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Author: Publisher: ISBN: Category : Languages : en Pages : 74
Book Description
Unmanned Aerial Vehicles (UAVs) have been recently used in many areas due to their ability to perform almost as efficiently as piloted aircrafts, at low cost, without endangering human life. In this research, we consider a fleet of UAVs performing a search mission in a bounded area where static targets are located. The search strategy is a cooperative one, rather than a centralized one due to low bandwidth and dynamics in the system. Cooperation has been noted in the literature to be very important for multi-vehicle control systems such as the one considered in this research. Our solution method is a Dynamic Programming (DP) algorithm for computing the trajectories of multiple UAVs from a mission starting point with the objective of cooperatively searching the set of fixed targets. The algorithm presented in this research calculates a gain function for maximizing the number of targets found in the area. Each vehicle maintains a (dynamic) cognitive map of probabilities of indicating where the targets are likely to exist and where other vehicles have already been routed. (Abstract shortened by UMI.).
Author: Publisher: ISBN: Category : Languages : en Pages : 74
Book Description
Unmanned Aerial Vehicles (UAVs) have been recently used in many areas due to their ability to perform almost as efficiently as piloted aircrafts, at low cost, without endangering human life. In this research, we consider a fleet of UAVs performing a search mission in a bounded area where static targets are located. The search strategy is a cooperative one, rather than a centralized one due to low bandwidth and dynamics in the system. Cooperation has been noted in the literature to be very important for multi-vehicle control systems such as the one considered in this research. Our solution method is a Dynamic Programming (DP) algorithm for computing the trajectories of multiple UAVs from a mission starting point with the objective of cooperatively searching the set of fixed targets. The algorithm presented in this research calculates a gain function for maximizing the number of targets found in the area. Each vehicle maintains a (dynamic) cognitive map of probabilities of indicating where the targets are likely to exist and where other vehicles have already been routed. (Abstract shortened by UMI.).
Author: Marcelo I. Guzman Publisher: MDPI ISBN: 3039439855 Category : Science Languages : en Pages : 248
Book Description
This book is the first literature collection focused on the development and implementation of unmanned aircraft systems (UAS) and their integration with sensors for atmospheric measurements on Earth. The research covered in the book combines chemical, physical, and meteorological measurements performed in field campaigns, as well as conceptual and laboratory work. Useful examples for the development of platforms and autonomous systems for environmental studies are provided, which demonstrate how careful the operation of sensors aboard UAS must be to gather information for remote sensing in the atmosphere. The work serves as a key collection of articles to introduce the topic to new researchers interested in the field, guide future studies, and motivate measurements to improve our understanding of the Earth’s complex atmosphere.
Author: Ziquan Yu Publisher: Springer ISBN: 9789819976607 Category : Technology & Engineering Languages : en Pages : 0
Book Description
This book focuses on the fault-tolerant cooperative control (FTCC) of multiple unmanned aerial vehicles (multi-UAVs). It provides systematic and comprehensive descriptions of FTCC issues in multi-UAVs concerning faults, external disturbances, strongly unknown nonlinearities, and input saturation. Further, it addresses FTCC design from longitudinal motions to attitude motions, and outer-loop position motions of multi-UAVs. The book’s detailed control schemes can be used to enhance the flight safety of multi-UAVs. As such, the book offers readers an in-depth understanding of UAV safety in cooperative/formation flight and corresponding design methods. The FTCC methods presented here can also provide guidelines for engineers to improve the safety of aerospace engineering systems. The book offers a valuable asset for scientists and researchers, aerospace engineers, control engineers, lecturers and teachers, and graduates and undergraduates in the system and control community, especially those working in the field of UAV cooperation and multi-agent systems.
Author: Publisher: ISBN: Category : Languages : en Pages : 13
Book Description
A software architecture is presented, which introduces several agents which focus on different aspects of path planning for multiple autonomous unmanned aerial vehicles (UAV's) that are searching an uncertain and threatening environment for targets. One agent models threats in the environment. Another develops a model of the environment that allows targets to be defined by individual probability distribution. Lastly, an agent is presented that utilizes the information from the other agents to generate a near optimal path plan using a Dynamic Programming algorithm.
