PRE2024 3 Group7

Group

Name	Student ID	Email	Major	Ideas
Isaak Christou	1847260	i.christou@student.tue.nl	Electrical Engineering	Agricultural Weeding Robot Smart Trash Bin with AI Sorting (or no AI) Firefighting Drone Smart Walking Cane for the Visually Impaired Robotic Road Crack Detector
Luca van der Wijngaart	1565923	l.y.v.d.wijngaart@student.tue.nl	Computer Science
Daniel Morales Navarrete	1811363	d.morales.navarrete@student.tue.nl	Applied Mathematics	Robotic Beach Cleaner
Jeremiah Kamidi	1778013	j.j.kamidi@student.tue.nl	Psychology and Technology	self driving cars
Joshua Duddles	1719823	j.m.duddles@student.tue.nl	Psychology and Technology

Problem Statement

The Netherlands is currently facing a significant issue related to the aging concrete infrastructure, particularly regarding bridges, viaducts, and underpasses managed by Rijkswaterstaat. While concrete structures were initially designed to last for a long time (approximately 100 years), since around 2005, increasing traffic loads and evolving safety standards have exposed potential weaknesses in older infrastructure. The growing traffic volume and vehicle weight now exceed the original design expectations, and stricter regulations such as NEN 8700 and Eurocodes necessitate a thorough reassessment of the country's existing structures. Unlike new constructions, it is difficult to reinforce older structures, making precise recalculations essential to ensuring their safety and continued functionality. Rijkswaterstaat manages approximately 4,800 bridges and viaducts, part of the 90,000 such structures across the Netherlands, with a total replacement value of €65 billion. Most of these structures were built between 1960 and 1980, making them approximately 60 years old. Even structures such as these that have around 40 years of use left are showing concerning amounts of wear and tear. The issue is compounded by the reality that many of these aging bridges are nearing the end of their technical lifespan. This results in a significant challenge for maintenance, reinforcement, and potential replacement over the coming decades. Between 2040 and 2060, the Netherlands will face a critical challenge in replacing and renovating these aging bridges and viaducts and many of them will require major attention in the coming decades to ensure their structural safety and maintain the reliability of the country’s infrastructure. Rijkswaterstaat faces several challenges in addressing these issues:

1. Technical Challenge – Ensuring the ongoing safety and functionality of aging bridges and viaducts.
2. Future-Proofing – Adapting existing structures to meet modern usage requirements.
3. Limited Resources – A shortage of skilled professionals coupled with an increasing project workload.
4. Human Safety – Traditional inspection methods are hazardous, particularly for inspectors who need to climb or navigate dangerous parts of the bridge, and traffic closures affect public safety.

To help alleviate some of the burden on Rijkswaterstaat, our team proposes the development of a semi-automated data collection system designed specifically for the general inspecting of concrete bridges. General inspecting does not include thorough inspection methods that dive deeper into the structural state of the bridge and consists mainly of surface analysis which means the design of (NAME) can be cheaper and easier to maintain. However since there are approximately 4800 concrete bridges and they all need to be inspected at least once every 6 years Rijkswaterstaat needs to inspect 800 concrete bridges per year which is a significant workload for its current human resources.. This system, referred to as (NAME), is intended to streamline the inspection process and reduce the time and resources required for thorough assessments making inspections cheaper, faster and safer. Cheaper because there is less need for material and human resources to complete the inspection, faster and safer because there would be no need to close traffic and set up the inspection especially for difficult to access bridges like ones on top of water or ones that are too tall. (NAME) achieves this by being a wireless semi autonomous robot with instruments like high quality cameras in order to take the tens of thousands of pictures which are typical for inspections. The project will research what is required to create such a system, are drones better or should other alternatives be taken into consideration. What are good types of cameras and detections methods for structural defects, can AI be used and so on.

USE

Users

Bridge inspectors and maintenance personnel have specific needs when it comes to robotic inspection systems, as highlighted in an interview with Dick Schaafsma, the highest strategic advisor for Bridges and Viaducts at Rijkswaterstaat. He pointed out that one of the biggest challenges in bridge inspections is the necessity to close bridges for safety, which can lead to significant traffic disruptions. For instance, closing a bridge for inspection can reroute large trucks through city centers, causing potential hazards and public backlash if accidents occur. Therefore, Rijkswaterstaat seeks a system that allows for inspections without shutting down bridges. Additionally, inspecting high or waterway bridges presents challenges beyond safety, as they often require specialised equipment and can be hard to access in certain areas. The benefits of using robotic inspection systems include improved accuracy, reduced inspection time through simultaneous and more efficient inspections, and increased safety while minimising traffic disruptions. To effectively use these robotic systems, current inspectors will need training to integrate this technology into their current workflows. Nonetheless, challenges such as maintenance costs, legal restrictions on drone usage, and safety concerns about operating equipment around traffic must be addressed. Despite these challenges, Schaafsma expressed enthusiasm for the potential of robotic systems and AI to improve bridge inspections, while still emphasising the importance of having a human involved in the process to ensure reliability and effectiveness.

Society

Society and users are largely intertwined regarding this technology. Bridges in the Netherlands are under government supervision; the government is a societal stakeholder and partly a user. Dick Schaafsma emphasized that they will not directly be a user but will instead pay a company that handles the technology and then provides the information to Rijkswaterstaat, although this could change in the future if it decides to become internal. In addition to the governmental agencies that will obviously benefit from this technology, so will the general public, another significant stakeholder from a societal perspective. When correctly implemented, the general public can enjoy safer bridges and a more reliable traffic network. Road users might have concerns or questions when, for example, they soon see flying drones above the road or around the bridge conducting inspections. For this reason, it is important that the government informs communities about the benefits and implementation of this technology, as well as the associated (low) risks involved for the general public. Lastly, the technology must comply with laws, regulations, and standards that are already in place regarding safety and reliability.

Enterprise

From the enterprise perspective, we have the drone manufacturers and maintenance companies, as well as the companies that will apply the drone technology—either Rijkswaterstaat itself, if this part is internal, or another organization with extensive knowledge of drone use and its own specialized personnel. These organizations are paid by Rijkswaterstaat to monitor and inspect bridges and to provide reliable data that can be used to make informed decisions. This is the approach mentioned by Dick Schaafsma. If this inspection method proves to be cheaper or more beneficial, then the companies providing the inspection technology will become economically viable. This could potentially result in current bridge inspectors at Rijkswaterstaat being less utilized, as the inspection process becomes outsourced, thereby affecting the roles of these employees.

