In recent years, drones have increasingly supported First Responders (FRs) in monitoring incidents and providing additional information. However, analysing drone footage is time-intensive and cognitively demanding. In this research, we investigate the use of AI models for the detection of humans in drone footage to aid FRs in tasks such as locating victims. Detecting small-scale objects, particularly humans from high altitudes, poses a challenge for AI systems. We present first steps of introducing and evaluating a series of YOLOv8 Convolutional Neural Networks (CNNs) for human detection from drone images. The models are fine-tuned on a created drone image dataset of the Dutch Fire Services and were able to achieve a 53.1% F1-Score, identifying 439 out of 825 humans in the test dataset. These preliminary findings, validated by an incident commander, highlight the promising utility of these models. Ongoing efforts aim to further refine the models and explore additional technologies.
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Currently, published risk analyses for drones refer mainly to commercial systems, use data from civil aviation, and are based on probabilistic approaches without suggesting an inclusive list of hazards and respective requirements. Within this context, this paper presents: (1) a set of safety requirements generated from the application of the Systems Theoretic Process Analysis (STPA) technique on a generic small drone system; (2) a gap analysis between the set of safety requirements and the ones met by 19 popular drone models; (3) the extent of the differences between those models, their manufacturers, and the countries of origin; (4) the association of drone prices with the extent they meet the requirements derived by STPA. The application of STPA resulted in 70 safety requirements distributed across the authority, manufacturer, end user, and drone automation levels. A gap analysis showed high dissimilarities regarding the extent to which the 19 drones meet the same safety requirements. Statistical results suggested a positive correlation between drone prices and the extent that the 19 drones studied herein met the safety requirements generated by STPA, and significant differences were identified among the manufacturers. This work complements the existing risk assessment frameworks for small drones, and contributes to the establishment of a commonly endorsed international risk analysis framework. Such a framework will support the development of a holistic and methodologically justified standardization scheme for small drone flights.
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Hoofdstuk 15 15.1 Introduction 15.2 An international law perspective 15.3 The American position 15.4 International human rights developments 15.5 Effective remedy and reparations 15.6 Reflections References In the international arena there are some encouraging developments in relation to accountability and transparency for the use of armed drones. It is increasingly recognized that remote pilotless aircraft have become part of modern warfare, and that sometimes they are also used outside the context of armed conflict. Subsequently, both international humanitarian and human rights law can apply. The issue of access to justice, however, receives less explicit socio-political attention. Victims of armed remote pilotless aircraft strikes meet countless challenges in effectuating their right to an effective remedy. Often even a formal recognition that a strike has taken place is lacking. Furthermore, the states involved fail to publicly release information about their own investigations. This makes it difficult for those affected to substantiate their status as a victim and seek justice, including reparations. The international community should, in addition to urging involved states to independently and impartially investigate all armed drone strikes, ensure that access to an effective remedy for civilian victims, whether on an international, transnational or national level, becomes a reality.
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Renewable energy, particularly offshore wind turbines, plays a crucial role in the Netherlands' and EU energy-transition-strategies under the EU Green Deal. The Dutch government aims to establish 75GW offshore wind capacity by 2050. However, the sector faces human and technological challenges, including a shortage of maintenance personnel, limited operational windows due to weather, and complex, costly logistics with minimal error tolerance. Cutting-edge robotic technologies, especially intelligent drones, offer solutions to these challenges. Smaller drones have gained prominence through applications identifying, detecting, or applying tools to various issues. Interest is growing in collaborative drones with high adaptability, safety, and cost-effectiveness. The central practical question from network partners and other stakeholders is: “How can we deploy multiple cooperative drones for maintenance of wind turbines, enhancing productivity and supporting a viable business model for related services?” This is reflected in the main research question: "Which drone technologies need to be developed to enable collaborative maintenance of offshore wind turbines using multiple smaller drones, and how can an innovative business model be established for these services? In collaboration with public and private partners, Saxion, Hanze, and RUG will research the development of these collaborative drones and investigate the technology’s potential. The research follows a Design Science Research methodology, emphasizing solution-oriented applied research, iterative development, and rigorous evaluation. Key technological building blocks to be developed: • Morphing drones, • Intelligent mechatronic tools, • Learning-based adaptive interaction controllers and collaborations. To facilitate the sustainable industrial uptake of the developed technologies, appropriate sustainable business models for these technologies and services will be explored. The project will benefit partners by enhancing their operations and business. It will contribute to renewing higher professional education and may lead to the creation of spin-offs/spinouts which bring this innovative technology to the society, reinforcing the Netherlands' position as a leading knowledge economy.
