The Effect of Swarming on a Voltage Potential-Based Conflict Resolution Algorithm

Several conflict resolution algorithms for airborne self-separation rely on principles derived from the repulsive forces that exist between similarly charged particles. This research investigates whether the performance of the Modified Voltage Potential algorithm, which is based on this algorithm, can be improved using bio-inspired swarming behavior. To this end, the collision avoidance function of the algorithm is augmented with the velocity alignment and flock centering swarming traits displayed by animals such as birds and fish. The basic and swarm augmented versions of the algorithm were compared using large-scale fast time simulations, for multiple traffic demand scenarios. For ideal conditions, the results show that the process of aligning with neighboring traffic triggered a large number of conflicts. However, when noise was added to scenarios, swarming led to a lower increase in the number of intrusions, which could indicate that it can be used to improve the robustness of the Modified Voltage Potential algorithm. Furthermore, the stability results suggest that both versions of the algorithm could reduce the number of conflict chain reactions with respect to simulations without resolution. Future research will further explore the effect of conflict resolution on airspace stability, as well as whether varying the relative weights of swarming elements can improve the safety of swarm augmentations.