Secure Autonomous UAVs Fleets by Using New Specific Embedded Secure Elements

International audienc

Security, privacy and safety evaluation of dynamic and static fleets of drones

Inter-connected objects, either via public or private networks are the near future of modern societies. Such inter-connected objects are referred to as Internet-of-Things (IoT) and/or Cyber-Physical Systems (CPS). One example of such a system is based on Unmanned Aerial Vehicles (UAVs). The fleet of such vehicles are prophesied to take on multiple roles involving mundane to high-sensitive, such as, prompt pizza or shopping deliveries to your homes to battlefield deployment for reconnaissance and combat missions. Drones, as we refer to UAVs in this paper, either can operate individually (solo missions) or part of a fleet (group missions), with and without constant connection with the base station. The base station acts as the command centre to manage the activities of the drones. However, an independent, localised and effective fleet control is required, potentially based on swarm intelligence, for the reasons: 1) increase in the number of drone fleets, 2) number of drones in a fleet might be multiple of tens, 3) time-criticality in making decisions by such fleets in the wild, 4) potential communication congestions/lag, and 5) in some cases working in challenging terrains that hinders or mandates-limited communication with control centre (i.e., operations spanning long period of times or military usage of such fleets in enemy territory). This self-ware, mission-focused and independent fleet of drones that potential utilises swarm intelligence for a) air-traffic and/or flight control management, b) obstacle avoidance, c) self-preservation while maintaining the mission criteria, d) collaboration with other fleets in the wild (autonomously) and e) assuring the security, privacy and safety of physical (drones itself) and virtual (data, software) assets. In this paper, we investigate the challenges faced by fleet of drones and propose a potential course of action on how to overcome them.Comment: 12 Pages, 7 Figures, Conference, The 36th IEEE/AIAA Digital Avionics Systems Conference (DASC'17

Safe and Secure Support for Public Safety Networks

International audienceAs explained by Tanzi et al. in the first volume of this book, communicating and autonomous devices will surely have a role to play in the future Public Safety Networks. The “communicating” feature comes from the fact that the information should be delivered in a fast way to rescuers. The “autonomous” characteristic comes from the fact that rescuers should not have to concern themselves about these objects: they should perform their mission autonomously so as not to delay the intervention of the rescuers, but rather to assist them efficiently and reliably.</p

Internet of Robotic Things Intelligent Connectivity and Platforms

The Internet of Things (IoT) and Industrial IoT (IIoT) have developed rapidly in the past few years, as both the Internet and “things” have evolved significantly. “Things” now range from simple Radio Frequency Identification (RFID) devices to smart wireless sensors, intelligent wireless sensors and actuators, robotic things, and autonomous vehicles operating in consumer, business, and industrial environments. The emergence of “intelligent things” (static or mobile) in collaborative autonomous fleets requires new architectures, connectivity paradigms, trustworthiness frameworks, and platforms for the integration of applications across different business and industrial domains. These new applications accelerate the development of autonomous system design paradigms and the proliferation of the Internet of Robotic Things (IoRT). In IoRT, collaborative robotic things can communicate with other things, learn autonomously, interact safely with the environment, humans and other things, and gain qualities like self-maintenance, self-awareness, self-healing, and fail-operational behavior. IoRT applications can make use of the individual, collaborative, and collective intelligence of robotic things, as well as information from the infrastructure and operating context to plan, implement and accomplish tasks under different environmental conditions and uncertainties. The continuous, real-time interaction with the environment makes perception, location, communication, cognition, computation, connectivity, propulsion, and integration of federated IoRT and digital platforms important components of new-generation IoRT applications. This paper reviews the taxonomy of the IoRT, emphasizing the IoRT intelligent connectivity, architectures, interoperability, and trustworthiness framework, and surveys the technologies that enable the application of the IoRT across different domains to perform missions more efficiently, productively, and completely. The aim is to provide a novel perspective on the IoRT that involves communication among robotic things and humans and highlights the convergence of several technologies and interactions between different taxonomies used in the literature.publishedVersio

