Article
Engineering, Electrical & Electronic
Takuma Nemoto, Shunsuke Nansai, Shohei Iizuka, Masami Iwase, Hiroshi Itoh
Summary: This paper presents an approach to estimate the window shape for glass facade-cleaning robots to adapt to different buildings. A window scanning robot with a 2D laser range scanner is used for the experiment, and a method for window shape estimation is proposed, which includes robot's pose estimation with an extended Kalman filter (EKF) and loop closure based on the estimated pose. The effectiveness of the approach is demonstrated through an experiment on a placed window. The results show that the window scanning robot can accurately acquire the shape of the window surface and increase the accuracy of the estimation.
Article
Automation & Control Systems
Hobyeong Chae, Garam Park, Jiseok Lee, Kyungmin Kim, Taegyun Kim, Hwa Soo Kim, TaeWon Seo
Summary: To address the risks and inefficiencies of facade cleaning of high-rise buildings, researchers have proposed an improved design featuring modularity, passive obstacle overcoming mechanism, and compensation for lateral disturbance. Experimental results show enhanced performance of the robot in cleaning various scenarios, including obstacle overcoming and lateral disturbance compensation.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2021)
Article
Automation & Control Systems
Hobyeong Chae, Yecheol Moon, KyungUk Lee, SungJun Park, Hwa Soo Kim, TaeWon Seo
Summary: This article introduces a novel wall-cleaning robot Edelstro-M2, with the ability to perform both vertical and horizontal movements on walls and without the need for additional infrastructure. Real-world experiments have demonstrated the robot's mobility and cleaning performance.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2022)
Article
Automation & Control Systems
Geono Kim, Hoon Chung, Baek-Kyu Cho
Summary: With the recent advancements in delivery service robots, the necessity for stair climbing in indoor and outdoor environments has increased. The study developed MOBINN, a mobile balancing robot capable of climbing stairs, using novel flexible wheels and a head structure that maintains stability during stair climbing.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Robotics
Mohamed M. Khalil, Tomoaki Mashimo
Summary: This study presents an insect-scale wheeled climbing robot that utilizes low-cost dry adhesive technology to meet important requirements such as surface-to-surface transitions, vertical and inverted locomotion, and high payload capacity. The robot achieves high torque-to-weight ratio with two micro-geared ultrasonic motors, allowing for vertical and inverted locomotion while carrying a high payload capacity.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Robotics
Tingting Guo, Xiuyan Liu, Tongfu He, Dalei Song
Summary: This article proposes an underwater climbing adsorption robot that utilizes negative pressure adsorption and synchro-drive locomotion mechanisms to achieve maneuverability. The robot can navigate complex paths and balance adsorption and motion through discrete and continuous locomotion modes. It has the advantages of non-contact, high adsorption, and mobility, making it suitable for challenging operations on various underwater structural surfaces.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Engineering, Multidisciplinary
Max Austin, Ashley Chase, Brian Van Stratum, Jonathan E. Clark
Summary: This study investigates multi-modal limb locomotion and develops a limb aquatic-scansorial multi-modal robot.
BIOINSPIRATION & BIOMIMETICS
(2023)
Article
Robotics
Liang Yang, Bing Li, Jinglun Feng, Guoyong Yang, Yong Chang, Biao Jiang, Jizhong Xiao
Summary: This paper introduces a wall-climbing robot for metric concrete inspection that can detect and measure surface flaws and generate defect-highlighted 3D models. The robot has good operational performance and can work under low illuminated and texture-less environments. Additionally, the paper provides a publicly accessible concrete structure dataset.
JOURNAL OF FIELD ROBOTICS
(2023)
Article
Automation & Control Systems
Rui Li, Yang Liu, Ai Guo, Mengjie Shou, Mingfu Zhao, Dong Zhu, Ping-an Yang, Chul-Hee Lee
Summary: This study proposes an inchworm-like climbing robot based on synergistic cable-driven grippers and a telescopic torso, aiming to solve the critical problems of weak grasping, large size, and a lack of self-perception capability in climbing robots. The robot's structure was designed by analyzing the movements of primate hands and inchworms, and the soft grippers and telescopic torso were developed to mimic their respective motions. Flexible sensors were integrated into the grippers and torso for self-perception, and the experimental results show that the robot can climb pipes with different roughness and diameters at stable speeds.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2023)
Article
Engineering, Environmental
Jiaxin Wu, Wenfei Ai, Kai Hou, Chaofan Zhang, Yue Long, Kai Song
Summary: Researchers have developed a light-driven dynamic adsorption/desorption soft suction cup inspired by rock-climbing fish, which utilizes gas-liquid phase changes to generate suction and thrust. By integrating the soft suction cup with a shape memory alloy, a soft climbing robot capable of precise, facile, and programmed climbing on tilted or slippery surfaces via remote light manipulation has been developed. The findings are expected to advance the application of light-driven soft robots on such surfaces.
