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Abstract
Autonomous aerial refueling (AAR) technology can increase the flight endurance of unmanned air vehicles (UAVs) effectively. Drogue detection and target tracking method are significant for probe‐drogue refueling system in the docking stage. This paper proposes a novel vision‐based multistage image processing algorithm of drogue detection and target tracking for AAR. This algorithm divides the whole task into four stages: preprocessor, recognizer, predictor, and locker (PRPL). The adaptive threshold segmentation (ATS) algorithm and support vector machine (SVM) classifier are utilized in preprocessor and recognizer for drogue detection. An improved kernelized correlation filter (IKCF) tracking algorithm and scale adaptive method by window position as well as image resolution adjusted are adopted in predictor and locker for target tracking in complex dynamic environments. Finally, the proposed PRPL multistage image processing strategy is tested using an autonomous aerial refueling testbed. The results indicate that the proposed algorithm achieves high precision, good reliability, and real‐time capability compared with conventional algorithms. The average processing time is within 11 ms in various environments, which can meet the requirement for drogue detection and tracking in AAR.
Highlights
The aerial refueling (AAR) technology can greatly improve the flight duration and increase the payload of unmanned air vehicles (UAVs)
In the boom-and-receptacle refueling method, the tanker is an active part that steers a rigid retractable boom stretched from the rear of it to a socket installed on the UAV
In order to analyze the adaptive threshold segmentation detection and improved KCF tracking (ATS-IKCFT) algorithm performance proposed in this paper, the precision, success rate, and consuming time are analyzed and compared with the traditional KCF tracking (KCFT) method [28] and tracking learning detection tracking (TLDT) method [29]
Summary
The AAR technology can greatly improve the flight duration and increase the payload of UAVs. There are two key technologies in the docking of autonomous probe-and-drogue refueling system: vision-based drogue detection and target tracking and relative pose measurement The former is to obtain the exact location of drogue in the visual images [7], and the latter is to establish the relative pose between the UAV and drogue [8]. Yin et al proposed a new drogue detection and target tracking method based on the approximate circle and inner dark feature of refueling port of drogue, in which the edge information is used to obtain image feature [19].
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