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The purpose of this paper is to provide an overview of unmanned aerial vehicle (UAV) developments, types, the major functional components of UAV, challenges, and trends of UAVs, and among the various challenges, the authors are concentrating more on obstacle sensing methods. This also highlights the scope of on-board vision-based obstacle sensing for miniature UAVs.

The paper initially discusses the basic functional elements of UAV, then considers the different challenges faced by UAV designers. The authors have narrowed down the study on obstacle detection and sensing methods for autonomous operation.

Among the various existing obstacle sensing techniques, on-board vision-based obstacle detection has better scope in the future requirements of miniature UAVs to make it completely autonomous.

The paper gives original review points by doing a thorough literature survey on various obstacle sensing techniques used for UAVs.

Fire is a common disaster. Even though simple sensors such as those detecting smoke or heat are popularly employed, they require close proximity to fire. In order to obtain more reliable and more complete information, fire detection by vision sensing has recently acquired increasing attention. In the vision-based fire sensing, colour is usually used as an important cue for flame detection. However, considering there are still a large number of black-and-white (B/W) CCTV cameras installed for security purposes, a technique that can detect flame reliably in grey-scale images will be useful to protect human lives and property from the fire disaster. The paper aims to discuss these issues.

This article describes the automatic detection of fire flames in the grey-scale image sequences by a two-level image processing scheme: pixel-level and frame-level. In pixel-level processing, an evaluation function is devised to extract pixels that possibly belong to the flame region, particularly to its boundaries. Extracted fire pixel candidates are verified in frame-level processing by monitoring their distribution variations in sequential images. A circle is fitted to the candidate pixels in each image for efficient monitoring, and the presence of flame is reasoned when the position and size of the circle increase with high fluctuations.

Experimental results show that the proposed method can detect flame quite reliably using the intensity information and its temporal variations in grey-scale image sequences.

This paper presents a novel technique of vision-based flame detection. Unlike most existing techniques, the proposed technique is based on the grey-scale images of a B/W camera. To the best of the author's knowledge, it may be the first of its kind developed for general application to indoor and outdoor scenes.

Industrial robots are extensively used in the robotic assembly of rigid objects, whereas the assembly of flexible objects using the same robot becomes cumbersome and challenging due to transient disturbance. The transient disturbance causes vibration in the flexible object during robotic manipulation and assembly. This is an important problem as the quick suppression of undesired vibrations reduces the cycle time and increases the efficiency of the assembly process. Thus, this study aims to propose a contactless robot vision-based real-time active vibration suppression approach to handle such a scenario.

A robot-assisted camera calibration method is developed to determine the extrinsic camera parameters with respect to the robot position. Thereafter, an innovative robot vision method is proposed to identify a flexible beam grasped by the robot gripper using a virtual marker and obtain the dimension, tip deflection as well as velocity of the same. To model the dynamic behaviour of the flexible beam, finite element method (FEM) is used. The measured dimensions, tip deflection and velocity of a flexible beam are fed to the FEM model to predict the maximum deflection. The difference between the maximum deflection and static deflection of the beam is used to compute the maximum error. Subsequently, the maximum error is used in the proposed predictive maximum error-based second-stage controller to send the control signal for vibration suppression. The control signal in form of trajectory is communicated to the industrial robot controller that accommodates various types of delays present in the system.

The effectiveness and robustness of the proposed controller have been validated using simulation and experimental implementation on an Asea Brown Boveri make IRB 1410 industrial robot with a standard low frame rate camera sensor. In this experiment, two metallic flexible beams of different dimensions with the same material properties have been considered. The robot vision method measures the dimension within an acceptable error limit i.e. ±3%. The controller can suppress vibration amplitude up to approximately 97% in an average time of 4.2 s and reduces the stability time up to approximately 93% while comparing with control and without control suppression time. The vibration suppression performance is also compared with the results of classical control method and some recent results available in literature.

The important contributions of the current work are the following: an innovative robot-assisted camera calibration method is proposed to determine the extrinsic camera parameters that eliminate the need for any reference such as a checkerboard, robotic assembly, vibration suppression, second-stage controller, camera calibration, flexible beam and robot vision; an approach for robot vision method is developed to identify the object using a virtual marker and measure its dimension grasped by the robot gripper accommodating perspective view; the developed robot vision-based controller works along with FEM model of the flexible beam to predict the tip position and helps in handling different dimensions and material types; an approach has been proposed to handle different types of delays that are part of implementation for effective suppression of vibration; proposed method uses a low frame rate and low-cost camera for the second-stage controller and the controller does not interfere with the internal controller of the industrial robot.