CHEMICAL ENGINEERING JOURNAL
(2023)
Article
Engineering, Mechanical
Ori Inbar, David Zarrouk
Summary: This paper presents an analytical and experimental study on a reconfigurable field robot that can climb inside circular and rectangular pipes. The robot's ability to adapt to different pipe sizes is achieved through mechanisms that allow it to change its width, height, and center of mass. The study includes kinematic modeling, force analysis, and strategies for driving, climbing, and transitioning between modes. A control algorithm is also designed to automate the robot's movements inside pipes.
MECHANISM AND MACHINE THEORY
(2022)
Article
Computer Science, Artificial Intelligence
Son Thanh Nguyen, Hung Manh La
Summary: This paper introduces a new adaptable tank-like robot design for climbing on steel structures to collect data and perform inspections. The robot is able to climb on different steel structural shapes by using reciprocating mechanism and magnetic roller-chains. It has been rigorously tested and proven to be stable and reliable, integrating multiple sensors for comprehensive data collection.
JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS
(2021)
Article
Chemistry, Multidisciplinary
Mingjie Zheng, Dongkai Wang, Dekuan Zhu, Shuhong Cao, Xiaohao Wang, Min Zhang
Summary: This study develops a piezo-based soft robot with bioinspired footpads that demonstrate superior climbing performance on various surfaces and terrains. With unprecedented directional friction, the robot is able to complete rigorous climbing tasks and adapt to rough and changing surfaces. This soft robot paves the way for improved mobility and navigation of soft robots in challenging terrains.
ADVANCED FUNCTIONAL MATERIALS
(2023)
Article
Robotics
Xingguo Song, Xiaolong Zhang, Xiangyin Meng, Chunjun Chen, Dashan Huang
Summary: This paper proposes gait optimization for a hexapod robot to climb steps at different heights, enabling it to climb steps 3.9 times the length of its legs. By adjusting body inclination and using reversed claw-shape legs, the robot can overcome high obstacles and improve climbing height.
JOURNAL OF FIELD ROBOTICS
(2022)
Article
Engineering, Marine
Lepeng Chen, Rongxin Cui, Weisheng Yan, Hui Xu, Haiyan Zhao, Haoquan Li
Summary: Underwater robot has potential to clean ship hull fouling, but design and control are challenging. Use of negative pressure and magnetic adsorption technologies limit utility on rough hulls and warships. We propose a robot with thrusters and crawler belts to adapt to common hulls. A climbing controller guides robot to cover hull without repetition or omission, overcoming difficulties of inconsistent crawler belts and inaccurate depth measurement. Field experiments on giant ship verify effectiveness of proposed solution and controller.
Article
Robotics
Phone Thiha Kyaw, Anh Vu Le, Prabakaran Veerajagadheswar, Mohan Rajesh Elara, Theint Theint Thu, Nguyen Huu Khanh Nhan, Phan Van Duc, Minh Bui Vu
Summary: This article proposes a novel algorithm for planning energy-efficient and collision-free paths for reconfigurable robots. The algorithm combines BIT*, an informed planner, with energy-based objectives that consider the energy cost of each reconfigurable action. Additionally, it improves the convergence rate of the algorithm by enhancing the direct sampling technique of informed RRT*. Experimental results on a tetromino hinged-based reconfigurable robot demonstrate the effectiveness of the proposed path planning technique.
IEEE TRANSACTIONS ON ROBOTICS
(2022)
Article
Automation & Control Systems
Madan Mohan Rayguru, Balakrishnan Ramalingam, Mohan Rajesh Elara
Summary: The article introduces an adaptive control technique based on singular perturbation to address the issue of actuator saturation in industrial and robotic applications. By utilizing a high gain dynamic controller to enforce time scale separation, the technique ensures tracking performance and compensates for uncertainties. The methodology is also extended to multi input multi output systems like mobile robots, showing effectiveness through numerical simulations.