Objectives

• Discuss the ethical implications of semi-autonomous bridge inspection systems, particularly regarding data privacy, workforce impact, and liability in infrastructure assessment.
• Compare and research methods for detecting surface defects in concrete bridges, including high-resolution imaging, sensor-based inspection, and other non-destructive techniques.
• Develop a system for determining the severity of detected structural defects and classifying them for maintenance priority according to safety standards.
• Develop a conceptual model of a semi-autonomous robotic bridge inspection system that can efficiently collect and analyze bridge surface data.
• Research and design an AI-based system for the system's effectiveness in defect detection and classification, considering the feasibility of machine learning techniques in bridge inspection.
• Assess the feasibility of different mobility options, such as drones or other robotic systems, to determine the most suitable means of bridge inspections.
• Assess the effectiveness of the system by comparing its performance with conventional manual inspection methods.

Approach, milestones and deliverables

Our approach to reaching these objectives in regards to the problem statement and the user needs is as follows:

We want to research and design a conceptual framework of a robot that is able to detect cracks in bridges and map these out on a geographical map, for the benefit of bridge maintenance and infrastructure longevity. We will (partly) perform the first cycle of a multi-phase development cycle consisting of the following phases:

• Research & requirements gathering
  • This includes both: Conducting an interview with stakeholders in the bridge maintenance problem and research in the technology needed to build this robot.
• Sensor and other hardware selection
• Explain different types of cracks and crack detection in bridges
• Build a model/PoC (Proof of Concept)

Along these phases of our first development cycle we will set some milestones for ourselves as to keep our attention on the objectives set. This will be in the form of documentation of our work in a structured manner, making sure that the work put into each phase is represented. This documentation will in turn be part of our deliverables, as will be the model/PoC of our bridge crack detecting robot.

Technical Requirements

• Bridge condition monitoring (way to detect and take photos of cracks of 0.2mm)
• Data collection and a sufficiently large storage module (unspecified will be specified in the second interview)
• Semi automation (needs a driver but robot knows exact location and some other things about automation)
• Remote/wireless operation (real time camera for navigation and range)
• Battery life (can for example use battery packs that are swappable so no need to recharge every time)
• Cheap
• Easy to maintain/fix (can use 3d printed parts so if something breaks it is easy to fix)
• User friendly/easy to use
• Size (cant be too big)
• Integration into existing pipeline (expand)
• Able to access hard to reach parts of bridges like the underside and columns
• Safe for user and people around it (small, lightweight, consistency)
• Weight lifting capacity (the robot should be able to lift itself and the measuring equipment/cameras)

Requirements still need quite a bit more specifications

Planning

Week	General plan	Reached?
1	Problem statement Users Approach/deliverables State of the art	not reached
2	Contact users interview users Adjust week 1 content accordingly specify project	not reached
3	Interview users Adjust week 1 content accordingly and specify project Begin technical design	reached
carnival week	Finish all parts of technical design discussion and the design as a whole
4	Design limitations Second interview (feedback on first design) Technical design discussion of second design and the second design as a whole
5	Finish Technical design discussion of second design and the second design as a whole Start actual experiments
6	Actual experiments and results
7	Critical evaluation of the design performance and utility Make presentation Finish the wiki Conclusions

State of the art

Detection

3D vision technologies for a self-developed structural external crack damage recognition robot

This papers discusses the viability of multiple 3D vision techniques for detecting external cracks in infrastructure. This includes image based methods that only recently gained some adaptability, point cloud based methods that require substantial computational resources and 3D visual sensing and measuring methods such as 3D reconstruction. According to the article all methods presented lack one of three things: weight (the technology is usually to heavy), precision (to 0.1mm accuracy required for diagnosis) and robustness and accuracy. The authors then go to present a new type of automatic structural 3D crack detection system based on the fusion of high-precision LiDAR and camera which is more lightweight combines the depth sensing of LiDAR with the detailed imagery of the camera and has the required real time precision for safety diagnostics.

ROAD: Robotics-Assisted Onsite Data Collection and Deep Learning Enabled Robotic Vision System for Identification of Cracks on Diverse Surfaces

This paper discusses the architecture of ROAD (Robotics-Assisted Onsite Data Collection System) as a means of automatically detecting cracks and defects in road infrastructures. The paper looks into traditional methods of crack detection and their limitations and encourages the use of deep learning in crack detection. The paper also discusses the effectiveness of multiple deep learning algorithms in detecting cracks on roads and concludes that Xception has the best performance with an accuracy over 90% and mean square error of 0.03. More generally the paper claims that deep learning algorithm trained in good datasets outperform the traditional methods. The reason why the authors push for ROAD is due to the lack of automation when it comes to traditionally detecting cracks in roads and therefore introduces ROAD (Robotics-Assisted Onsite Data Collection System), which integrates robotic vision, deep learning, and Building Information Modeling (BIM) for real-time crack detection and structural assessment.

Novel pavement crack detection sensor using coordinated mobile robots

The paper proposes the design of an integrated unmanned ground vehicle (UGV) and drone system for real-time road crack detection and pavement monitoring. A drone conducts an initial survey using image analysis to locate potential cracks, while the UGV follows a computed path for detailed inspection using thermal and depth cameras. The collected data is processed using MATLAB and CrackIT, enhanced by a tailored image processing pipeline for improved accuracy and recall. A crowd-sourced crack database was developed to train and validate the system. Webots software was used for simulation, demonstrating the system’s effectiveness in structural health monitoring. The proposed system offers high mobility, precision, and efficiency, making it suitable for smart city applications.

Pixel-Wise Crack Detection Using Deep Local Pattern Predictor for Robot Application

This study introduces a novel crack detection method using a Convolutional Neural Network (CNN)-based Local Pattern Predictor (LPP). Unlike traditional methods that classify patches, this approach evaluates each pixel’s probability of belonging to a crack based on its local context. The proposed seven-layer CNN extracts spatial patterns, making the method robust to noise, lighting variations, and image degradation. Experiments using real-world bridge crack images demonstrate superior accuracy over existing methods (STRUM and block-wise CNN). The study also explores optimized sampling techniques and Fisher criterion-based training to enhance performance when datasets are limited. The method shows potential for real-time crack detection in robotic vision applications.

Development of AI- and Robotics-Assisted Automated Pavement-Crack-Evaluation System

The paper presents AMSEL, a semi-automated robotic platform designed to inspect pavement cracks in real-time using a deep learning model called RCDNet. The system uses both manual and automated navigation to collect data indoors and outdoors, with RCDNet detecting cracks based on image analysis. Despite some limitations, such as difficulty detecting cracks smaller than 1 mm and issues with lighting and shadow interference, the system provides an efficient alternative to manual inspections. Future improvements include integrating non-destructive testing (NDE) sensors, expanding the use of visual sensors for faster coverage, and developing deep learning models that can fuse data from multiple sources for more comprehensive defect detection.

Robotic surface exploration with vision and tactile sensing for cracks detection and characterization

The paper Robotic Surface Exploration with Vision and Tactile Sensing for Cracks Detection and Characterization suggests a hybrid approach to crack detection by complementing vision-based detection with tactile sensing. The system first employs a camera and object detection algorithm to identify potential cracks and generate a graph model of their structure. A minimum spanning tree algorithm then plans an effective exploration path for a robotic manipulator that reduces redundant movements.