The demanding nature of construction industry poses serious health risks to construction workers. In recent years, construction health management (CHM) has gained much attention to ensure a healthier and safer workplace. However, there is still lack of a systematic review to bring together the disaggregated studies and determine the development status of this research field. As essential for addressing health issues in construction industry, a bibliometric and content-based review on of previous CHM studies would be presented in this paper.

In total, 753 journal articles published in Web of Science core collection from 1990 to 2020 were examined using a systematic review. Bibliometric analysis concentrated on the analysis of publication and citation pattern of CHM research while content analysis was employed to identify main health hazards, levels of analysis and topical focuses.

The results indicated that the USA was the leading country in this research domain. Five health hazards together with 17 research topics at different levels of analysis were classified to allow researchers to track the structure and temporal evolution of the research field. Finally, three emerging trends and a set of research agenda were proposed to guide future research directions.

It is the first to highlight the issues of occupational health management from the perspective of construction workers. It contributes to the field of construction health management by clarifying the knowledge structure, emerging trends and future research directions. It offers valuable guidance and in-depth understanding to researchers, practitioners and policymakers to further promote construction workers' health performance.

Industrial robots are extensively deployed to perform repetitive and simple tasks at high speed to reduce production time and improve productivity. In most cases, a compliant gripper is used for assembly tasks such as peg-in-hole assembly. A compliant mechanism in the gripper introduces flexibility that may cause oscillation in the grasped object. Such a flexible gripper–object system can be considered as an under-actuated object held by the gripper and the oscillations can be attributed to transient disturbance of the robot itself. The commercially available robots do not have a control mechanism to reduce such induced vibration. Thus, this paper aims to propose a contactless vision-based approach for vibration suppression which uses a predictive vibrational amplitude error-based second-stage controller.

The proposed predictive vibrational amplitude error-based second-stage controller is a real-time vibration control strategy that uses predicted error to estimate the second-stage controller output. Based on controller output, input trajectories were estimated for the internal controller of the robot. The control strategy efficiently handles the system delay to execute the control input trajectories when the oscillating object is at an extreme position.

The present controller works along with the internal controller of the robot without any interruption to suppress the residual vibration of the object. To demonstrate the robustness of the proposed controller, experimental implementation on Asea Brown Boveri make industrial robot (IRB) 1410 robot with a low frame rate camera has been carried out. In this experiment, two objects have been considered that have a low (<2.38 Hz) and high (>2.38 Hz) natural frequency. The proposed controller can suppress 95% of vibration amplitude in less than 3 s and reduce the stability time by 90% for a peg-in-hole assembly task.

The present vibration control strategy uses a camera with a low frame rate (25 fps) and the delays are handled intelligently to favour suppression of high-frequency vibration. The mathematical model and the second-stage controller implemented suppress vibration without modifying the robot dynamical model and the internal controller.

The purpose of this paper is to present an approach to improve the accuracy of estimating feature points of human body on a vision‐based motion capture system (MCS) by using the variable‐density self‐organizing map (VDSOM).

The VDSOM is a kind of self‐organizing map (SOM) and has an ability to learn training samples incrementally. The authors let VDSOM learn 3D feature points of human body when the MCS succeeded in estimating them correctly. On the other hand, one or more 3D feature point could not be estimated correctly, the VDSOM is used for the other purpose. The SOM including VDSOM has an ability to recall a part of weight vector which have learned in the learning process. This ability is used to recall correct patterns and complement such incorrect feature points by replacing such incorrect feature points with them.

Experimental results show that the approach is effective for estimation of human posture robustly compared with the other methods.

The proposed approach is interesting for the collaboration between an MCS and an incremental learning.

Construction action recognition is essential to efficiently manage productivity, health and safety risks. These can be achieved by tracking and monitoring construction work. This study aims to examine the performance of a variant of deep convolutional neural networks (CNNs) for recognizing actions of construction workers from images of signals of time-series data.

This paper adopts Inception v1 to classify actions involved in carpentry and painting activities from images of motion data. Augmented time-series data from wearable sensors attached to worker's lower arms are converted to signal images to train an Inception v1 network. Performance of Inception v1 is compared with the highest performing supervised learning classifier, k-nearest neighbor (KNN).