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING
(2022)
Article
Engineering, Electrical & Electronic
Zhenyu Weng, Huiping Zhuang, Haizhou Li, Balakrishnan Ramalingam, Rajesh Elara Mohan, Zhiping Lin
Summary: This paper proposes a new online multi-face tracking method, OMTMCM, which improves tracking performance by utilizing both face and body information. The method consists of two stages: detection alignment and detection association. In the first stage, a detection alignment module is used to align face and body detections from the same person. In the second stage, a cascaded matching module associates face detections across frames using past face and body features. Experimental results show that OMTMCM performs on par with or better than other online tracking methods for multi-face tracking.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2023)
Article
Chemistry, Analytical
Yin Jia, Balakrishnan Ramalingam, Rajesh Elara Mohan, Zhenyuan Yang, Zimou Zeng, Prabakaran Veerajagadheswar
Summary: This research presents a deep-learning-based context-aware multi-level information fusion framework for autonomous mobile cleaning robots to detect and avoid hazardous objects with a higher confidence level. The proposed algorithm improves the detection and avoidance of low-feature and occluded hazardous objects in an indoor environment. Experimental results show that the algorithm detects low-feature and occluded hazardous objects with a higher confidence level than conventional object detectors, achieving an average detection accuracy of 88.71%.
Article
Mathematics
Sathian Pookkuttath, Mohan Rajesh Elara, Madan Mohan Rayguru, Zaki Saptari Saldi, Vinu Sivanantham, Balakrishnan Ramalingam
Summary: This work presents the design methodology of an autonomous steam mopping robot named 'Snail', which is eco-friendly and efficient in cleaning and disinfecting indoor floors. The robot's design includes mechanical systems, hardware and software architecture, and motion control system to ensure smooth maneuverability. It is ideal for hospitals and eldercare centers, promoting hygiene and avoiding harsh chemicals.
Article
Mathematics
Manivannan Kalimuthu, Thejus Pathmakumar, Abdullah Aamir Hayat, Prabakaran Veerajagadheswar, Mohan Rajesh Elara, Kristin Lee Wood
Summary: Reconfigurable robots based on polyominos or n-Omino are increasingly being explored in cleaning and maintenance tasks. This paper proposes a new strategy to select the optimal morphologies of n-Omino-based robots that maximize area coverage and minimize energy consumption.
Article
Mathematics
Yin Jia, Prabakaran Veerajagadheswar, Rajesh Elara Mohan, Balakrishnan Ramalingam, Zhenyuan Yang
Summary: Floor-cleaning robots are being used in public places to maintain cleanliness. However, operating in dynamic environments can be risky. This study proposes a context avoidance framework using sound-based deep learning to detect specific sounds and avoid hazardous areas. The model uses a spectrogram as input and produces two outputs for classification and localization. The system achieved a high success rate in detecting and avoiding escalator sounds in real-world scenarios.
Article
Computer Science, Artificial Intelligence
Lim Yi, Ash Yaw Sang Wan, Anh Vu Le, Abdullah Aamir Hayat, Q. R. Tang, Rajesh Elara Mohan
Summary: To enhance the efficiency of area coverage in complex and confined environments, a complete coverage path planning algorithm for omnidirectional self-reconfigurable robots with varying width is proposed. The algorithm leverages the flexibility of the robot's variable width to cover wide areas quickly and navigate through tight spaces. It generates a global path that determines the robot's width to increase area coverage in open areas and reduce the footprint in tight spaces.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Sathian Pookkuttath, Braulio Felix Gomez, Mohan Rajesh Elara, Pathmakumar Thejus
Summary: Autonomous professional cleaning robots are widely used in large indoor workplaces today to improve hygiene and productivity. However, the current manual supervision and periodic maintenance methods make it challenging to detect failures in advance, leading to higher costs and safety risks. This study proposes an automated Condition Monitoring (CM) system using a novel vibration-based method to predict abnormal vibration sources and enhance Condition-based Maintenance (CbM) and operational safety.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Mathematics
Sathian Pookkuttath, Prabakaran Veerajagadheswar, Mohan Rajesh Elara
Summary: This work proposes an AI-enabled automated condition monitoring framework using two heterogeneous sensor datasets to accurately predict the sources of anomalous vibration in mobile robots. It triggers proper maintenance or corrective actions based on the condition of the robot's health or workspace, allowing for condition-based maintenance. The proposed method shows significantly higher accuracy (98.4%) compared to previous studies, and it is fast, suitable for the environment, and ideal for real-time applications in enhancing mobile robots' productivity and operational safety.