To improve the accuracy of detection, a fiber-optic tactile sensor mounted on the manipulator verifies the presence of cracks, removing false positives from lighting or surface textures. Once verified, the system performs an in-depth characterization of the cracks, pulling out significant attributes such as length, width, orientation, and branching patterns. The two-sensing modality yields more precise measurements than traditional vision-only methods.

Experimental validation demonstrates that this integrated approach significantly enhances detection accuracy while reducing operating costs. By optimizing motion planning and reducing reliance on full-surface scanning, the system offers a more efficient and less expensive method of automated infrastructure inspection and maintenance.

Complete and Near-Optimal Robotic Crack Coverage and Filling in Civil Infrastructure

The paper Complete and Near-Optimal Robotic Crack Coverage and Filling in Civil Infrastructure proposes a new approach for autonomous crack inspection and repair with a simultaneous sensor-based inspection and footprint coverage (SIFC) planning scheme. The method blends real-time crack mapping and robot motion planning for effective and complete inspection. Integration of sensing and actuation through sensing and actuation integration makes the system efficient by avoiding redundant motion and providing optimal crack coverage.

The robot takes a two-step strategy, first, onboard sensors are used to detect and map cracks in real-time and calculate an optimal path of coverage using a greedy exploration algorithm. Second, a robotic manipulator follows the path and dispenses crack-filling substances where needed. The algorithm adjusts its path in real-time based on new cracks, allowing the system to react to irregular and complex surfaces without pre-computed structural maps.

Experimental results reveal that this system significantly improves the detection and effectiveness of crack repairs at a lower cost of operation. Through ensuring total crack coverage with minimal travel distance, the system outshines traditional procedures, making it a promising alternative for extensive rehabilitation of infrastructure.

Crack-pot: Autonomous Road Crack and Pothole Detection

The paper Crack-Pot: Autonomous Road Crack and Pothole Detection proposes an autonomous real-time road crack and pothole detection system using deep learning. This system employs a neural network architecture to handle road surface textures and spatial features, enabling the discrimination between damaged and undamaged areas. The approach improves the accuracy by reducing the misclassification due to environmental factors like lighting variations and surface unevenness.

The detection is carried out by capturing road images through a camera-based system mounted on an automobile or robotic platform. The images are input into a convolutional neural network (CNN) which identifies cracks and potholes based on their unique structural features. Compared with traditional thresholding-based methods, the learning-based approach is made versatile under different conditions with better robustness against occlusions, shadows, and background noise.

Experimental results show that the system achieves high accuracy of detection while operating in real-time, making it feasible for monitoring large-scale infrastructure. By automating road inspection, this method enhances efficiency and reduces the need for manual inspections, resulting in more proactive and cost-effective road maintenance procedures.

Visual Detection of Road Cracks for Autonomous Vehicles Based on DeepLearning

The research article Visual Detection of Road Cracks for Autonomous Vehicles Based on Deep Learning and Random Forest Classifier presents a high-tech image-based approach towards detecting road cracks based on the combination of deep learning and machine learning methods. The study integrates convolutional neural networks (CNNs) with a Random Forest classifier to improve accuracy in identifying faults in road surfaces. The method is intended to assist autonomous cars in driving over faulty roads while contributing to the maintenance of the infrastructure as well.

The system utilizes three state-of-the-art CNN models: MobileNet, InceptionV3, and Xception, trained on a 30,000 road image dataset. The learning rate of the network was tuned in experimentation to 0.001, yielding a maximum validation accuracy of 99.97%. The model was also tested on 6,000 additional images, where it recorded a high detection accuracy of 99.95%, demonstrating robustness under real-world conditions.

The results demonstrate the hybrid deep learning and machine learning technique significantly enhances crack detection accuracy compared to traditional methods. With its integration into autonomous vehicle technology or roadway maintenance initiatives, the technique offers a highly scalable, effective solution for real-time infrastructure monitoring and defect detection.

Article Deep Learning Based Pavement Inspection Using Self-Reconfigurable Robot

The paper Deep Learning-Based Pavement Inspection Using Self-Reconfigurable Robot introduces a robot system utilizing deep learning to conduct real-time pavement inspection and defect detection. The robotic system is centered on Panthera, a self-reconfigurable robot utilizing semantic segmentation and deep convolutional neural networks (DCNNs) for the detection of road defects and environmental obstructions such as litter.

The inspection process has two primary components: SegNet, a deep learning model that delineates pavement areas from other objects, and a DCNN-based defect detection module that detects different types of road defects. To enhance the system's usability in practical applications, it is integrated with a Mobile Mapping System (MMS) that geotags cracks and defects detected, allowing for precise location tracking. The Panthera robot has NVIDIA GPUs, which enable real-time processing and decision-making functions.

Experimental testing confirms that the system is highly accurate in detecting pavement damage and functions well under diverse urban environments. The technique not only optimizes the effectiveness of autonomous road maintenance and cleaning but also provides a scalable means for intelligent infrastructure management, reducing the need for manual inspections.

Vehicle/movement

[11] The Current Opportunities and Challenges in Drone Technology

This recently published paper discusses the the advancements that Drone Sensor Technology and Drone Communcation Systems have made, after which it defines some opportunities and challenges that the field of drone technology faces and it draws some conclusions on where it thinks this technology is headed and the general importance this field will have in certain industries.

It discusses the applications of Drone technology in the sectors Agriculture, Healthcare, and Military & Security. According to the paper, drones have already started being a critical too in the Agriculture sector as they perform crop monitoring and analysis to detect diseases early, leading to improved yields, but also Livestock monitoring by tracking movements and using thermal cameras. Healthcare has started using drones for medical supply deliveries and emergency response: drones can easily get crucial supplies to hard-to-reach areas. Drones are also being used by the military for surveillance and reconnaissance and higher precision of (air-)strikes. This leads to less colateral damage and enhances battlefield efficiency.

The paper states some opportunities pertaining to these previously mentioned sectors, but more interestingly it states some challenges that it believes drone technology faces, that can be important to many sectors besides these 3. It mentions that current regulations and legal frameworks limit the use of drones immensely, and drones are prone to cybersecurity threats, being at the risk of hacking and unauthorized control. It also names some technical limitations such as limited battery life, payload capacity and drone costs being high.

[A] Drone Technology: Types, Payloads, Applications, Frequency Spectrum Issues and Future Developments

This paper discusses various aspects of drone technology, such as types of drones, levels of autonomy, size and weight, payloads, energy sources, and future developments. Although the paper was published in 2016—9 years ago—a lot of the core technology remains the same, albeit more efficient and better built. Here, we'll summarize some parts briefly.