Results show that the performance of Inception v1 network improved when trained with signal images of the augmented data but at a high computational cost. Inception v1 network and KNN achieved an accuracy of 95.2% and 99.8%, respectively when trained with 50-fold augmented carpentry dataset. The accuracy of Inception v1 and KNN with 10-fold painting augmented dataset is 95.3% and 97.1%, respectively.

Only acceleration data of the lower arm of the two trades were used for action recognition. Each signal image comprises 20 datasets.

Little has been reported on recognizing construction workers' actions from signal images. This study adds value to the existing literature, in particular by providing insights into the extent to which a deep CNN can classify subtasks from patterns in signal images compared to a traditional best performing shallow network.

The construction industry remains one of the most hazardous industries worldwide, with a higher number of fatalities and injuries each year. The safety and well-being of workers at a job site are paramount as they face both immediate and long-term risks such as falls and musculoskeletal disorders. To mitigate these dangers, sensor-based technologies have emerged as a crucial tool to promote the safety and well-being of workers on site. The implementation of real-time sensor data-driven monitoring tools can greatly benefit the construction industry by enabling the early identification and prevention of potential construction accidents. This study aims to explore the innovative method of prototype development regarding a safety monitoring system in the form of smart personal protective equipment (PPE) by taking advantage of the recent advances in wearable technology and cloud computing.

The proposed smart construction safety system has been meticulously crafted to seamlessly integrate with conventional safety gear, such as gloves and vests, to continuously monitor construction sites for potential hazards. This state-of-the-art system is primarily geared towards mitigating musculoskeletal disorders and preventing workers from inadvertently entering high-risk zones where falls or exposure to extreme temperatures could occur. The wearables were introduced through the proposed system in a non-intrusive manner where the safety vest and gloves were chosen as the base for the PPE as almost every construction worker would be required to wear them on site. Sensors were integrated into the PPE, and a smartphone application which is called SOTER was developed to view and interact with collected data. This study discusses the method and process of smart PPE system design and development process in software and hardware aspects.

This research study posits a smart system for PPE that utilises real-time sensor data collection to improve worksite safety and promote worker well-being. The study outlines the development process of a prototype that records crucial real-time data such as worker location, altitude, temperature and hand pressure while handling various construction objects. The collected data are automatically uploaded to a cloud service, allowing supervisors to monitor it through a user-friendly smartphone application. The worker tracking ability with the smart PPE can help to alleviate the identified issues by functioning as an active warning system to the construction safety management team. It is steadily evident that the proposed smart PPE system can be utilised by the respective industry practitioners to ensure the workers' safety and well-being at construction sites through monitoring of the workers with real-time sensor data.

The proposed smart PPE system assists in reducing the safety risks posed by hazardous environments as well as preventing a certain degree of musculoskeletal problems for workers. Ultimately, the current study unveils that the construction industry can utilise cloud computing services in conjunction with smart PPE to take advantage of the recent advances in novel technological avenues and bring construction safety management to a new level. The study significantly contributes to the prevailing knowledge of construction safety management in terms of applying sensor-based technologies in upskilling construction workers' safety in terms of real-time safety monitoring and safety knowledge sharing.

The purpose of this study is to monitor the progress of construction activities in an automated way by using sensor-based technologies for tracking multiple resources that are used in building construction.

An automated on-site progress monitoring approach was proposed and a proof-of-concept prototype was developed, followed by a field experimentation study at a high-rise building construction site. The developed approach was used to integrate sensor data collected from multiple resources used in different steps of an activity. It incorporated the domain-specific heuristics that were related to the site layout conditions and method of activity.

The prototype estimated the overall progress with 95% accuracy. More accurate and up-to-date progress measurement was achieved compared to the manual approach, and the need for visual inspections and manual data collection from the field was eliminated. Overall, the field experiments demonstrated that low-cost implementation is possible, if readily available or embedded sensors on equipment are used.

Previous studies either monitored one particular piece of equipment or the developed approaches were only applicable to limited activity types. This study demonstrated that it is technically feasible to determine progress at the site by fusing sensor data that are collected from multiple resources during the construction of building superstructure. The rule-based reasoning algorithms, which were developed based on a typical work practice of cranes and hoists, can be adapted to other activities that involve transferring bulk materials and use cranes and/or hoists for material handling.