Drones have been verified as the camera of 2024 due to the enormous exponential growth in terms of the relevant technologies and applications such as smart agriculture, transportation, inspection, logistics, surveillance and interaction. Therefore, the commercial solutions to deploy drones in different working places have become a crucial demand for companies. Warehouses are one of the most promising industrial domains to utilize drones to automate different operations such as inventory scanning, goods transportation to the delivery lines, area monitoring on demand and so on. On the other hands, deploying drones (or even mobile robots) in such challenging environment needs to enable accurate state estimation in terms of position and orientation to allow autonomous navigation. This is because GPS signals are not available in warehouses due to the obstruction by the closed-sky areas and the signal deflection by structures. Vision-based positioning systems are the most promising techniques to achieve reliable position estimation in indoor environments. This is because of using low-cost sensors (cameras), the utilization of dense environmental features and the possibilities to operate in indoor/outdoor areas. Therefore, this proposal aims to address a crucial question for industrial applications with our industrial partners to explore limitations and develop solutions towards robust state estimation of drones in challenging environments such as warehouses and greenhouses. The results of this project will be used as the baseline to develop other navigation technologies towards full autonomous deployment of drones such as mapping, localization, docking and maneuvering to safely deploy drones in GPS-denied areas.
Inleiding en praktijkvraag De groeiende wereldbevolking gecombineerd met de klimaatverandering zorgt voor een de noodzaak tot een duurzame voedselvoorziening (KIA missie Landbouw, voedsel & water). Een significante reductie van gewasbestrijdingsmiddelen is daarbinnen een belangrijke doelstelling. Robotica maakt als technologie motor van de precisielandbouw plant specifieke precisie-bestrijding mogelijk. Het projectconsortium onderzoekt een semiautonoom samenwerkend grond-luchtrobot platform voor de precisielandbouw. Projectdoelstelling De doelstelling van het project AGRobot Platform is dan ook: “Onderzoek de mogelijkheden van een semi-autonoom samenwerkend grond-lucht robotplatform voor de precisielandbouw”. De hoofddoelstelling wordt binnen dit project beantwoordt door de deliverables uit de volgende subdoelstellingen: 1. Case studie onderzoek naar de mogelijke voordelen van het grond-luchtrobotplatform 2. Onderzoek naar de benodigde technologieën voor een grond-luchtrobotplatform 3. Ontwikkelen van een eerste (mogelijk case-specifieke) demonstrator 4. Ontwikkelen van (nieuwe) samenwerkingsvormen. Vraagsturing & Netwerkvorming Riwo Engineering is een industriële automatiseeerder die met zijn grondrobots en control-besturingssytemen actief is in de veeteelt. DRONEXpert gebruikt hyperspectrale camera’s onder drones voor het bemeten van gewassen. Saxion mechatronica onderzoekt met de onderzoekslijn unmanned robotic systems hoe de nieuwste robotica technologieën systemen mogelijk maakt voor ongestructureerde omgevingen. De partners bezitten gezamenlijk een enorm netwerk (TValley, Space53, euRobotics) en klanten om via de case studies de kansen te achterhalen en te realiseren. Innovatie Nergens ter wereld is een samenwerkend grond-luchtrobot platform actief in de precisielandbouw. Voor OostNederland, met naast veel robotica kennis ook veel Agro-kennis, zal het project letterlijk de KIEM zijn voor nieuwe projecten waaruit de valorisatie kansen richting heel Europa gaan. Activiteitenplan & Projectorganisatie Het project wordt geleid door de lector Dr. Ir. D.A.Bekke en uitgevoerd door Abeje Mersha en Mark Reiling samen met het deelnemend MKB. Het project bestaat uit 4 werkpakketten die achtereenvolgens antwoordt geven op de gestelde subdoelstellingen. Aan elk werkpakket zijn deliverables gekoppeld.
