned BIM data with the as-built status. This paper proposes a new approach to perform remote monitoring of the construction progress in real-time using a quadruped robot and an AR solution. The proposed computational framework in this study uses a cloud-based solution to integrate the quadruped robot’s control for remote navigation through the construction site with 360° live-stream video of the construction status, as well as a real-time AR solution to visualize and compare the as-built status with as-planned BIM geometry. The implementation of the proposed framework is discussed, and the developed framework is evaluated in two use cases through experimental investigations.
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    Robots in Inspection and Monitoring of Buildings and Infrastructure: A Systematic Review
    Halder, Srijeet; Afsari, Kereshmeh (MDPI, 2023-02-10)
    Regular inspection and monitoring of buildings and infrastructure, that is collectively called the built environment in this paper, is critical. The built environment includes commercial and residential buildings, roads, bridges, tunnels, and pipelines. Automation and robotics can aid in reducing errors and increasing the efficiency of inspection tasks. As a result, robotic inspection and monitoring of the built environment has become a significant research topic in recent years. This review paper presents an in-depth qualitative content analysis of 269 papers on the use of robots for the inspection and monitoring of buildings and infrastructure. The review found nine different types of robotic systems, with unmanned aerial vehicles (UAVs) being the most common, followed by unmanned ground vehicles (UGVs). The study also found five different applications of robots in inspection and monitoring, namely, maintenance inspection, construction quality inspection, construction progress monitoring, as-built modeling, and safety inspection. Common research areas investigated by researchers include autonomous navigation, knowledge extraction, motion control systems, sensing, multi-robot collaboration, safety implications, and data transmission. The findings of this study provide insight into the recent research and developments in the field of robotic inspection and monitoring of the built environment and will benefit researchers, and construction and facility managers, in developing and implementing new robotic solutions.
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    Routing and Control of Unmanned Aerial Vehicles for Performing Contact-Based Tasks
    Anderson, Robert Blake (Virginia Tech, 2021-05-05)
    In this dissertation, two main topics are explored, the vehicle routing problem (VRP) and model reference adaptive control (MRAC) for unknown nonlinear systems. The VRP and its extension, the split delivery VRP (SVRP), are analyzed to determine the effects of using two different objective functions, the total cost objective, and the last delivery objective. A worst-case analysis suggests that using the SVRP can improve total costs by as much as a factor of 2 and the last delivery by a factor that scales with the number of vehicles over the classical VRP. To test the theoretical worst-cases against the solutions of benchmark datasets, a heuristic is developed based on embedding a random variable neighborhood search within an iterative local search heuristic. Results suggest that the split deliveries do in fact improve total cost and last delivery times over the classical formulation. The SVRP has been developed classically for use with vehicles such as trucks which have large payload capacities and typically long ranges for deliveries, but are limited to traversing on roads. Unmanned aerial vehicles (UAVs) are useful for their high maneuverability, but suffer from limited capacity for payloads and short ranges. The classical SVRP formulation is extended to one more suitable for UAVs by accounting for limited range, limited payloads, and the ability to swap batteries at known locations. Instead of Euclidean distances, path plans which are adjusted for a known, constant wind underlie the cost matrix of the optimization problem. The effects of payload on the vehicle's range are developed using propeller momentum theory, and simulations verify that the proposed approach could be used in a realistic scenario. Two novel MRAC laws are then developed. The first, MRAC laws for prescribed performance, exploits barrier Lyapunov functions and a 2-Layer approach to guarantee user-defined performance. This control law allows unknown nonlinear systems to verify a user-defined rate of convergence of the tracking error while verifying apriori control and tracking error constraints. Numerical simulations are performed on the roll dynamics of a delta-wing aircraft. The second novel MRAC law is MRAC for switched dynamical systems which is proven in two different mathematical frameworks. Applying the Caratheodory framework, it is proven that if the switching signal has an arbitrarily small, but non-zero, dwell-time, then solutions of both the trajectory tracking error's and the adaptive gains' dynamics exist, are unique, and are defined almost everywhere, and the trajectory tracking error converges asymptotically to zero. Employing the Filippov framework, it is proven that if the switching signal is Lebesgue integrable and has countably many points of discontinuity, then maximal solutions of both the trajectory tracking error and the adaptive gains dynamics exist and are defined almost everywhere, and the trajectory tracking error converges to zero asymptotically. The proposed MRAC law is experimentally verified in the case where a UAV with tilting propellers is tasked with mounting an unknown camera onto a wall. The previous results are then combined into a novel application in construction. A method for using a UAV to measure autonomously the moisture of an exterior precast concrete envelope is developed which can provide data feedback through contact-based measurements to improve safety and real-time data acquisition through the integration with the Building Information Model (BIM). To plan the path of the vehicle, the path planning and SVRP for UAV approaches developed in previous chapters are utilized. To enable the UAS to contact surfaces, a switched MRAC law is employed to control the vehicle throughout and guarantee successful measurements. A full physics-based simulation environment is developed, and the proposed framework is used to simulate taking multiple measurements.
