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This study was conducted to address the methodical shortcomings and high associated cost of understanding the use of new, poorly understood architectural spaces, such as makerspaces. The proposed quantified method of enhancing current post-occupancy evaluation (POE) practices aims to provide architects, engineers and building professionals with accessible and intuitive data that can be used to conduct comparative studies of spatial changes, understand changes over time (such as those resulting from COVID-19) and verify design intentions after construction through a quantified post-occupancy evaluation.

In this study, we demonstrate the use of ultra-wideband (UWB) technology to gather, analyze and visualize quantified data showing interactions between people, spaces and objects. The experiment was conducted in a makerspace over a four-day hackathon event with a team of four actively tracked participants.

The study shows that by moving beyond simply counting people in a space, a more nuanced pattern of interactions can be discovered, documented and analyzed. The ability to automatically visualize findings intuitively in 3D aids architects and visual thinkers to easily grasp the essence of interactions with minimal effort.

By providing a method for better understanding the spatial and temporal interactions between people, objects and spaces, our approach provides valuable feedback in POE. Specifically, our approach aids practitioners in comparing spaces, verifying design intent and speeding up knowledge building when developing new architectural spaces, such as makerspaces.

Autonomous flight of unmanned aerial vehicles (UAVs) in global position system (GPS)-denied environments has become an increasing research hotspot. This paper aims to realize the indoor fixed-point hovering control and autonomous flight for UAVs based on visual inertial simultaneous localization and mapping (SLAM) and sensor fusion algorithm based on extended Kalman filter.

The fundamental of the proposed method is using visual inertial SLAM to estimate the position information of the UAV and position-speed double-loop controller to control the UAV. The motion and observation models of the UAV and the fusion algorithm are given. Finally, experiments are performed to test the proposed algorithms.

A position-speed double-loop controller is proposed, by fusing the position information obtained by visual inertial SLAM with the data of airborne sensors. The experiment results of the indoor fixed-points hovering show that UAV flight control can be realized based on visual inertial SLAM in the absence of GPS.

A position-speed double-loop controller for UAV is designed and tested, which provides a more stable position estimation and enabled UAV to fly autonomously and hover in GPS-denied environment.

This study aims to address the issue of random movement and non coordination between docking mechanisms and locking mechanisms, and proposes a comprehensive dynamic docking control architecture that integrates perception, planning, and motion control.

Firstly, the proposed dynamic docking control architecture uses laser sensors and a charge-coupled device camera to perceive the pose of the target. The sensor data are mapped to a high-dimensional potential field space and fused to reduce interference caused by detection noise. Next, a new potential function based on multi-dimensional space is developed for docking path planning, which enables the docking mechanism based on Stewart platform to rapidly converge to the target axis of the locking mechanism, which improves the adaptability and terminal docking accuracy of the docking state. Finally, to achieve precise tracking and flexible docking in the final stage, the system combines a self-impedance controller and an impedance control algorithm based on the planned trajectory.

Extensive simulations and experiments have been conducted to validate the effectiveness of the dynamic docking system and its control architecture. The results indicate that even if the target moves randomly, the system can successfully achieve accurate, stable and flexible dynamic docking.

This research can provide technical guidance and reference for docking task of unmanned vehicles under the ground conditions. It can also provide ideas for space docking missions, such as space simulator docking.

Studies showed that construction digitalisation could prevent or mitigate accidents rate on sites. Digitalisation applications may prevent or mitigate building project collapse (BPC) but with some encumbrances, especially in developing countries. There is a paucity of research on digital technologies application to prevent or mitigate BPC in Nigeria. Thus, the research aims to explore the perceived barriers that may hinder digital technologies from preventing or mitigating building collapse and recommend measures to improve technology applications during development.

The study is exploratory because of the unexplored approach. The researchers collected data from knowledgeable participants in digitalisation and building collapse in Nigeria. The research employed a phenomenology approach and analysed collected data via a thematic approach. The study achieved saturation at the 29th interviewee.

Findings show that lax construction digitalisation implementation, absence of regulatory framework, lax policy, unsafe fieldworkers' behaviours, absence of basic infrastructure, government attitude, hesitation to implement and high technology budget, especially in developing countries, are threats to curbing building collapse menace via digitalisation. The study identified technologies relevant to preventing or mitigating building collapse. Also, it proffered measures to prevent or mitigate building collapse via improved digital technology applications during development.

This research contributes to the construction digitalisation literature, especially in developing countries, and investigates the perceived barriers that may hinder digital technologies usage in preventing or mitigating building collapse in Nigeria.

Unlike many existing methods that are primarily focused on two-dimensional localization, this research paper extended the scope to three-dimensional localization. This enhancement is particularly significant for unmanned aerial vehicle (UAV) applications that demand precise altitude information, such as infrastructure inspection and aerial surveillance, thereby broadening the applicability of UAV-assisted wireless networks.

The paper introduced a novel method that employs recurrent neural networks (RNNs) for node localization in three-dimensional space within UAV-assisted wireless networks. It presented an optimization perspective to the node localization problem, aiming to balance localization accuracy with computational efficiency. By formulating the localization task as an optimization challenge, the study proposed strategies to minimize errors while ensuring manageable computational overhead, which are crucial for real-time deployment in dynamic UAV environments.

Simulation results demonstrated significant improvements, including a channel capacity of 99.95%, energy savings of 89.42%, reduced latency by 99.88% and notable data rates for UAV-based communication with an average localization error of 0.8462. Hence, the proposed model can be used to enhance the capacity of UAVs to work effectively in diverse environmental conditions, offering a reliable solution for maintaining connectivity during critical scenarios such as terrestrial environmental crises when traditional infrastructure is unavailable.

Conventional localization methods in wireless sensor networks (WSNs), such as received signal strength (RSS), often entail manual configuration and are beset by limitations in terms of capacity, scalability and efficiency. It is not considered for 3-D localization. In this paper, machine learning such as multi-layer perceptrons (MLP) and RNN are employed to facilitate the capture of intricate spatial relationships and patterns (3-D), resulting in enhanced localization precision and also improved in channel capacity, energy savings and reduced latency of UAVs for wireless communication.

Dynamically tracking the target by unmanned ground vehicles (UGVs) plays a critical role in mobile drone recovery. This study aims to solve this challenge under diverse random disturbances, proposing a dynamic target tracking framework for UGVs based on target state estimation, trajectory prediction, and UGV control.

To mitigate the adverse effects of noise contamination in target detection, the authors use the extended Kalman filter (EKF) to improve the accuracy of locating unmanned aerial vehicles (UAVs). Furthermore, a robust motion prediction algorithm based on polynomial fitting is developed to reduce the impact of trajectory jitter caused by crosswinds, enhancing the stability of drone trajectory prediction. Regarding UGV control, a dynamic vehicle model featuring independent front and rear wheel steering is derived. Additionally, a linear time-varying model predictive control algorithm is proposed to minimize tracking errors for the UGV.

To validate the feasibility of the framework, the algorithms were deployed on the designed UGV. Experimental results demonstrate the effectiveness of the proposed dynamic tracking algorithm of UGV under random disturbances.

This paper proposes a tracking framework of UGV based on target state estimation, trajectory prediction and UGV predictive control, enabling the system to achieve dynamic tracking to the UAV under multiple disturbance conditions.