| Autor: |
Kaya Kuru, John Michael Pinder, Benjamin Jon Watkinson, Darren Ansell, Keith Vinning, Lee Moore, Chris Gilbert, Aadithya Sujit, David Jones |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 11, Pp 100323-100342 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2023.3314504 |
| Popis: |
The subject of autonomy within unmanned aerial vehicles (UAVs) has proven to be a remarkable research field – mostly due to the development of AI techniques within embedded advanced bespoke microcontrollers – during the last several decades. For drones, as safety-critical systems, there is an increasing need for onboard detect & avoid (DAA) technology i) to see, sense or detect conflicting traffic or imminent non-cooperative threats due to their high mobility with multiple degrees of freedom and the complexity of deployed unstructured environments, and subsequently ii) to take the appropriate actions to avoid collisions depending upon the level of autonomy. The safe and efficient integration of UAV traffic management (UTM) systems with air traffic management (ATM) systems, using intelligent autonomous approaches, is an emerging requirement where the number of diverse UAV applications is increasing on a large scale in dense air traffic environments for completing swarms of multiple complex missions flexibly and simultaneously. Significant progress over the past few years has been made in detecting UAVs present in aerospace, identifying them, and determining their existing flight path. This study makes greater use of electronic conspicuity (EC) information made available by PilotAware Ltd (https://www.pilotaware.com) in developing an advanced collision management methodology – Drone Aware Collision Management (DACM) – capable of determining and executing a variety of time-optimal evasive collision avoidance (CA) manoeuvres using a reactive geometric conflict detection and resolution (CDR) technique. The merits of the DACM methodology have been demonstrated through extensive simulations and real-world field tests in avoiding mid-air collisions (MAC) between UAVs and manned aeroplanes. The results show that the proposed methodology can be employed successfully in avoiding collisions while limiting the deviation from the original trajectory in highly dynamic aerospace without requiring sophisticated sensors and prior training. With the proposed technological improvement equipped with Artificial Intelligence (AI) techniques, MAC risks which cannot be avoided with the current off-the-shelf sensor technologies, in particular, between flights with very high velocities, can be definitely prevented with the accurate measurements and state and situation awareness (SSA) that uses a global coverage strategy with real-time low latency EC data feeds acquired from all aircraft. The MAC standards, dictated by the aviation authorities, can be mandated for UAVs considering the reliable decision-making abilities of DACM – without creating new collision risks during evasive manoeuvres, which can expedite the safe and efficient integration of UAVs into ATM systems. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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