There are three main classes of drones: fixed-wing systems, multirotor systems, and other systems, such as ornithopters or drones using jet engines. Fixed-wing and multirotor systems are the most used and important. The first class is built for fast flight over long distances but requires a landing strip to take off and land. Benefits of the latter include reduced noise and the ability to hover in the air.

The United States Department of Defense distinguishes four levels of autonomy: human-operated systems, human-delegated systems, human-supervised systems, and fully autonomous systems. A distinction is made between autonomous systems and automatic systems: "An automatic system is a fully preprogrammed system that can perform a preprogrammed assignment on its own. Automation also includes aspects like automatic flight stabilization. Autonomous systems, on the other hand, can deal with unexpected situations by using a preprogrammed ruleset to help them make choices."

This requires energy. There are four main energy sources: traditional airplane fuel, battery cells, fuel cells, and solar cells. Airplane fuel is mainly used in large fixed-wing drones, while battery cells are the most common in smaller multirotor drones. Fuel cells are not widely used—one reason being that these types of cells are relatively heavy—so only larger fixed-wing drones can be equipped with them. Solar cells are also not often used in the drone industry. Low efficiency is one of the reasons for their limited application.

Lastly, the paper expects three major developments in the coming years in terms of drone technology, namely miniaturization (i.e., smaller and lighter drones), greater autonomy (i.e., more autonomous drones), and swarms (i.e., more drones that can communicate with each other).

[B] ANAFI Ai Photogrammetry

Parrot is a leading French drone manufacturer that focuses exclusively on professional-grade drones, offering two options: the ANAF Ai and ANAFI USA. As they say, “With our professional drones, we provide best-in-class technology for inspection, first responders, firefighters, search-and-rescue teams, security agencies, and surveying professionals.” Going into more depth, the ANAF Ai is capable of photogrammetry, which is the process of creating visual 3D models from overlapping photographs. Some key features of this drone are its 48 MP camera that can capture stills at 1 fps, compatibility with the PIX4D software suite, in-flight 4G transfer of data to the cloud, and the ability to create a flight plan with just one click. The ANAFI Ai is equipped with a camera that tilts from -90° to +90°, making it ideal to inspect the underside of bridges. Perception systems ensure the safety of the flight plan, so users don't need to worry about obstacles. The ANAFI Ai avoids them autonomously.

[C] DJI Bridge Inspection

Another leading drone manufacturer, and by far the biggest, is a Chinese company called DJI (short for Da-Jiang Innovations). This company offers an immense amount of products to the market—not just drones, but also power supplies, handheld cameras, and drive systems for e-bikes. Their primary specialization, however, is drones. Their range is vast, encompassing consumer camera drones, specialized agriculture drones for crop treatment, delivery drones, and enterprise drones for business use cases. On their website, they describe the different use cases and provide corresponding "solutions." These solutions combine a base drone platform, various payloads, software packages, and recommended workflows. For example, for bridge inspection, they provide three different solutions. One of these, their "Bridge Digital Twin Asset Management" solution, features the Matrice 350 RTK base drone (weighing approximately 6.47 kg) with payloads such as the Zenmuse P1—a 45 MP full-frame camera—and the Zenmuse L2, a LiDAR sensor. In addition, DJI Pilot 2, DJI Terra, and DJI Modify are software packages that integrate seamlessly to create an efficient workflow. Other solutions involve fewer sensors and smaller drones, allowing potential buyers to customize the possibilities.

[D] Drone-enabled bridge inspection methodology and application

This paper explores using drones for inspecting bridges as an efficient, low-cost alternative to traditional methods. With many bridges deteriorating, as noted by the ASCE, the study focuses on a timber bridge near Keystone, South Dakota, using a DJI Phantom 4. Researchers developed a five-stage inspection method based on extensive literature review and current practices. The results showed that the drone produced measurements and images comparable to those of conventional inspections while reducing time and risk to inspectors. The study demonstrates that drone technology can support legally mandated inspections and offers potential benefits in cost savings, safety, and data quality for future infrastructure assessments.

[E] Bridge Inspection with an Off-the-Shelf 360° Camera Drone

This study by Andreas Humpe examines how an off-the-shelf 360° camera drone can be used to inspect bridges. The research shows that using an easily available drone equipped with a 360° camera is a practical and cost-effective alternative to traditional inspection methods. The drone captures comprehensive, high-quality images from all directions, making it easier to spot damages and structural issues. By reducing the time and risk involved in manual inspections, this approach can improve safety and efficiency. The findings suggest that such technology could play a significant role in modernizing bridge inspection practices and supporting reliable maintenance decisions.

Communication

[12] THz band drone communications with practical antennas: Performance under realistic mobility and misalignment scenarios

This recently published paper explores the role of Terahertz (THz) band communications in 6G non-terrestrial networks (NTN), focusing on drone-based connectivity, spectrum allocation, and power optimization. Drones are expected to act as airborne base stations, enabling high-speed, ultra-reliable connectivity for applications like surveillance, sensing, and localization.

The study evaluates the true performance of THz drone links under real mobility conditions and beam misalignment, finding that while data rates of 10s to 100s of Gbps are achievable, severe performance degradation can occur due to misalignment and antenna orientation changes. It analyzes three channel selection schemes (MaxActive, Common Flat Band, and Standard) along with two power allocation strategies (Water-Filling and Equal Power), identifying a commonly available THz band for stable transmission.

The paper highlights major challenges for THz drone communications, including frequency selectivity, beam misalignment, and mobility-induced disruptions. It emphasizes the need for active beam control solutions to maintain reliable performance. While THz technology offers vast bandwidth potential, overcoming alignment and stability issues is critical for practical deployment in 6G drone networks.

[13] Redefining Aerial Innovation: Autonomous Tethered Drones as a Solution to Battery Life and Data Latency Challenges

This article explores the idea of using drones connected to a power supply through tethering it to one. It mentions that flight durations typically range between 20 and 55 minutes, and that this could result in having to frequent recharging or battery replacements which disrupts operations. Additionally it mentions that this way, communication can also be done through this tether which removes the problem of data latency and would allow for more responsive controls and transferring data like images to an external storage, removing the need of a SSD card or other storage module on the vehicle itself.

The study highlights the technological advancements that enable tethered drones to operate efficiently. Modern tether designs incorporate lightweight yet durable materials capable of transmitting power and data at high speeds. Some models use fiber optic cables to achieve data transmission rates of up to 10Gbps, significantly reducing latency. Despite these advantages, tethered drones come with their own set of challenges consisting of mobility restrictions due to the physical tether, but also the vulnerability to environmental conditions such as winds and rain. It proposes future potential developments such as improved tether materials, better autonomous navigation and integrating 5G technology. It concludes stating that it is a innovative solution in the UAV technology for applications requiring long flights in which battery life is an issue.