Article
Mathematics
Sathian Pookkuttath, Raihan Enjikalayil Abdulkader, Mohan Rajesh Elara, Prabakaran Veerajagadheswar
Summary: This study presents an AI-enabled condition monitoring and vibrotactile haptic-feedback-based real-time control framework for outdoor mobile robots. It involves developing a 1D CNN model for predicting system degradation and terrain flaws, and a wearable haptic feedback device for remote control. The proposed framework was validated through three field case studies, demonstrating its effectiveness.
Article
Mathematics
Manivannan Kalimuthu, Abdullah Aamir Hayat, Thejus Pathmakumar, Mohan Rajesh Elara, Kristin Lee Wood
Summary: This study proposes a systematic approach for generating optimal morphologies that can improve the performance of reconfigurable robots. By using transformation design principles and reinforcement learning techniques, the optimal hinge angles for a given task are identified by maximizing a reward signal.
Article
Mathematics
Sathian Pookkuttath, Povendhan Arthanaripalayam Palanisamy, Mohan Rajesh Elara
Summary: This study proposes a novel CM approach for outdoor mobile robots using a 3D LiDAR to extract vibration-indicated data and predicts vibration threshold classes using a 1D CNN-based model. A threshold class mapping framework is developed to generate a real-time 3D Condition-based Maintenance (CbM) map. The results show high accuracy and validate the suitability of the proposed framework for outdoor mobile robots.
Article
Mathematics
Muhammad Ilyas, Anirudh Krishna Lakshmanan, Anh Vu Le, Mohan Rajesh Elara
Summary: This article describes a deep learning approach using a convolutional neural network (CNN)-based robot operation system (ROS) framework for staircase detection, localization, and maneuvering. The presented approach achieves high accuracy and recall in detecting and locating staircases, making it efficient for autonomous floor-cleaning robots to climb stairs and clean multi-floor environments.
Article
Automation & Control Systems
Madan Mohan Rayguru, Spandan Roy, Lim Yi, Mohan Rajesh Elara, Simone Baldi
Summary: Reconfigurable robots have advantages in cleaning tasks due to their better area coverage and adaptability. However, the changes in robot dynamics caused by configuration changes are often not considered in control design. This paper proposes a switched uncertain Euler-Lagrangian model to embed configuration changes in control design, and a novel switched adaptive design for trajectory tracking. The proposed approach is implemented and validated on a self-reconfigurable pavement cleaning mobile robot.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)
Article
Construction & Building Technology
Jia Liang, Qipeng Zhang, Xingyu Gu
Summary: A lightweight PCSNet-based segmentation model is developed to address the issues of insufficient performance in feature extraction and boundary loss information. The introduction of generalized Dice loss improves prediction performance, and the visualization of class activation mapping enhances model interpretability.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Gilsu Jeong, Minhyuk Jung, Seongeun Park, Moonseo Park, Changbum Ryan Ahn
Summary: This study introduces a contextual audio-visual approach to recognize multi-equipment activities in tunnel construction sites, improving monitoring effectiveness. Tested against real-world operation data, the model achieved remarkable results, emphasizing the potential of contextual multimodal models in enhancing operational efficiency in complex construction sites.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Jin Wang, Zhigao Zeng, Pradip Kumar Sharma, Osama Alfarraj, Amr Tolba, Jianming Zhang, Lei Wang
Summary: This study presents a dual-path network for pavement crack segmentation, combining Convolutional Neural Network (CNN) and transformer. A lightweight CNN encoder is used for local feature extraction, while a novel transformer encoder integrates high-low frequency attention mechanism and efficient feedforward network for global feature extraction. Additionally, a complementary fusion module is introduced to aggregate intermediate features extracted from both encoders. Evaluation on three datasets confirms the superior performance of the proposed network.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Pierre Gilibert, Romain Mesnil, Olivier Baverel
Summary: This paper introduces a flexible method for crafting 2D assemblies adaptable to various geometric assumptions in the realm of sustainable construction. By utilizing digital fabrication technologies and optimization approaches, precise control over demountable buildings can be achieved, improving mechanical performance and sustainability.
