Robust Vision-Based Control of a Rotorcraft UAV for Uncooperative Target Tracking

This paper investigates the problem of using an unmanned aerial vehicle (UAV) to track and hover above an uncooperative target, such as an unvisited area or an object that is newly discovered. A vision-based strategy integrating the metrology and the control is employed to achieve target tracking and hovering observation. First, by introducing a virtual camera frame, the reprojected image features can change independently of the rotational motion of the vehicle. The image centroid and an optimal observation area on the virtual image plane are exploited to regulate the relative horizontal and vertical distance. Then, the optic flow and gyro measurements are utilized to estimate the relative UAV-to-target velocity. Further, a gain-switching proportional-derivative (PD) control scheme is proposed to compensate for the external interference and model uncertainties. The closed-loop system is proven to be exponentially stable, based on the Lyapunov method. Finally, simulation results are presented to demonstrate the effectiveness of the proposed vision-based strategy in both hovering and tracking scenarios.

Design and Analysis of Refined Inspection of Field Conditions of Oilfield Pumping Wells Based on Rotorcraft UAV Technology

The traditional oil well monitoring method relies on manual acquisition and various high-precision sensors. Using the indicator diagram to judge the working condition of the well is not only difficult to establish but also consumes huge manpower and financial resources. This paper proposes the use of computer vision in the detection of working conditions in oil extraction. Combined with the advantages of an unmanned aerial vehicle (UAV), UAV aerial photography images are used to realize real-time detection of on-site working conditions by real-time tracking of the working status of the head working and other related parts of the pumping unit. Considering the real-time performance of working condition detection, this paper proposes a framework that combines You only look once version 3 (YOLOv3) and a sort algorithm to complete multi-target tracking in the form of tracking by detection. The quality of the target detection in the framework is the key factor affecting the tracking effect. The experimental results show that a good detector makes the tracking speed achieve the real-time effect and provides help for the real-time detection of the working condition, which has a strong practical application.

Conceptual Design Approach of Hybrid UAV (Huav): Technology Enabler for Aerotourism in Malaysia

The introduction of Aerotourism using Unmanned Aerial Vehicle (UAV) is timely due to the importance of strong, positive destination image and how it works to influence tourist destination selection and satisfaction, as well as destination sustainability. UAV has certain advantages over piloted aircraft missions, which include the lower operating cost, improved safety, flexibility in mission planning, less expensive remote sensing platform, more rapid deployment capability than piloted aircraft and closer proximity to the target requirement. The increasing trend of UAV (fixed wing and rotorcraft) as part of image capturing and live telecasting has prompted many tourism operators to migrate into this autonomous technology. Unfortunately, due to its restricted design configurations and requirements related to fixed wing and rotorcraft UAV such as the need for landing strip and long endurance has receded the ultimatum usage of this technology. This paper aims to develop a conceptual framework of Aerotourism for destination image enhancement, as well as addresses the conceptual approach of Hybrid UAV (hUAV) by integrating fixed wing with rotorcraft. It is predicted that this enabling technology will achieve the smooth transition from hover to cruising and the realization of Aerotourism in Malaysia. This research could be considered as a breakthrough for Malaysian destination image, thus it could further boost the tourism industry in Malaysia.