Main system

[14] Drone-Based Non-Destructive Inspection of Industrial Sites: A Review and Case Studies

This paper explores the increased use of unmanned aerial vehicles (UAV) for inspecting industrial sites through non-destructive inspection methods. It speaks of advantages over manual inspections performed by humans in the forms of enhanced safety, cost reduction and easier access to hard-to-reach areas. It discusses different inspection techniques like thermography, visual inspection and ultrasonic mapping. The paper also identifies challenging areas such as battery limitations, vibration effects on sensors and environmental factors affecting data accuracy.

The paper also presents different applications of these UAV inspections including bridge condition assessments and specifically mentions that drones can assist in detecting cracks, delaminations and corrosino in concrete structures such as bridges and buildings, being of great use in the field of preventive maintenance. It gives a few case studies in this bridge maintenance sector as well as other sectors, and finally emphasizes the need of further research and development in drone stability, sensor accuracy and automated defect detection algorithms.

[15] Automated wall-climbing robot for concrete construction inspection

In this article highlights the development of an automated wall-climbing robot designed for inspection of concrete structures. The way the robot sticks to a surface is with the use of a negative pressure adhesive module. A flexible skirt seal is attached at the bottom of this vacuum to prevent air from escaping and maintaining negative pressure. The robot also has wheels on the bottom. When climbing curved surfaces the negative pressure presses the robot against the surface and allows it to move over shallow grooves. The authors further specify that the robot is equipped with a RGB-D camera and deep learning algorithms to detect flaws. In the robot is a chip which communicated over the WIFI with a server with a dedicated GPU where the deep learning algorithms get applied. The camera and the motion control is connected with an USB to the chip and can be remotely controlled. When detecting the surface the robot creates a 3D surface map.

[16] Novel adhesion mechanism and design parameters for concrete wall-climbing robot

In this paper a prototype robot gets built which can climb on reinforced concrete structures using a non-contact magnetic adhesion mechanism. The robot is primarily built for non-destructive testing of the concrete. The authors argue that using such a wall-climbing robot can make inspections safer, more cost effective and more efficient. The robot has four wheels and the adhesion module fixed underneath. The authors go over the simulations they have done and eventually go for neodymium magnets with grey cast iron. The magnets are orientated in such a way that a magnetic field gets created with one side of the magnets with their north side pointed to the rebar in the concrete and another magnet with their south side pointed at the rebar. Increasing the thickness of the yoke also makes sure that the flux gets concentrated more. The eventual robot can climb a wall with just one rebar located 30 mm away from it and can attain a adhesion force of 61.8N.

[17] Deep Concrete Inspection Using Unmanned Aerial Vehicle Towards CSSC Database

In this article the authors write about an automated approach for concrete spalling and crack inspection using unmanned aerial vehicles. They also try to create an open database containing concrete spalling and cracks. The goal of the writers is to locate spalling and crack regions using 3D registration and neural networks. For the database they also used pictures from the internet. The system uses visual-SLAM to build a 3D mapping system.

Interview 1

Questions:

• General introduction of people and project.
• Getting informal consent or formal if needed GDPR compliance (can we use the interview and the answers we get, anonymity, can we record, etc.).
• Can you walk us through the entire inspection process from start to finish? What technologies, tools, and expertise are involved?
• How often are different types of structures inspected?
• For a typical inspection, how many people are involved, and what are their roles?
• What factors influence how long an inspection takes?
• Do you foresee a need for more personnel in the future, or is automation a priority?
• What is the total number of bridges and viaducts under Rijkswaterstaat’s responsibility, and how many are inspected annually?
• What types of defects are most common, and which are most critical to detect early, and which need to just be documented or monitored (introduce the structure of assessment)?
• Do inspectors currently use any AI or image processing for crack detection, or is it all visual/manual?
• How are defect reports documented? What data is collected?
• Are there known cases of critical failures or near-misses due to undetected defects?
• What are the most common failure points in aging infrastructure?
• What would an ideal crack detection tool look like in terms of usability, accuracy, and integration with current workflows?
• Would a semi-autonomous system (human-in-the-loop) or a fully autonomous one be preferable?"
• What environmental challenges should a robotic system be designed for (e.g., rain, dirt, lighting conditions)?
• What would be the biggest barriers to adopting an automated crack detection system? (is something like certification needed for drones for example?)
• What would be an acceptable price range for such a system?
• Set up date for second interview to review our design (finish design before this date and send specifications to interviewee) Date: discuss with team.

Interview Summary:

Bridges last in theory 100 years, but right now they’re already experiencing problems with bridges that are no where close to 100 years (40-60 years old). But let’s say in the most favorable situation, that a bridge lasts 100 years, then they should be replacing one bridges per week (5000 bridges, 100 years), but they’re not reaching this number by far of course. Well to form prioritization within these bridges inspections are held. Also this is a massive task for which they lack the expertise but also the funds to be upscaling greatly, which means that they’re going to have to utilize automation and maybe robotization.

Each bridge has a general inspection every 6 years, but when they see during these global inspections that a bridge is deteriorating fast, they perform specific inspections to see how urgent it really is. In these inspections they might do certain tests like a ‘hammer’ test and they try to clean something here and there.

What they’re not looking for really, is drones for usage in these 6 years-inspections, but rather for inspections in which an inspection would otherwise lead to having to obstruct bridges, redirect traffic, but also in cases where a human inspection could be dangerous. Things like a high inspection for corrosion at the top of a spanning bridge (not sure if this is the right term, a bridge that has these cables that keep it up), this would need the bridge to be obstructed or for catching nets to be installed. This all costs a lot of time and money but maybe the biggest problem is the closing and redirecting  of traffic. They’re looking for inspection and detection methods that don’t lead to traffic unsafety.

There also might be use cases for very small robots for places where humans can’t reach.

Drones on the other hand would be of great use for bridges over water: They would otherwise need a ship and the river would have to be blocked off. On the flip side, drones are difficult to use because of restrictions: For bridges near the airport, army bases, royal house, etc it is very unlikely that drones are viable since these facilities don’t allow them near them. Also flying a drone in the dark might be hard, and they do a lot of inspections at night because there is less traffic to reroute.

Scholvorming (=’Schol’forming): Something like loose pieces of concrete kind of breaking off and possibly falling. Normally in a specific/specialized inspection, they could test this by hitting a hammer on a piece of bridge and if these come loose, then there is a problem. A drone is not able to do these more physical tests.

An inspection tipically takes half a day, so they need to make sure to look at the right and important things: These areas of importance have to be defined before hand. But besides this they also look globally at it to inspect new cracks that they did not know of before. A drone would also have to do this: making a lot of pictures, but mainly of the important parts of the bridge where points of concerns lie.

An example of a more specific test is for example how the bridge reacts to vibrations of traffic. For this a drone might need certain sensors, highspeed camera’s. But on the other hand: drones are of limited utility with these specific inspections because they often need some sort of physical tests or actions are required (like the hammer test, ‘plakstrook ophangen’, or cleaning).

