Drogue detection is one of the challenging tasks in autonomous aerial refueling due to the requirement for accuracy and rapidity. Saliency detection based on image intrinsic cues can achieve fast detection, but with poor accuracy. Recent studies reveal that optimization-based methods provide accurate and quick solutions for saliency detection. This paper presents a hybrid pigeon-inspired optimization method, the optimized color opponent, that aims to adjust the weight of color opponent channels to detect the drogue region. It can optimize the weights in the selected aerial refueling scene offline, and the results are applied for drogue detection in the scene. A novel algorithm aggregated by the optimized color opponent and robust background detection is presented to provide better precision and robustness. Experimental results on benchmark datasets and aerial refueling images show that the proposed method successfully extracts the saliency region or drogue and exhibits superior performance against the other saliency detection methods with intrinsic cues. The algorithm designed in this paper is competent for the drogue detection task of autonomous aerial refueling.

In this paper, Active Disturbance Rejection Control (ADRC) is utilized in the pitch control of a vertical take-off and landing fixed-wing Unmanned Aerial Vehicle (UAV) to address the problem of height fluctuation during the transition from hover to level flight. Considering the difficulty of parameter tuning of ADRC as well as the requirement of accuracy and rapidity of the controller, a Multi-Strategy Pigeon-Inspired Optimization (MSPIO) algorithm is employed. Particle Swarm Optimization (PSO), Genetic Algorithm (GA), the basic Pigeon-Inspired Optimization (PIO), and an improved PIO algorithm CMPIO are compared. In addition, the optimized ADRC control system is compared with the pure Proportional-Integral-Derivative (PID) control system and the non-optimized ADRC control system. The effectiveness of the designed control strategy for forward transition is verified and the faster convergence speed and better exploitation ability of the proposed MSPIO algorithm are confirmed by simulation results.