Velocity Obstacle Based Conflict Avoidance in Urban Environment with Variable Speed Limit

Abstract

Current investigations into urban aerial mobility, as well as the continuing growth of global air transportation, have renewed interest in conflict detection and resolution (CD&R) methods. The use of drones for applications such as package delivery, would result in traffic densities that are orders of magnitude higher than those currently observed in manned aviation. Such densities do not only make automated conflict detection and resolution a necessity, but will also force a re-evaluation of aspects such as coordination vs. priority, or state vs. intent. This paper looks into enabling a safe introduction of drones into urban airspace by setting travelling rules in the operating airspace which benefit tactical conflict resolution. First, conflicts resulting from changes of direction are added to conflict resolution with intent trajectory propagation. Second, the likelihood of aircraft with opposing headings meeting in conflict is reduced by separating traffic into different layers per heading–altitude rules. Guidelines are set in place to make sure aircraft respect the heading ranges allowed at every crossed layer. Finally, we use a reinforcement learning agent to implement variable speed limits towards creating a more homogeneous traffic situation between cruising and climbing/descending aircraft. The effects of all of these variables were tested through fast-time simulations on an open source airspace simulation platform. Results showed that we were able to improve the operational safety of several scenarios.

U-Space Utilisation of Airspace under Various Layer Function Assignments and Allocations

“…In areas with high-rises, the airspace began at the highest obstacle and ended 1000 feet (304.8 m) above it. Other work [11,12,14] placed the airspace completely between buildings. In some cities (e.g., New York, Hong Kong) it may not be feasible for small UAVs to travel above the highest obstacle.…”

“…The network may be constrained by the existing buildings (i.e., above roads) or by some virtual constraints. Several workers [11,12,14,[18][19][20] developed concepts in which drones follow the network. It is important to note that the border between the two airspace types was not always clear.…”

“…This particular rule-set limits the chance of an encounter between UAVs travelling in different directions. Airspace designs in orthogonal networks were considered in [11,12,18].…”

“…For this purpose, two sub-experiments are performed in order to isolate the effect of each on the performance of a U-space system. Sub-experiment 1 investigates different airspace structures in which layers are assigned a specific function (turning, cruising, deconflicting) [11,12]. Assigning layers for cruising will reduce the available airspace for other functions.…”

Using Reinforcement Learning in a Layered Airspace to Improve Layer Change Decision

“…Once a conflict is found, MVP displaces the predicted future positions of both ownship and intruder at the closest point of approach (CPA) in the shortest way out of the protected zone of the intruder. More details on state-based detection and the avoidance manoeuvres calculated by MVP can be gathered from previous work [24].…”