Internal cracks: These are not checked during global inspections but they might be during the more specific inspections when these internal cracks are suspected.

Tand-nok: Some sort of design where there are very tight nooks and crannies. These things are not included in modern designs anymore, because they’re very hard to inspect.

Every type of bridge has their own problems:

·        Moving bridges have problems with operatingsystems, malfunctions. These can also lead to bridges physically breaking down, if a break system fails it can over extend.

·        Steel bridges deal with steel fatigue

·        Concrete bridges and viaducts (they have by far the biggest number of these), can deal with problems being non-reinforced concrete, or badly reinforced concrete, which can lead to forces being to big and causing cracks and corrosion of the reinforcements (steel within concrete).               

Margins and cracks are very small: cracks of 0.2mm can be fine for now, but 0.3mm can be too big and need action. Spider cobwebs can be seen as a crack by AI. How to solve this?

First design technical specifications (discussion)

Detection methods

Based on an interview with [Name] from Rijkswaterstaat, general-purpose bridge inspections primarily involve surface-level visual assessments conducted by inspectors without the aid of advanced tools. The inspection process requires a thorough examination of the entire structure, during which inspectors capture thousands of photographs, focusing on areas prone to cracks and other signs of wear and tear. Additionally, the team was advised that the use of AI in government-related agencies presents challenges and may not be ideal. Due to these constraints, the inspection methodology is inherently limited in scope and must, at a minimum, incorporate a high-quality camera capable of capturing high-definition close-up images of cracks and defects as small as 0.2mm. It was indicated that cracks of this size begin to pose structural concerns. Furthermore, inspectors are responsible for identifying aesthetic issues, reinforcing the necessity of a high-resolution camera. The camera must also be lightweight and compact to meet the technical requirements of the inspection process.

Another interesting choice for detection methods would be the use of thermal and laser depth cameras. The temperature contrast between the interior and exterior of a crack can facilitate crack detection while providing additional insights into its shape, size, depth, and severity. Moreover, the high colour contrast generated by thermal imaging—such as infrared cameras—can simplify image processing and may prove beneficial when integrated with AI-powered image analysis models. A depth camera can further enhance assessment accuracy by estimating the approximate depth of cracks, allowing inspectors to better evaluate structural risks and distinguish genuine cracks from superficial or aesthetic surface imperfections.

In order for the detection system to work effectively using remote control some steps must be taken. The drone/grounded robot must have a low latency low-latency, first-person view (FPV) flying camera in order for the controller to be able to manually navigate if needed. The the thermal and depth cameras complement this FPV camera in detecting cracks since it is usually of limited resolution. Once a crack has been detected the high quality camera the thermal camera and the laser depth camera can be used to take pictures which are stored locally and can be downloaded once the inspection is over for further analysis and discussion.

Communication methods

ZigBee (Xbee) wireless technology protocol range/ wifi (some examples i can give to help you both can be used with microcontrollers like arduino and pi)

Movement method

Drone, grounded, battery life, how much weight it can carry, etc.

Movement

As part of our solution to the problem statement above, we defined two options of types of robots. One is a drone which is able to make tens of thousand high-quality images of the bridge, and the other is a grounded robot which is able to do the same, but would have some more difficulties with the hard-to-reach places of a bridge and reaching the undercarriage of the bridges. In this section we will discuss some advantages and disadvantages of each solution.

Drone robot

A drone has great application in bridge inspection and mapping, as the problem statement mentions that in a normal inspection, the bridge would have to be (partly) closed off for the duration of the inspection, and difficult and sometimes unsafe methods have to be used like aereal work platforms or climbing up the bridge columns. Also bridges over water can be easily inspected using drones. A drone has some limitations though, as it can only carry a limited weight and the combination of flying with this weight and many sensors requiring a power source can mean that the operating time is limited. This is one main problem that should be looked into when choosing for a drone as the method of bridge inspection.

Grounded robot

Grounded robots offer a reliable alternative for bridge inspections, especially when it comes to stability, endurance, and power availability. Unlike drones, they are not restricted by weight constraints in the same way, allowing them to carry heavier and more powerful batteries, additional sensors, and onboard computing units. Grounded robots can also operate for longer periods since they are not subjected to the high energy demands of sustained flight.

Battery and battery life

Choosing the ideal battery for your robot is crucial when it comes to optimizing its performance and longevity. Battery life depends on a few factors, and there are a few options to choose from. The main 3 types suitable for batteries in robots are: (1) Li-Ion, a lithium-ion battery (2) Li-Poly, a lithium polymer battery and (3) NiMH, a nickel-metal hybride battery. In this section we will discuss what batteries are best for what application, under which we will consider a decently sized grounded robot and a drone.

For the grounded version, an article [x] argues that based on some standard grounded vehicle without many sensors and actuators, a Li-ion battery would be good for a high energy production and a Li-po battery would be a safe option regarding chemistry build up. Since our robot would likely need a lot of sensors and highspeed camera's, choosing high energy production is preferred here. For a drone on the other hand, weight is a big constraint. NiMH is not suitable because of its inferior energy-to-weight ratio compared to lithium batteries. In his article, Radek Jarema mentions that Li-poly batteries are often chosen over Li-ion batteries in drone applications for its durable design and high discharge current.

The battery-life is important because a lot of sensors are interconnected and all require power. In our case, the battery should not drain during an inspection, because having to charge it once or twice while on site, would take a lot of time, which is what we want to limit with the use of drones. One option however is to make our design take into account this charging issue, and make the batteries easily replaceable. This would mean that for smaller bridges, the drone could perform the inspection on one battery charge, but for the bigger bridges it would have to return to the deployment site for a swapping of batteries. This would pose an extra challenge as the operating system of the robot would need to display the battery life to the operator and when to return for a battery-swap. Having stated this, once all the sensors and actuators have been selected, an analysis should be made based on the individual power consumptions of these components and it should be calculated how many batteries should be put on the robot for a certain duration of inspection. This duration of inspection should be analyzed aswell.

Weight

Weight is a crucial factor for drones but also plays a role in the design of grounded robots. For drones, weight limitations significantly impact flight duration and stability. Each added gram requires additional thrust, leading to faster battery depletion.

For drones, the main components that contribute to weight include:

• Battery pack
• High-speed cameras and sensors
• Protective casing and structural frame
• Communication (and possibly GPS) modules

A balance must be struck between weight and functionality to ensure the drone can carry out its inspection tasks without compromising flight time. Li-Poly batteries are often preferred in drones due to their high discharge rates and lightweight design, even though they have a lower energy density compared to Li-Ion batteries.

Grounded robots are less constrained by weight, but it is still a factor to consider, particularly for robots that may need to traverse challenging terrain or be lifted for deployment. A heavier battery provides longer operating time but increases motor power requirements and could limit maneuverability in certain situations. For larger, long-duration missions, battery weight distribution and energy efficiency become key design considerations.

