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This paper proposes an algorithm that will allow an autonomous aerial drone to approach and follow a steady or moving herd of cattle using only range measurements. The algorithm is also insensitive to the complexity of the herd’s movement and the measurement noise. Once arrived at the herd of cattle, the aerial drone can follow it to a desired destination. The primary motivation for the development of this algorithm is to use simple, inexpensive and robust sensing hence range sensors. The algorithm does not depend on the accuracy of the range measurements, rather the rate of change of range measurements. The proposed method is based on sliding mode control which provides robustness. A mathematical analysis, simulations and experimental results with a real aerial drone are presented to demonstrate the effectiveness of the proposed method.

This paper presents a methodology that can be used to avoid collisions of aerial drones. Even though there are many collision avoidance methods available in literature, collision cone is a proven method that can be used to predict a collision beforehand. In this research, we propose an algorithm to avoid a collision in a time-efficient manner for collision cone based aerial collision avoidance approaches. Furthermore, the paper has considered all possible scenarios including heading change, speed change and combined heading and speed change, to avoid a collision. The heading-based method was mathematically proven to be the most time-efficient method out of the three. The proposed heading-based method was compared with other work presented in the literature and validated with both simulations and experiments. A Matrice 600 Pro hexacopter is used for the collision avoidance experiments.

An accurate approach for localization and segmentation of an optic disk (OD) in the retinal images is one of the most imperative tasks in an automated screening system. The retinal fundus images analysis is extensively used in the diagnosis and treatment of several eye diseases such as glaucoma and diabetic retinopathy. This research brings out a new algorithm that has not been used before to detect and segment the optic disk in all categories of retinal images, specifically healthy retinal images plus anomalous, or in other words fundus images affected due to diseases. The technique adopted for the separation of the optic disk is centered on histogram specification, mathematical morphological operations includes erosion as well as dilation along with proper thresholding and detection of circles using the Hough Transform technique. The proposed procedure has been tested on standard databases provided for ophthalmic image processing researches on internet, Diaretdb0, Diaretdb1 and local databases collected from the National Eye Hospital and the Vision Care (Pvt) Ltd. The proposed algorithm is able to locate the OD in 90% of all tested images.