This monograph is motivated by a significant number of vision based algorithms for Unmanned Aerial Vehicles (UAV) that were developed during research and development projects. Vision information is utilized in various applications like visual surveillance, aim systems, recognition systems, collision-avoidance systems and navigation. This book presents practical applications, examples and recent challenges in these mentioned application fields. The aim of the book is to create a valuable source of information for researchers and constructors of solutions utilizing vision from UAV. Scientists, researchers and graduate students involved in computer vision, image processing, data fusion, control algorithms, mechanics, data mining, navigation and IC can find many valuable, useful and practical suggestions and solutions. The latest challenges for vision based systems are also presented.
Control of unmanned aerial vehicles is a very active topic in research with lots of applications ranging from civilian to military. To control a UAV, its attitude is often controlled using gyroscopes, but to control its position, inertial sensors together with GPS are often used. However, obtaining accurate current position is difficult using inertial sensors because of the integration drift. GPS on the other hand is not functional in indoor applications since it cannot connect to GPS satellites. Since vision has been proved to be an inexpensive and consistent source of relative position information, vision-based control is getting more popular in UAVs recently, but then again, using vision in outdoor applications is challenging as the target can move fast and out of the vision sensor field of view. So, in order to keep the target inside the field of view, two algorithms are being developed and tested via simulation in this research. Using pan/tilt/zoom cameras or multi camera systems, the target is guaranteed to stay in vision system field of view and hence, the vision based pose estimation can provide the control system with proper relative position. Two case studies - vision-based mobile-target tracking of a quadrotor using a multi-camera vision sensor and vision-based mobile-target tracking of a tilting rotor aircraft equipped with a zooming camera - are presented in this research to show the applicability of these methods in UAV control.
Abstract: Unmanned Aerial Vehicles (UAVs) are versatile, adapting hardware and software for research. They are vital for remote monitoring, especially in challenging settings such as volcano observation with limited access. In response, economical computer vision systems provide a remedy by processing data, boosting UAV autonomy, and assisting in maneuvering. Through the application of these technologies, researchers can effectively monitor remote areas, thus improving surveillance capabilities. Moreover, flight controllers employ onboard tools to gather data, further enhancing UAV navigation during surveillance tasks. For energy efficiency and comprehensive coverage, this paper introduces a budget-friendly prototype aiding UAV navigation, minimizing effects on endurance. The prototype prioritizes improved maneuvering via the integrated landing and obstacle avoidance system (LOAS). Employing open-source software and MAVLink communication, these systems underwent testing on a Pixhawk-equipped quadcopter. Programmed on a Raspberry Pi onboard computer, the prototype includes a distance sensor and basic camera to meet low computational and weight demands.Tests occurred in controlled environments, with systems performing well in 90% of cases. The Pixhawk and Raspberry Pi documented quad actions during evasive and landing maneuvers. Results prove the prototype's efficacy in refining UAV navigation. Integrating this cost-effective, energy-efficient model holds promise for long-term mission enhancement--cutting costs, expanding terrain coverage, and boosting surveillance capabilities
Across the globe, the past several years have seen a tremendous increase in the role of cooperative autonomous systems. The field of cooperative control and optimization has established itself as a part of many different scientific disciplines. The contents of this hugely important volume, which adds much to the debate on the subject, are culled from papers presented at the Seventh Annual International Conference on Cooperative Control and Optimization, held in Gainesville, Florida, in January 2007.
Quad Rotorcraft Control develops original control methods for the navigation and hovering flight of an autonomous mini-quad-rotor robotic helicopter. These methods use an imaging system and a combination of inertial and altitude sensors to localize and guide the movement of the unmanned aerial vehicle relative to its immediate environment. The history, classification and applications of UAVs are introduced, followed by a description of modelling techniques for quad-rotors and the experimental platform itself. A control strategy for the improvement of attitude stabilization in quad-rotors is then proposed and tested in real-time experiments. The strategy, based on the use low-cost components and with experimentally-established robustness, avoids drift in the UAV’s angular position by the addition of an internal control loop to each electronic speed controller ensuring that, during hovering flight, all four motors turn at almost the same speed. The quad-rotor’s Euler angles being very close to the origin, other sensors like GPS or image-sensing equipment can be incorporated to perform autonomous positioning or trajectory-tracking tasks. Two vision-based strategies, each designed to deal with a specific kind of mission, are introduced and separately tested. The first stabilizes the quad-rotor over a landing pad on the ground; it extracts the 3-dimensional position using homography estimation and derives translational velocity by optical flow calculation. The second combines colour-extraction and line-detection algorithms to control the quad-rotor’s 3-dimensional position and achieves forward velocity regulation during a road-following task. In order to estimate the translational-dynamical characteristics of the quad-rotor (relative position and translational velocity) as they evolve within a building or other unstructured, GPS-deprived environment, imaging, inertial and altitude sensors are combined in a state observer. The text give the reader a current view of the problems encountered in UAV control, specifically those relating to quad-rotor flying machines and it will interest researchers and graduate students working in that field. The vision-based control strategies presented help the reader to a better understanding of how an imaging system can be used to obtain the information required for performance of the hovering and navigation tasks ubiquitous in rotored UAV operation.
This monograph provides comprehensive guidelines on the current and future trends of innovative simulation systems. In particular, their important components, such as augmented reality and unmanned vehicles are presented. The book consists of three parts. Each part presents good practices, new methods, concepts of systems and new algorithms. Presented challenges and solutions are the results of research and conducted by the contributing authors. The book describes and evaluates the current state of knowledge in the field of innovative simulation systems. Throughout the chapters there are presented current issues and concepts of systems, technology, equipment, tools, research challenges and current, past and future applications of simulation systems. The book is addressed to a wide audience: academic staff, representatives of research institutions, employees of companies and government agencies as well as students and graduates of technical universities in the country and abroad. The book can be a valuable source of information for constructors and developers of innovative simulation systems and their components. Scientists and researchers involved in mechanics, control algorithms, image processing, computer vision or data fusion can find many valuable suggestions and solutions.
This book has been motivated by an urgent need for designing and implementation of innovative control algorithms and systems for tracked vehicles. Nowadays the unmanned vehicles are becoming more and more common. Therefore there is a need for innovative mechanical constructions capable of adapting to various applications regardless the ground, air or water/underwater environment. There are multiple various activities connected with tracked vehicles. They can be distributed among three main groups: design and control algorithms, sensoric and vision based in-formation, construction and testing mechanical parts of unmanned vehicles. Scientists and researchers involved in mechanics, control algorithms, image processing, computer vision, data fusion, or IC will find this book useful.
This book presents advanced technologies used in practice to enable early recognition and tracking of various threats to national security. It discusses practical applications, examples and recent challenges in the application fields using sophisticated sensory devices, embedded designs and airborne and ground unmanned vehicles. Undeniably rapid advances in the development of sophisticated sensory devices, significant increases of computing power available to embedded designs and the development of airborne and ground unmanned vehicles offer almost unlimited possibilities for fighting various types of pathologies affecting our societies. The book provides scientists, researchers, engineers and graduate students involved in computer vision, image processing, data fusion, control algorithms, mechanics, data mining, navigation and integrated circuit (IC) with numerous valuable, useful and practical suggestions and solutions.