Main body and integration

this will look into challenges in integrating a whole system, for example when detecting if we have a camera do we mount the camera on something like a servo to rotate it and get a wider field of view or will we turn the entire system body. How do we make sure we looked over the whole structure (different for autonomous or non-autonomous). Different components need different voltage levels how do we regulate that, multiple sources or one for all. Figure more stuff out.

Autonomous Flight System

Autonomous flight platforms enable drones to fly and perform tasks with minimal human intervention, which benefits applications such as infrastructure inspection, mapping, and surveillance. Autonomous flight systems integrate multiple technologies including flight control algorithms, sensor fusion, GPS navigation, AI decision-making, and obstacle detection to enable precise and trustworthy operation. Through the utilization of these advanced functions, autonomous drones can successfully pursue pre-established routes of flight, a capability very valuable for repetitive inspection operations, such as monitoring the condition of bridges and viaducts.

Inside an autonomous drone is the flight control system, commonly called the autopilot. This regulates altitude, speed, and direction, executing pre-coded flight plans automatically without manual interference. Open-source solutions such as ArduPilot and PX4 provide solutions to create customizable autonomous navigation functionality to allow flight plans to be pre-programmed. The autopilot system automatically corrects the movement of the drone through constant feedback from real-time onboard sensors to achieve stability and accuracy throughout the mission.

Autonomous drone navigation is facilitated through the use of Global Navigation Satellite Systems (GNSS), including GPS, GLONASS, and Galileo, to provide precise positioning information. For specific applications that require higher precision, such as bridge structural damage inspection, Real-Time Kinematic (RTK) GPS can be employed to provide centimeter-class accuracy. Aside from GPS usage, drones also employ LiDAR, cameras, and ultrasonic sensors to enhance localization to acclimatize to evolving conditions.

To safely navigate through complicated environments, autonomous drones must be equipped with obstacle detection and avoidance systems. These systems utilize computer vision, LiDAR scanning, and ultrasonic sensors to sense and fly around obstacles in real-time. Advanced AI algorithms process this information, enabling drones to adjust their flight paths autonomously. Some systems also utilize Simultaneous Localization and Mapping (SLAM) techniques, which allow drones to map their surroundings in real-time and navigate from that.

Another very important part of autonomous drone flight is waypoint flight, where the drone flies from a list of pre-determined GPS waypoints. Users may apply Mission Planner, QGroundControl, or some other ground station software to build flight plans with which they may input specific waypoints and set such actions as photo capture, hover, or adjustment of altitude. Drones might also apply geo-fencing to stay in predetermined airspace limits in some instances to stop unauthorized movement along paths other than their planned courses.

From a regulatory perspective, autonomous drone usage must comply with Netherlands aviation regulations, which are derived from the European Union Aviation Safety Agency (EASA) regulations. The majority of drone operations currently must follow Visual Line of Sight (VLOS) regulations, meaning that the drone must be within the direct sightline of the operator at all times. However, for totally autonomous flights that travel Beyond Visual Line of Sight (BVLOS), special authorization and risk assessments are required. Adherence to these regulations is a key step in the development of an autonomous inspection system.

First design (here we put the whole system together)

Detection Methods/Sensors

Camera/Sensor	Estimated price (€)	Dimensions (mm)	Weight (g)	Power Usage
Caddx Polar Nano Starlight FPV	130	15.8 × 14 × 14	2.7	Not specified
Intel RealSense D415 Depth Camera	140-180	99 × 20 × 23	72	1.5W
Arducam 64MP Camera	90	40 × 25 × 24	34	1.2W
FLIR Lepton 3.5	230-320	11.5 × 12.7 × 7.2	1.2	150mW

Body (Drone): (Jeremiah)

Battery pack: (Luca)

Extra features like cleaning spiderwebs or water spray (atomizer)

Water spray

From the interview it was found that inspectors often use a water sprat during inspections in order to make the concrete more visually contrast (as the concrete gets wet and then the water evaporates the water inside cracks will remain providing colour contrast). Such a system would be a good inclusion in order to make inspection easier and more accurate. In addition it can be used in conjunction with the thermal cameras to improve the temperature contrast between the inside and outside of the crack. In order to mount such a mechanism on a drone a few things are needed:

• A small water reservoir (max 100ml to limit weight)
• A mini water pump (the smaller the better to save on weight and energy usage)
• A solenoid valve in order to control the water flow remotely
• A fine mist spraying nozzle
• Tubing

Even with these considerations a system such as this although desirable by inspectors will create some technical challenges. The weight and energy usage are one but the extra structures mounted on the drone might increase drag and instability so careful considerations and a minimalist design are required.

Obstacle removal

The detection of cracks in concrete from images is only effective under two conditions: first, the pictures taken by the drone must be of a high enough resolution, and second, the cracks in the pictures need to be unobstructed by obstacles such as dust, debris, dirt, cobwebs or moss; otherwise, the AI model will be unable to detect them. In the interview, it was mentioned that this is currently done manually by a person using a brush or similar methods. This part focuses on how obstacle removal can be performed by drones, discussing various approaches and proposing an optimal method.

Suction

The suction method involves a drone equipped with a vacuum system capable of sucking away obstacles from the surface of the bridge, after which high-resolution pictures can be taken. The vacuum tube's head could also feature a brush, combining suction with mechanical removal. Unlike a regular vacuum cleaner, a drone's vacuum system does not necessarily require a storage module, as the goal is to remove obstacles from the bridge's surface rather than from the environment, saving space and weight. For example, the drone could suck away cobwebs on one side and blow them out into the air on the other side. As demonstrated in everyday life, this is a highly effective method for obstacle removal. However, it increases the drone's power consumption, reducing its flight time.

Mechanical

Mechanical removal of obstacles can be accomplished using various tools attached to the drone’s body, such as brushes or scrapers. A brush is ideal for eliminating dust, debris, dirt, and cobwebs—anything loosely attached to the surface—while a scraper works well for removing moss or other materials that are more firmly attached. Electrically powered versions, such as a rotating brush, could prove more effective, albeit with higher power demands.  

This technique, like other subsequent methods discussed in this section, might spread obstacles around rather than completely removing them, potentially making them a less effective solution as opposed to suction. Additionally, a drawback of this method is that it requires direct contact with the surface.

Water

Drones that use water to remove obstacles or clean surfaces are the most commonly implemented of the four methods in the real world. Numerous companies offer building cleaning services using drones. Most of these drones are equipped with a high-pressure jet that sprays water—sometimes mixed with a cleaning solution—to clean surfaces such as buildings, wind turbines, or billboards. The drone connects to a water supply on the ground via a hose attached at the bottom, providing it with an unlimited water source. Without this connection, carrying enough water would be too heavy and require frequent refills. This hose complicates the drone’s flight path around bridges though, and it is not the most sustainable method, as it requires large amounts of water.

