Vision based UAV Navigation through Narrow Passages

This research paper presents a novel approach for navigating a micro UAV (Unmanned Aerial Vehicle) through narrow passages using only its onboard camera feed and a PID control system. The proposed method uses edge detection and homography techniques to extract the key features of the passage from the camera feed and then employs a tuned PID controller to guide the UAV through and out of the passage while avoiding collisions with the walls. To evaluate the effectiveness of the proposed approach, a series of experiments were conducted using a micro-UAV navigating in and out of a custom-built test environment (constrained rectangular box). The results demonstrate that the system is able to successfully guide the UAV through the passages while avoiding collisions with the walls.Comment: Currently under review in IEEE CASE 202

A simple visual navigation system for an UAV

We present a simple and robust monocular camera-based navigation system for an autonomous quadcopter. The method does not require any additional infrastructure like radio beacons, artificial landmarks or GPS and can be easily combined with other navigation methods and algorithms. Its computational complexity is independent of the environment size and it works even when sensing only one landmark at a time, allowing its operation in landmark poor environments. We also describe an FPGA based embedded realization of the method’s most computationally demanding phase

Crack detection using enhanced thresholding on UAV based collected images

© 2018 Australasian Robotics and Automation Association. All rights reserved. This paper proposes a thresholding approach for crack detection in an unmanned aerial vehicle (UAV) based infrastructure inspection system. The proposed algorithm performs recursively on the intensity histogram of UAV-taken images to exploit their crack-pixels appearing at the low intensity interval. A quantified criterion of interclass contrast is proposed and employed as an object cost and stop condition for the recursive process. Experiments on different datasets show that our algorithm outperforms different segmentation approaches to accurately extract crack features of some commercial buildings

New Applications of 3D SLAM on Risk Management Using Unmanned Aerial Vehicles in the Construction Industry

Risk Management is an integral part of the Corporate Governance of the Companies, whose objective is to estimate the risks related to each line of business and to make appropriate decisions regarding the adoption of preventive measures. The construction industry, due to its peculiar characteristics about occupational risks, is a sector that must pay particular attention to this issue. Unmanned aerial robots are part of a generation of new technologies, which are emerging in the attempt to develop robust and efficient algorithms capable of obtaining 3D models of structures under construction, to support the assessment of the situation in case of an eventuality, before the direct human intervention. This article proposes to develop a risk management strategy for the construction industry based on obtaining 3D models of work environments using drones, which will allow safe evaluation of risks present in construction zones

UAV Attitude Estimation Using Low-Frequency Radio Polarization Measurements

A method of attitude determination, which makes use of measurements of the polarization of the magnetic field of low-frequency (LF) radio signals, is presented and evaluated. This approach offers advantages relative to existing accelerometer-based systems in high-acceleration, cost-constrained environments such as small fixed-wing unmanned aerial vehicles. Flight test results are presented which demonstrate that LF polarization measurements can be used to obtain a significantly more accurate result than traditional approaches.Engineering and Physical Sciences Research Council (Doctoral Training Account award)This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/TAES.2016.263750

Employing Drones in Agriculture: An Exploration of Various Drone Types and Key Advantages

This article explores the use of drones in agriculture and discusses the various types of drones employed for different agricultural applications. Drones, also known as unmanned aerial vehicles (UAVs), offer numerous advantages in farming practices. They provide real-time and high-resolution data collection, enabling farmers to make informed irrigation, fertilization, and pest management decisions. Drones assist in precision spraying and application of agricultural inputs, minimizing chemical wastage and optimizing resource utilization. They offer accessibility to inaccessible areas, reduce manual labor, and provide cost savings and increased operational efficiency. Drones also play a crucial role in mapping and surveying agricultural fields, aiding crop planning and resource allocation. However, challenges such as regulations and limited flight time need to be addressed. The advantages of using drones in agriculture include precision agriculture, cost and time savings, improved data collection and analysis, enhanced crop management, accessibility and flexibility, environmental sustainability, and increased safety for farmers. Overall, drones have the potential to revolutionize farming practices, leading to increased efficiency, productivity, and sustainability in agriculture.Comment: 5 pages, 8 figure

Scan matching by cross-correlation and differential evolution

Scan matching is an important task, solved in the context of many high-level problems including pose estimation, indoor localization, simultaneous localization and mapping and others. Methods that are accurate and adaptive and at the same time computationally efficient are required to enable location-based services in autonomous mobile devices. Such devices usually have a wide range of high-resolution sensors but only a limited processing power and constrained energy supply. This work introduces a novel high-level scan matching strategy that uses a combination of two advanced algorithms recently used in this field: cross-correlation and differential evolution. The cross-correlation between two laser range scans is used as an efficient measure of scan alignment and the differential evolution algorithm is used to search for the parameters of a transformation that aligns the scans. The proposed method was experimentally validated and showed good ability to match laser range scans taken shortly after each other and an excellent ability to match laser range scans taken with longer time intervals between them.Web of Science88art. no. 85

Sensing and Automation Technologies for Ornamental Nursery Crop Production: Current Status and Future Prospects

The ornamental crop industry is an important contributor to the economy in the United States. The industry has been facing challenges due to continuously increasing labor and agricultural input costs. Sensing and automation technologies have been introduced to reduce labor requirements and to ensure efficient management operations. This article reviews current sensing and automation technologies used for ornamental nursery crop production and highlights prospective technologies that can be applied for future applications. Applications of sensors, computer vision, artificial intelligence (AI), machine learning (ML), Internet-of-Things (IoT), and robotic technologies are reviewed. Some advanced technologies, including 3D cameras, enhanced deep learning models, edge computing, radio-frequency identification (RFID), and integrated robotics used for other cropping systems, are also discussed as potential prospects. This review concludes that advanced sensing, AI and robotic technologies are critically needed for the nursery crop industry. Adapting these current and future innovative technologies will benefit growers working towards sustainable ornamental nursery crop production