White paper - Agricultural Robotics: The Future of Robotic Agriculture

Agri-Food is the largest manufacturing sector in the UK. It supports a food chain that generates over £108bn p.a., with 3.9m employees in a truly international industry and exports £20bn of UK manufactured goods. However, the global food chain is under pressure from population growth, climate change, political pressures affecting migration, population drift from rural to urban regions and the demographics of an aging global population. These challenges are recognised in the UK Industrial Strategy white paper and backed by significant investment via a wave 2 Industrial Challenge Fund Investment (“Transforming Food Production: from Farm to Fork”). RAS and associated digital technologies are now seen as enablers of this critical food chain transformation. To meet these challenges, here we review the state of the art of the application of RAS in Agri-Food production and explore research and innovation needs to ensure novel advanced robotic and autonomous reach their full potential and deliver necessary impacts. The opportunities for RAS range from; the development of field robots that can assist workers by carrying weights and conduct agricultural operations such as crop and animal sensing, weeding and drilling; integration of autonomous system technologies into existing farm operational equipment such as tractors; robotic systems to harvest crops and conduct complex dextrous operations; the use of collaborative and “human in the loop” robotic applications to augment worker productivity and advanced robotic applications, including the use of soft robotics, to drive productivity beyond the farm gate into the factory and retail environment. RAS technology has the potential to transform food production and the UK has the potential to establish global leadership within the domain. However, there are particular barriers to overcome to secure this vision: 1.The UK RAS community with an interest in Agri-Food is small and highly dispersed. There is an urgent need to defragment and then expand the community.2.The UK RAS community has no specific training paths or Centres for Doctoral Training to provide trained human resource capacity within Agri-Food.3.While there has been substantial government investment in translational activities at high Technology Readiness Levels (TRLs), there is insufficient ongoing basic research in Agri-Food RAS at low TRLs to underpin onward innovation delivery for industry.4.There is a concern that RAS for Agri-Food is not realising its full potential, as the projects being commissioned currently are too few and too small-scale. RAS challenges often involve the complex integration of multiple discrete technologies (e.g. navigation, safe operation, multimodal sensing, automated perception, grasping and manipulation, perception). There is a need to further develop these discrete technologies but also to deliver large-scale industrial applications that resolve integration and interoperability issues. The UK community needs to undertake a few well-chosen large-scale and collaborative “moon shot” projects.5.The successful delivery of RAS projects within Agri-Food requires close collaboration between the RAS community and with academic and industry practitioners. For example, the breeding of crops with novel phenotypes, such as fruits which are easy to see and pick by robots, may simplify and accelerate the application of RAS technologies. Therefore, there is an urgent need to seek new ways to create RAS and Agri-Food domain networks that can work collaboratively to address key challenges. This is especially important for Agri-Food since success in the sector requires highly complex cross-disciplinary activity. Furthermore, within UKRI most of the Research Councils (EPSRC, BBSRC, NERC, STFC, ESRC and MRC) and Innovate UK directly fund work in Agri-Food, but as yet there is no coordinated and integrated Agri-Food research policy per se. Our vision is a new generation of smart, flexible, robust, compliant, interconnected robotic systems working seamlessly alongside their human co-workers in farms and food factories. Teams of multi-modal, interoperable robotic systems will self-organise and coordinate their activities with the “human in the loop”. Electric farm and factory robots with interchangeable tools, including low-tillage solutions, novel soft robotic grasping technologies and sensors, will support the sustainable intensification of agriculture, drive manufacturing productivity and underpin future food security. To deliver this vision the research and innovation needs include the development of robust robotic platforms, suited to agricultural environments, and improved capabilities for sensing and perception, planning and coordination, manipulation and grasping, learning and adaptation, interoperability between robots and existing machinery, and human-robot collaboration, including the key issues of safety and user acceptance. Technology adoption is likely to occur in measured steps. Most farmers and food producers will need technologies that can be introduced gradually, alongside and within their existing production systems. Thus, for the foreseeable future, humans and robots will frequently operate collaboratively to perform tasks, and that collaboration must be safe. There will be a transition period in which humans and robots work together as first simple and then more complex parts of work are conducted by robots; driving productivity and enabling human jobs to move up the value chain

Key technologies for safe and autonomous drones

Drones/UAVs are able to perform air operations that are very difficult to be performed by manned aircrafts. In addition, drones' usage brings significant economic savings and environmental benefits, while reducing risks to human life. In this paper, we present key technologies that enable development of drone systems. The technologies are identified based on the usages of drones (driven by COMP4DRONES project use cases). These technologies are grouped into four categories: U-space capabilities, system functions, payloads, and tools. Also, we present the contributions of the COMP4DRONES project to improve existing technologies. These contributions aim to ease drones’ customization, and enable their safe operation.This project has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No 826610. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Spain, Austria, Belgium, Czech Republic, France, Italy, Latvia, Netherlands. The total project budget is 28,590,748.75 EUR (excluding ESIF partners), while the requested grant is 7,983,731.61 EUR to ECSEL JU, and 8,874,523.84 EUR of National and ESIF Funding. The project has been started on 1st October 2019

Unmanned Systems Sentinel / 3 June 2016

Approved for public release; distribution is unlimited