Datasets and processing methods for boosting visual inspection of civil infrastructure: A comprehensive review and algorithm comparison for crack classification, segmentation, and detection

Drones, also known as unmanned aerial vehicles (UAVs), have gained numerous applications due to their low cost, ease of use, vertical takeover and landing, and ability to operate in high-risk or hard-to-reach areas. The contribution of this review is that of building the bridge between drone technology and its application and advancements in the field of Geographic Information System (GIS). The integration of drones and GIS is valuable as it reduces costs and improves accessibility for geospatial data collection. Traditional methods involving aircraft for aerial photography are expensive, requiring the hiring of aircraft, pilots, and photographers. Drones equipped with advanced cameras and artificial intelligence software can replace the conventional technique and at the same time, be economical and time-efficient. The integration of drones and GIS is expected to bring revolutionary benefits in the fields of precision agriculture, urban planning, emergency health response, disaster management, the development of smart cities, food delivery, etc. In this paper, a state-of-the-art review of the deployment of drone-integrated GIS applications in different fields is presented. Numerous techniques and associated challenges related to their development, formulation, implementation, and regulation are highlighted. It has been concluded that drone-integration solutions in GIS improve efficiency and accuracy, enhance the decision-making process, and facilitate better real-time monitoring. The findings of this review paper are intended to help and benefit researchers, business developers, emergency service providers, industrialists, and policymakers.

Advancements and Applications of Drone-Integrated Geographic Information System Technology—A Review

Digital-PV: A digital twin-based platform for autonomous aerial monitoring of large-scale photovoltaic power plants

Deploying aerial swarm robotic systems in real-life scenarios can be challenging. Using them to monitor wildfires requires the system to be easily used by a swarm operator. To achieve this with the minimum associated costs, advanced frameworks must be developed to optimise, monitor, and control the swarm in real time. One approach to achieve this is the creation of a digital twin where physical counterparts can be mirrored in a virtual world. Our aim was to create a digital twin to support and accelerate the design, testing and deployment of aerial swarms. Our framework is composed of the following main parts: a digital twin system for development and optimisation of swarm algorithms as well as real-time monitoring and control of swarm deployments; a cloud infrastructure to allow data passing between our system components; and a swarm of uncrewed aerial vehicles (UAVs). We developed a user interface that allows a swarm operator to define missions for a swarm to monitor areas in search for a digital wildfire. We tested our system in field trials using a mix of three physical and three digital aircraft. During the trials an operator was able to task the UAVs to perform autonomous search amongst three different search strategies, to stack at specific locations, and finally to land. 

Digital twin technology for wildfire monitoring using UAV swarms

Abstract This paper presents the development of algorithms for high-level control and intelligent path planning of multi-rotor aerial vehicles (MAVs) in the tasks of inspecting civil infrastructure. After revisiting the multicopter modeling, we describe the hierarchy of high-level control for MAVs and develop optimization algorithms for generating optimal paths and enabling automatic flight during inspection tasks, making use of the digital twin technology. A co-simulation framework is then established to simulate and evaluate inspection mission scenarios, integrating these essential components. Real-world examples from built infrastructure illustrate this concept. An advantage of this approach is its ability to rigorously test, validate, verify, and evaluate MAV operations under abnormal conditions without requiring physical implementation or field tests. This significantly reduces testing efforts throughout the development cycle, ensuring optimal cooperation, safety, smoothness, fault tolerance, and energy efficiency. The methodology is validated through simulations and real-world inspection of a monorail bridge. 

Intelligent path planning for civil infrastructure inspection with multi-rotor aerial vehicles

This work applies the information integration of the geographic information system (GIS) to the physical world and expands the digital virtual environment in reality with accuracy, reaching the level of the digital twin. This technology has dramatically enhanced the on-site planning, data collection, and result visualization of inspection tasks. It covers a set of virtual environment development processes, including exploring open GIS data, deploying them in the simulator, using GIS-based real-world environment transformation techniques, and collecting data with drones. The developed method has also been validated with field tests. The research can enhance the deployment of unmanned aerial vehicles (UAVs) in inspection tasks.

Sim-in-Real: Digital Twin Based UAV Inspection Process

Synergistic Temporal Logic Planning for an Aerial Gripper

Beyond programming robots to accomplish a single high-level task at a time, people also hope robots follow instructions and complete a series of tasks while meeting their requirements. This paper presents an interactive software system that consists of a multiple-task linear temporal logic (LTL) path planner and a human-machine interface (HMI). The HMI transforms human oral instructions into task commands that can be understood by the machine. The planner grows a rapid random exploring tree to search for solutions for multiple tasks. When switching tasks, the search tree is re-initialized and reconnected to utilize the information gathered during the exploration of the workspace. The feasibility of the improved planner is theoretically guaranteed, and profiling in simulation shows an acceleration in planning. An experiment with a quadcopter is conducted to show that the combination of the multiple-task LTL planner and the HMI results in a synergistic effect in real-world applications.

An Interactive System for Multiple-Task Linear Temporal Logic Path Planning

A Self-Evolving Digital Twin System Architecture for Indoor UAV Management Based on Online Learning

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Sim-in-Real: Digital Twin Based UAV Inspection Process

Synergistic Temporal Logic Planning for an Aerial Gripper

An Interactive System for Multiple-Task Linear Temporal Logic Path Planning

A Self-Evolving Digital Twin System Architecture for Indoor UAV Management Based on Online Learning