Towards collision-free trajectory for autonomous and pilot-controlled unmanned aerial vehicles

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 to see, sense or detect conflicting traffic or other hazards due to their high mobility and the complexity of deployed unstructured environments, and subsequently take the appropriate action to avoid collisions depending upon various levels of autonomy. The safe 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 areas 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 airspace, identifying them, and determining their existing flight path. With electronic conspicuity (EC) information made available by commercially available low-cost systems such as PilotAware, this study makes greater use of this information by developing an advanced collision management (CM) methodology capable of determining and executing a variety of evasive collision avoidance (CA) manoeuvres using a reactive geometric conflict detection and resolution (CDR) technique. The merits of the proposed methodology have been demonstrated through extensive simulations and real-world field tests. The results show that the proposed methodology can be employed successfully in avoiding collisions while limiting the deviation from the original trajectory in dynamic aerospace without requiring sophisticated sensors and prior training.