AUTOMATION IN CONSTRUCTION
(2024)
Review
Construction & Building Technology
Jorge Loy-Benitez, Myung Kyu Song, Yo-Hyun Choi, Je-Kyum Lee, Sean Seungwon Lee
Summary: This paper discusses the advancement of tunnel boring machines (TBM) through the application of artificial intelligence. It highlights the significance of AI-based management subsystems for automatic TBM operations and presents recent contributions in this field. The paper evaluates modeling, monitoring, and control subsystems and suggests research paths for integrating existing management subsystems into TBM automation.
AUTOMATION IN CONSTRUCTION
(2024)
Review
Construction & Building Technology
Alireza Shamshiri, Kyeong Rok Ryu, June Young Park
Summary: This paper reviews the application of text mining and natural language processing in the construction field, highlighting the need for automation and minimizing manual tasks. The study identifies potential research opportunities in strengthening overlooked construction aspects, coupling diverse data formats, and leveraging pre-trained language models and reinforcement learning.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Zhengyi Chen, Hao Wang, Keyu Chen, Changhao Song, Xiao Zhang, Boyu Wang, Jack C. P. Cheng
Summary: This study proposes an improved coverage path planning system that leverages building information modeling and robotic configurations to optimize coverage performance in indoor environments. Experimental validation shows the effectiveness and applicability of the system. Future research will focus on further enhancing coverage ratio and optimizing computation time.
AUTOMATION IN CONSTRUCTION
(2024)
Review
Construction & Building Technology
Yonglin Fu, Junjie Chen, Weisheng Lu
Summary: This study presents a review of human-robot collaboration (HRC) in modular construction manufacturing (MCM), focusing on tasks, human roles, and interaction levels. The review found that HRC solutions are applicable to various MCM tasks, with a primary focus on timber component production. It also revealed the diverse collaborative roles humans can play and the varying levels of interaction with robots.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Qiong Liu, Shengbo Cheng, Chang Sun, Kailun Chen, Wengui Li, Vivian W. Y. Tam
Summary: This paper presents an approach to enhance the path-following capability of concrete printing by integrating steel cables into the printed mortar strips, and validates the feasibility and effectiveness of this approach through experiments.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Honghu Chu, Lu Deng, Huaqing Yuan, Lizhi Long, Jingjing Guo
Summary: The study proposes a method called Cascade CATransUNet for high-resolution crack image segmentation. This method combines the coordinate attention mechanism and self-cascaded design to accurately segment cracks. Through a customized feature extraction architecture and an optimized boundary loss function, the proposed method achieves impressive segmentation performance on HR images and demonstrates its practicality in UAV crack detection tasks.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Daniel Lamas, Andres Justo, Mario Soilan, Belen Riveiro
Summary: This paper introduces a new method for creating synthetic point clouds of truss bridges and demonstrates the effectiveness of a deep learning approach for semantic and instance segmentation of these point clouds. The proposed methodology has significant implications for the development of automated inspection and monitoring systems for truss bridges.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Kahyun Jeon, Ghang Lee, Seongmin Yang, Yonghan Kim, Seungah Suh
Summary: This study proposes two enhanced unsupervised text classification methods for domain-specific non-English text. The results of the tests show that these methods achieve excellent performance on Korean building defect complaints, outperforming state-of-the-art zero-shot and few-shot text classification methods, with minimal data preparation effort and computing resources.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Yoonhwa Jung, Julia Hockenmaier, Mani Golparvar-Fard
Summary: This study introduces a transformer-based natural language processing model, UNIfORMATBRIDGE, that automatically labels activities in a project schedule with Uniformat classification. Experimental results show that the model performs well in matching unstructured schedule data to Uniformat classifications. Additionally, the study highlights the importance of this method in developing new techniques.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
De-Graft Joe Opoku, Srinath Perera, Robert Osei-Kyei, Maria Rashidi, Keivan Bamdad, Tosin Famakinwa
Summary: This paper introduces a digital twin technology combining Building Information Modelling and the Internet of Things for the construction industry, aiming to optimize building conditions. The technology is implemented in a university library, successfully achieving real-time data capture and visual representation of internal conditions.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Zaolin Pan, Yantao Yu
Summary: The construction industry faces safety and workforce shortages globally, and worker-robot collaboration is seen as a solution. However, robots face challenges in recognizing worker intentions in construction. This study tackles these challenges by proposing a fusion method and investigating the best granularity for recognizing worker intentions. The results show that the proposed method can recognize multi-granular worker intentions effectively, contributing to seamless worker-robot collaboration in construction.
AUTOMATION IN CONSTRUCTION
(2024)