Air

This last subsection briefly reviews the air blowing method. This technique utilises compressed air jets to blow obstacles away from the surface. It involves attaching air nozzles to the drone that can generate focused blasts of air, dispersing obstacles from the crack under investigation. A shortcoming of this method is its lower obstacle removal power relative to water and mechanical alternatives.

Conclusion

In conclusion, both air and suction methods show the most promise for drone-based obstacle removal in bridge inspection. Future experiments might provide a clearer preference of one method over the other. The water method is unsuitable due to weight and refill constraints associated with an onboard tank or hoses that complicate flight paths. Additionally, mechanical methods are more complex than necessary, given that the most common obstacles on concrete bridges—dirt, dust, and cobwebs, as noted in the interview—can be effectively addressed with simpler solutions, like with air and suction.

Image Recognition System for Crack Detection

Convolutional Neural Network (CNN) is a specific kind of deep learning model that is used to process and interpret visual information. Unlike conventional machine learning models, CNNs learn feature hierarchies from images with very limited feature extraction being carried out manually. A CNN consists of several layers that process an image input to give a classification output. These layers are convolutional layers, pooling layers, fully connected layers, and activation functions. The CNN model utilized for detecting cracks is a structured pipeline that enables it to extract useful features from images of a road surface.

The input layer is supplied with an image, usually reduced to a constant size and converted to grayscale for convenience. The image is held in a numeric matrix with pixel values between 0 and 255. Pixel values are normalized before processing for improved model efficiency. Convolutional layers are the backbone of CNNs. They apply a convolutional filter sliding over the input image to extract significant features such as edges, texture, and patterns. A filter glides over the image, computing a feature map highlighting specific features significant in detecting cracks. Every convolutional layer learns to identify different features of the cracks. Early layers learn basic features such as edges, and deeper layers learn more complex structures. After convolution, an activation function is applied to introduce non-linearity to the model for learning complex patterns. ReLU (Rectified Linear Unit) is most frequently utilized in CNNs. ReLU only permits positive values to be passed forward, hence making computations simple and preventing vanishing gradients. Pooling layers down sample feature maps, reducing their size in space but preserving important information. Max pooling is popular, where a maximum value in each region in a feature map is used. It reduces the computational cost and allows the model to be invariant to small displacements and distortions in the cracks. As an example, a max pool 2×2 reduces a 128×128 feature map to 64×64 with a max value in each 2×2 window. Features from convolution and pool are flattened to a one-dimensional vector and then passed to fully connected layers. These are like regular artificial neural networks, learning complex relationships between extracted features. A fully connected layer computes a weighted sum and passes it to an activation function. The final layer in the CNN produces a probability score for whether or not an image contains a crack. Since binary classification is being performed (crack or no crack), a sigmoid activation function is used, then a crack is found if greater than 0.5.

Once the CNN architecture is specified, the network is trained using a labeled database of road surface images. Training is the process of supplying images to the network and adjusting its internal parameters in order to minimize classification errors. (We can ask the users for real crack images to train the model). While the training time of the model can often be large, this is only done once. As a result, a highly reliable and efficient method to detect road cracks from images is obtained.

Add extra components here

Work Records

Week 1

Name	Hours	Work
Isaak Christou	8	Made the wiki page, problem statement, 5 relevant papers summaries in state of the art
Luca van der Wijngaart	4	Group meeting, first start to the Approach section. first 2 out of 5 papers for state of the art section.
Daniel Morales	6	Wrote objectives, found 5 relevant papers for state of the art and wrote a summary

Week 2

Name	Hours	Work
Isaak Christou	1.5	Remade problem statement
Daniel Morales	2	Remade Objectives and meeting

Week 3

Name	hours	Work
Isaak Christou	20	Meeting + made interview questions + edited the wiki for better structure Remade problem statement Made preliminary technical requirements Research on detection methods and sensors
Luca van der Wijngaart	6	Meeting, interview 1 and Movement methods of first design technical specifications, Summarized 2 more State of the Art papers
Daniel Morales	20	Meetings, review interview information, adjust objectives based, investigate and write autonomous flight system, investigate understand and write CNN for detecting road cracks on images

Week 4

Week 4

Week 5

Week 5

Week 6

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Week 8

Week 8

References

[1][2][3][4][5]

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[12] Saeed, A., Erdem, M., Gurbuz, O., & Akkas, M. A. (2024). THz band drone communications with practical antennas: Performance under realistic mobility and misalignment scenarios. Ad Hoc Networks, 166, 103644. https://doi.org/10.1016/j.adhoc.2024.103644

[13] Folorunsho, S., Norris, W., (2024) Redefining Aerial Innovation: Autonomous Tethered Drones as a Solution to Battery Life and Data Latency Challenges https://arxiv.org/html/2403.07922v1

[14] Nooralishahi O, et al (2021) Drone-Based Non-Destructive Inspection of Industrial Sites: A Review and Case Studies https://www.mdpi.com/2504-446X/5/4/106

[15] Yang, L., Li, B., Feng, J., Yang, G., Chang, Y., Jiang, B., & Xiao, J. (2022). Automated wall‐climbing robot for concrete construction inspection. Journal of Field Robotics, 40(1), 110–129. https://doi.org/10.1002/rob.22119

[16] Howlader, M. D. O. F., & Sattar, T. P. (2015). Novel adhesion mechanism and design parameters for concrete wall-climbing robot. IEEE, 267–273. https://doi.org/10.1109/intellisys.2015.7361153

[17] Yang, L., Li, B., Li, W., Liu, Z., Yang, G., & Xiao, J. (2017). A robotic system towards concrete structure spalling and crack database. 2021 IEEE International Conference on Robotics and Biomimetics (ROBIO). https://doi.org/10.1109/robio.2017.8324593

[A] Vergouw, B., Nagel, H., Bondt, G., & Custers, B. (2016). Drone technology: types, payloads, applications, frequency spectrum issues and future developments. In Information technology and law series/Information technology & law series (pp. 21–45). https://doi.org/10.1007/978-94-6265-132-6_2

[B] Parrot. (n.d.). Parrot ANAFI Ai | The 4G robotic UAV | Autonomous Photogrammetry. https://www.parrot.com/us/drones/anafi-ai/technical-documentation/photogrammetry

[C] Bridge Inspection - Infrastructure - Inspection - DJI Enterprise. (n.d.-b). DJI. https://enterprise.dji.com/inspection/bridge-inspection

[D] Seo, J., Duque, L., & Wacker, J. (2018). Drone-enabled bridge inspection methodology and application. Automation in Construction, 94, 112–126. https://doi.org/10.1016/j.autcon.2018.06.006

[E] Humpe, A. (2020). Bridge Inspection with an Off-the-Shelf 360° Camera Drone. Drones, 4(4), 67. https://doi.org/10.3390/drones4040067