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Unmanned Aerial Vehicles: COMP4DRONES (ECSEL JU)

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Unmanned Aerial Vehicles: COMP4DRONES (ECSEL JU)
Reda Nouacer (Researcher, CEA)

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Unmanned Aerial Vehicles: COMP4DRONES (ECSEL JU)

  1. 1. Transport Session Framework of key enabling technologies for safe and autonomous drones’ applications Réda NOUACER CEA LIST reda.nouacer@cea.fr
  2. 2. Agenda 1. Drone Market 2018-2024 2. Unlocking potential value of drones 3. COMP4DRONES project 4. Objectives 5. Pilots 6. Conclusion – Expected Impact
  3. 3. 3
  4. 4. 4 Problem • Adoption of civilian drones will be challenging due to the complexity of physical environments where they evolve. • Unmanned aerial vehicles (UAV) only add value to the user if there are ways to process data quickly and without putting additional efforts into this process. • The faster, the more accurate, and the easier the data can be evaluated, the better. Answer • Combining drones and artificial intelligence seems to be the answer. • AI in relation to Machine Learning, Deep Learning, and Motion Planning are the hottest topics. Unlocking potential value of drones “Drones technology can only unlock its full potential when data acquisition and data analytics happen at the highest degree of automation.”
  5. 5. 5 Drones and AI – Motion Planning Machine Perception (MP) UAV must be able to perceive and absorb the environment or objects in some way (electro-optical, stereo-optical, and LiDAR) Computer Vision (CV) Automatic extraction, analysis, and understanding of useful information from one or more images Machine/Deep Learning (ML/DL) To optimize differentiable parameters, techniques of Machine Learning can be applied. ML algorithms are designed in such a way that they can learn and improve over time when exposed to new data. DL is a subset of ML in which neural networks learn from vast amounts of data. Inspect & Avoid the blades of the vertical wind turbine
  6. 6. 6 Drones & AI - Drone Data Analytics “Actionable” data. One of the most important goals of AI in the drone industry is to make efficient use of large data sets which are collected by the drone. • Availability of high-resolution images used for various tasks such as maintenance, surveying, mapping and monitoring is increasing. • There are already many established software companies on the market offering intelligent data analysis solutions to make unstructured drone data “actionable” and gain meaningful insights without time-consuming manual analytics. Making unstructured drone data “actionable”
  7. 7. 7 COMP4DRONES Project Running Time 01/10/2019 – 30/09/2022 ECSEL JU* under GA N° 826610 Start date: Duration: Total costs: Total Funding (60%) Number of participants: Number of countries: 01 October 2019 36 months 29.76 M€ 17.88 M€ 49 08 The ECSEL Joint Undertaking - the Public-Private Partnership for Electronic Components and Systems – funds Research, Development and Innovation projects for world-class expertise in these key enabling technologies, essential for Europe's competitive leadership in the era of the digital economy.
  8. 8. 8 Four primary safety concerns 1. The "inability to recognize" and "avoid other aircraft" and "airborne objects" in a manner similar to manned aircraft. 2. A lack of "technological" and "operational standards" needed to guide safe and consistent performance of UAVs 3. Vulnerabilities in the command and control. (e.g. "GPS-jamming, hacking", and the potential for "cyber-terrorism") 4. A lack of comprehensive "government regulations" necessary to safely facilitate the accelerated integration of UAVs into the national airspace system Source: EASA, SESAR, GAO, UK House Of Lords Safety issues surrounding UAVs
  9. 9. 9 Easing the integration and customization of drone embedded system Enabling drones to take safe autonomous decisions Ensure the deployment of trusted communications Minimizing the design and verification efforts for complex drone applications Ensuring sustainable impact and creation of an industry-driven community Project Objectives COMP4DRONES will develop key technologies to provide secure and autonomous drones for complex applications in the fields of transportation, construction, logistics, surveillance and agriculture.
  10. 10. 10 Enabling drones to take safe autonomous decisions Design and develop a safe and reconfigurable components for the navigation subsystem for enhancing the capability to autopilot a drone with novel perception capacities and smart algorithms Sensors State Prediction Conflict Detection Conflicts Resolution Evasion Maneuver Cooperative (communicate with other aircraft) Non-cooperative (sensing without communication) Support of Artificial Intelligence to Motion Planning
  11. 11. 11 Trusted communication Availability & Performance Confidentiality & Integrity Safety Communication & Security Monitoring & Control Validation through Attack Scenarios Joint use of mesh and infrastructure technologies Communication safety Ensure robust and efficient drone communications even in presence of malicious attackers and taking into considerations intrinsic platform constraints. • Distributed Intrusion Detection System with in-drone Machine Learning – based detection probes • Embedded autonomous management system for drones, backed with artificial intelligence and machine learning capabilities
  12. 12. 12 Pilots (9 demonstrators) INDRA ACORDE ACCIONA HIB HEMAV ACORDE UNICAN TOTAL ALTRAN ATE ENSMA SCALIAN SHERPA SIEMENS SOBEN ENAC IMCS LMT TNL ANYWI DEMCOM IMEC-NL TUD TUE IMEC-BG AIROBOT BUT SM AIT FB IFAT SKYA WBM ABI AI AIK ROT TEKNE UDANET UNIMORE UNISANNIO UNISS UNIVAQ ALM
  13. 13. 13 UC3 Logistics - METIS® Demonstrator 1: Deployment of an Autonomous Communication System in hard-to-access areas • Selecting and Managing an heterogeneous fleet of autonomous vehicles • Using a communication infrastructure with redundant, secure, robust, dissimilar and deterministic abilities • Navigating and sensing at the landing or dropping zone with a high positioning accuracy and a guarantee of absence of objects, people or animals • Detecting and considering dynamically of aircrafts in the mission area and integrating vehicles of the system in air traffic management • Reducing risks and complexity on interactions between system operators and autonomous vehicles METIS® - https://www.youtube.com/watch?v=mp-9-mXny58
  14. 14. 14 UC3 Logistics Demonstrator 2: Logistics in 5G urban environment: Clinical Sample delivery in Hospital campus • Develop a solution which will satisfy hospital requirements (special challenge is safety and security requirements). • LMT will work on establishing and benchmarking 5G communication with special attention to security of communications. • IMCS will work on integrating project results regarding object recognition & avoidance and online safety monitoring in a ROS node. • Atechsys and SOBEN will work on coupling between a transport drone and a logistics droide in a periurban area
  15. 15. 15 Conclusion - Expected Impact • Strengthen artificial intelligence integration in drones and its applications • Reinforcing the ecosystems of drones industry by providing methodology and a reference software architecture framework that meets performance and safety requirements • Improving innovation capacity and the integration of new knowledge. A structuring aspect of COMP4DRONES is the adoption of a "safe-by-design" approach • Enabling and easing delivery of new services using drones in Europe.
  16. 16. Thank you! Questions? Contact • Réda Nouacer (CEA) reda.nouacer@cea.fr

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