  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 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.
