Drogue detection for autonomous aerial refueling via hybrid pigeon-inspired optimized color opponent and saliency aggregation

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.

Eagle Vision-Based Coordinate Landing Control Framework of Unmanned Aerial Vehicles on an Unmanned Surface Vehicle

The coordinate landing control of the unmanned aerial vehicles(UAVs)is a key technology to expand the cooperation between the UAVs and the unmanned surface vehicle(USV).This paper provides an eagle vision-based coordinate landing control framework for the UAV swarm landing on a USV.Back-stepping controller is designed to guarantee that the tracking errors in both of the approaching stage and the landing stage converge to zero.Eagle vision is applied in the visual navigation to improve the landing accuracy.The experiment verified the feasibility of the proposed method.