Maneuvering Decision Making Based on Cloud Modeling Algorithm for UAV Evasion–Pursuit Game

Abstract

When facing problems in the aerial pursuit game, most of the current unmanned aerial vehicles (UAVs) have good maneuverability performance, but it is difficult to utilize the overload maneuverability of UAVs properly; further, UAVs tend to be more costly, and it is often difficult to effectively prevent the enemy from reaching the tailgating position behind the UAV in the aerial pursuit game. Therefore, there is a pressing need for a maneuvering algorithm that can effectively allow a UAV to quickly protect itself in a disadvantageous position, stably and effectively select a maneuver with the maneuvering algorithm, and stably and effectively establish an advantage by moving to an advantageous position. Therefore, this paper establishes a cloud model-based UAV-maneuvering aerial pursuit decision-making model based on pursuit-and-evasion game positions. Based on the evaluation of the latter, when the UAV is at a disadvantage, we use the constructed defensive maneuver expert pool to abandon the disadvantageous position. When the UAV is at an advantage, we use cloud model-based pursuit-and-evasion game maneuvering decision making to establish an advantageous position. According to the results of the simulation examples, the maneuvering decision-making method designed in this paper confirms that the UAV can quickly abandon its position and establish an advantage in case of parity or disadvantage and that it can also stably establish a tail-chasing position in case of advantage.