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    Toward a Data-Driven Template Model for Quadrupedal Locomotion
    Fawcett, Randall T.; Afsari, Kereshmeh; Ames, Aaron D.; Hamed, Kaveh A. (IEEE, 2022-07-01)
    This work investigates a data-driven template model for trajectory planning of dynamic quadrupedal robots. Many state-of-the-art approaches involve using a reduced-order model, primarily due to computational tractability. The spirit of the trajectory planning approach in this work draws on recent advancements in the area of behavioral systems theory. Here, we aim to capitalize on the knowledge of well-known template models to construct a data-driven model, enabling us to obtain an information rich reduced-order model. In particular, this work considers input-output states similar to that of the single rigid body model and proceeds to develop a data-driven representation of the system, which is then used in a predictive control framework to plan a trajectory for quadrupeds. The optimal trajectory is passed to a low-level and nonlinear model-based controller to be tracked. Preliminary experimental results are provided to establish the efficacy of this hierarchical control approach for trotting and walking gaits of a high-dimensional quadrupedal robot on unknown terrains and in the presence of disturbances.
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    Towards Immersive Virtual Environments using 360 Cameras for Human Building Interaction Studies
    Amezquita Radillo, Esteban (Virginia Tech, 2022-05-11)
    Virtual Reality has been growing in popularity and demand as technology has been substantially improved and become more readily available to the general public in the recent years. Similarly, the Architecture, Engineering and Construction industries have benefited from these advances and extensive research has been performed to adopt and streamline its utilization. An example of this adoption has been the use of Immersive Virtual Environments (IVE) as a representation of the built environment for different purposes such as building design and occupant behavior studies in the post construction stage – i.e., Human Building Interaction. This research has investigated a workflow for different alternatives of reality-capturing-based technologies that have been tested to generate a more realistic representation of the built environment regarding HBI. One of these alternatives considered was 360-image based IVEs. This alternative in particular was tested and compared by the means of a preliminary user study in order to evaluate whether it is an adequate representation of the built environment regarding HBI, and how it is compared to commonly used benchmarked Graphical based IVEs. Ultimately, participants of this user study reported a strong feeling of immersion and presence in the 360-image based IVE and showed a better performance in cognitive tasks such as reading speed and comprehension. In contrast, participants showed a better performance in object identification and finding in the Graphical based IVE. The results of our preliminary user study indicate that 360-image based IVEs could potentially be an adequate representation in the study of Human Building Interaction based on these metrics. Further research with a larger sample size should be done in performed in order to generalize any findings.
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    Unmanned Aerial Manipulators in Construction - Opportunities and Challenges
    Nagori, Chinmay (Virginia Tech, 2020-12-23)
    Unmanned Aerial Vehicles (UAVs) have now been accepted as an alternative medium to human workers for data collection processes in various industries. The capabilities of UAVs are now being extended from passive tasks of data collection to active tasks of interacting with the environment by equipping them with robotic arms and function as Unmanned Aerial Manipulators (UAMs). Research on Unmanned Aerial Manipulators has been growing in the last few years. The applications of UAMs in terms of sensor installation, inspections, door opening, valve turning, pick and drop, etc. have been studied for the oil and gas industry, and civil applications, etc. However, there is a lack of studies in understanding applications of UAMs and their capabilities in construction and in advancing construction activities. The goal of this research is to identify potential opportunities and challenges of the application of UAM in construction projects. The study will undertake an extensive literature review and semi-structured interviews with industry experts to address research questions. This study will have a significant contribution to the introduction and development of new contact-based UAV-guided technologies in construction.
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    Work Order Prioritization Using Neural Networks to Improve Building Operation
    Ensafi, Mahnaz (Virginia Tech, 2022-10-20)
    Facility management involves a variety of processes with a large amount of data for managing and maintaining facilities. Processing and prioritizing work orders constitute a big part of facility management, given the large number of work orders submitted daily. Current practices for prioritizing work orders are mainly user-driven and lack consistency in collecting, processing, and managing a large amount of data. Decision-making methods have been used to address challenges such as inconsistency. However, they have challenges, including variations between comparisons during the actual prioritization task as opposed to those outside of the maintenance context. Data-driven methods can help bridge the gap by extracting meaningful and valuable information and patterns to support future decision-makings. Through a review of the literature, interviews, and survey questionnaires, this research explored different industry practices in various facilities and identified challenges and gaps with existing practices. Challenges include inconsistency in data collection and prioritizing work orders, lack of data requirements, and coping strategies and biases. The collected data showed the list of criteria and their rankings for different facilities and demonstrated the possible impact of facility type, size, and years of experience on criteria selection and ranking. Based on the results, this research proposed a methodology to automate the process of prioritizing work orders using Neural Networks. The research analyzed the work order data obtained from an educational facility, explained data cleaning and preprocessing approaches, and provided insights. The data exploration and preprocessing revealed challenges such as submission of multiple work orders as one, missing data for certain criteria, long durations for work orders' execution, and lack of correlation between collected criteria and the schedule. Through hyperparameter tuning, the optimum neural network configuration was identified. The developed neural network predicts the schedule of new work orders based on the existing data. The outcome of this research can be used to develop requirements and guidelines for collecting and processing work order data, improve the accuracy of work order scheduling, and increase the efficiency of existing practices using data-driven approaches.