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The purpose of this paper is to present a novel approach to the design of an autonomous Unmanned Aerial Vehicle (UAV) to aid with the internal inspection and classification of tall or large structures. Focusing mainly on the challenge of robustly determining the position and velocity of the UAV, in three dimensional space, using on‐board Simultaneous Localisation and Mapping (SLAM). Although capable of autonomous flight, the UAV is primarily intended for semi‐autonomous operation, where the operator instructs the UAV where to go. However, if communications with the ground station are lost, it can backtrack along its path until communications are re‐established.

A UAV has been designed and built using primarily commercial‐off‐the‐shelf components. Software has been developed to allow the UAV to operate autonomously, using solely the on‐board computer and sensors. It is currently undergoing extensive flight tests to determine the performance and limitations of the system as a whole.

Initial test flights have proven the presented approach and resulting real‐time SLAM algorithms to function robustly in a range of large internals. The paper also briefly discusses the approach used by similar projects and the challenges faced.

The proposed novel algorithms allow for on‐board, real‐time, three‐dimensional SLAM in unknown and unstructured environments on a computationally constrained UAV.

The purpose of this paper is to describe the specification language TML for adaptive mission plans that the authors designed and implemented for the open-source framework Aerostack for aerial robotics.

The TML language combines a task-based hierarchical approach together with a more flexible representation, rule-based reactive planning, to facilitate adaptability. This approach includes additional notions that abstract programming details. The authors built an interpreter integrated in the software framework Aerostack. The interpreter was validated with flight experiments for multi-robot missions in dynamic environments.

The experiments proved that the TML language is easy to use and expressive enough to formulate adaptive missions in dynamic environments. The experiments also showed that the TML interpreter is efficient to execute multi-robot aerial missions and reusable for different platforms. The TML interpreter is able to verify the mission plan before its execution, which increases robustness and safety, avoiding the execution of certain plans that are not feasible.

One of the main contributions of this work is the availability of a reliable solution to specify aerial mission plans, integrated in an active open-source project with periodic releases. To the best knowledge of the authors, there are not solutions similar to this in other active open-source projects. As additional contributions, TML uses an original combination of representations for adaptive mission plans (i.e. task trees with original abstract notions and rule-based reactive planning) together with the demonstration of its adequacy for aerial robotics.

The humanitarian supply chain (HSC) area is rich with conceptual frameworks with a focus on the importance of information and digital technology (IDT) applications. These frameworks have a limited scope in investigating and prioritizing barriers to IDT adoption in HSCs. The present study thus identifies and prioritizes the barriers to IDT adoption in organizations involved in HSCs.

By using a literature review allied with expert discussions and a fuzzy analytic hierarchy process (F-AHP), the study identifies and prioritizes a comprehensive set of barriers that organizations involved in HSCs may consider to improve IDT adoption.

The study investigates five main barriers (strategic, organizational, technological, financial and human) interlocked with 25 sub-barriers impacting the level of IDT adoption in organizations involved in HSCs. The findings indicate that strategic barriers (SBs) are of greatest importance, followed by organizational, technological, financial and human barriers. The findings indicate the difference in ranking barriers influencing the adoption of IDTs in HSCs compared to the commercial supply chain.

Although a three-step method adopted for this study is rigorous in terms of the way this research is conducted, it is essential to report that prioritization is based on the subjective opinions of the experts.

The findings aim to assist policymakers and practitioners in developing effective strategies to improve IDT adoption in organizations engaged in HSCs. Moreover, the prioritization of barriers provides a systematic way to overcome any barriers to improve HSC performance.

This study is first of its kind that investigates and prioritizes the barriers to IDT adoption in HSCs.

The purpose of this paper is to present a new data set of aerial imagery from robotics simulator (AIR). AIR data set aims to provide a starting point for localization system development and to become a typical benchmark for accuracy comparison of map-based localization algorithms, visual odometry and SLAM for high-altitude flights.

The presented data set contains over 100,000 aerial images captured from Gazebo robotics simulator using orthophoto maps as a ground plane. Flights with three different trajectories are performed on maps from urban and forest environment at different altitudes, totaling over 33 kilometers of flight distance.

The review of previous research studies show that the presented data set is the largest currently available public data set with downward facing camera imagery.

This paper presents the problem of missing publicly available data sets for high-altitude (100‒3,000 meters) UAV flights; the current state-of-the-art research studies performed to develop map-based localization system for UAVs depend on real-life test flights and custom-simulated data sets for accuracy evaluation of the algorithms. The presented new data set solves this problem and aims to help the researchers to improve and benchmark new algorithms for high-altitude flights.

Reviews a presentation by Professor Robert Michelson of Georgia Institute of Technology in the USA about the growing interest in unmanned aerial vehicles, which can be used for military purposes, but also for tasks like traffic surveillance. Michelson has a particular interest in micro aerial vechiles, and he has a team at Georgia Tech Research Institute developing an insect‐like flapping wing vehicle called an “entomopter”, which is expected to fly in 1998. Michelson has also instituted a series of international aerial robotics competitions, of which the most ambitious it to be held in the millennial year. The competition will simulate search and rescue operations in a disaster situation. The rules are published and qualifying tests will begin in 1998.

The purpose of this paper is to present a localization and mapping data set acquired by a fixed-wing unmanned aerial vehicle (UAV). The data set was collected for educational and research purposes: to save time in dealing with hardware and to compare the results with a benchmark data set. The data were collected in standard Robot Operating System (ROS) format. The environment, fixed-wing, and sensor configuration are explained in detail. GPS coordinates of the fixed-wing are also available as ground truth. The data set is available for download (www.ece.unb.ca/COBRA/open_source.htm).

The data were collected in standard ROS format. The environment, fixed-wing, and sensor configuration are explained in detail.

The data set can be used for target localization and mapping. The data were collected to assist algorithm developments and help researchers to compare their results. Robotic data sets are specifically important when they are related to unmanned systems such as fixed-wing aircraft.

The Robotics Data Set Repository (RADISH) by A. Howard and N. Roy hosts 41 well-known data sets with different sensors; however, there is no fixed-wing data set in RADISH. This work presents two data sets collected by a fixed-wing aircraft using ROS standards. The data sets can be used for target localization and SLAM.

Rapid 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.

A 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.

This 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.

The 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.

Rapid 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.