Author: David R. Green Publisher: CRC Press ISBN: 1482246082 Category : Technology & Engineering Languages : en Pages : 256
Book Description
Unmanned Aircraft Systems (UAS) are a rapidly evolving technology with an expanding array of diverse applications. In response to the continuing evolution of this technology, this book discusses unmanned aerial vehicles (UAVs) and similar systems, platforms and sensors, as well as exploring some of their environmental applications. It explains how they can be used for mapping, monitoring, and modeling a wide variety of different environmental aspects, and at the same time addresses some of the current constraints placed on realizing the potential use of the technology such as s flight duration and distance, safety, and the invasion of privacy etc. Features of the book: Provides necessary theoretical foundations for pertinent subject matter areas Introduces the role and value of UAVs for geographical data acquisition, and the ways to acquire and process the data Provides a synthesis of ongoing research and a focus on the use of technology for small-scale image and spatial data acquisition in an environmental context Written by experts of the technology who bring together UAS tools and resources for the environmental specialist Unmanned Aerial Remote Sensing: UAS for Environmental Applications is an excellent resource for any practitioner utilizing remote sensing and other geospatial technologies for environmental applications, such as conservation, research, and planning. Students and academics in information science, environment and natural resources, geosciences, and geography, will likewise find this comprehensive book a useful and informative resource.
Author: Azima Motaghi Publisher: ISBN: 9781303920318 Category : Drone aircraft Languages : en Pages : 85
Book Description
The ability to track targets using Unmanned Aerial Vehicles (UAVs) has a wide range of civilian and military applications. For example, for military personnel, it is critical to track and locate a variety of objects, including the movement of enemy vehicles. For civilian applications, we can easily find UAVs performing tasks related to land survey, weather forecasting, search and rescue missions, and monitoring farm crops. This study presents a novel method for determining the locations of moving ground-based targets using UAVs and human operators. In previous research, Sharma et al. [1] developed a vision-based target tracking algorithm. They used a Kalman-filter to estimate the target's position and velocity. An information-filter was used to control the sensor. Targets were geo-localized using the pixel locations of the targets in an image. The measurements of the UAV position, altitude, and camera pose angle along with the information embedded in the image provide the required input to an estimator to geo-locate ground targets. Using the highly sophisticated skills of humans for sensing environments, we are interested in integrating the abilities of human operators as a part of sensor network. The main contribution of this thesis is a cyber-physical system developed for reducing the localization errors of targets observed by either UAVs or humans working cooperatively. In particular, in the process of developing the system, we developed (1) an Extended Kalman Filter (EKF) based algorithm to estimate the positions of multiple targets, (2) a human sensor model using neural networks, and (3) a weighted filter to fuse local target estimations from multiple UAVs. Human sensor inputs were utilized to improve the geo localization accuracy of target position estimates. This technique requires operators to be equipped with an Android device; providing operators an easy access to Google map and Global Positioning System (GPS); such that they can specify a target's position on the map. Each sensor, UAVs or human operators, exchanges data through a Wi-Fi sensor network. A central station is used to collect the information observed by independent sensors for data fusion and combines them to generate more accurate estimates that would not be available from any single UAV or a human operator. The capability of the system was demonstrated using simulation results and Android hardware.
Author: Hao Liu (Of Beijing hang kong hang tian da xue) Publisher: ISBN: 9781032150246 Category : Drone aircraft Languages : en Pages : 0
Book Description
"This book is based on the authors' recent research results on formation control problems, including time-varying formation, communication delays, fault-tolerant formation for multiple UAV systems with highly nonlinear and coupled, parameter uncertainties, and external disturbances. Differentiating from existing works, this book presents a robust optimal formation approach to designing distributed cooperative control laws for a group of UAVs, based on the linear quadratic regulator control method and the robust compensation theory. The proposed control method is composed of two parts: the nominal part to achieve desired tracking performance and the robust compensation part to restrain the influence of highly nonlinear and strongly coupled, parameter uncertainties, and external disturbances on the global closed-loop control system. Furthermore, this book gives proof of their robust properties. The influence of communication delays and actuator fault tolerance can be restrained by the proposed robust formation control protocol, and the formation tracking errors can converge into a neighborhood of the origin bounded by a given constant in a finite time. Moreover, the book provides details about the practical application of the proposed method to design formation control systems for multiple quadrotors and tail-sitters. Additional features include a robust control method that is proposed to address the formation control problem for UAVs and theoretical and experimental research for the cooperative flight of the quadrotor UAV group and the tail-sitter UAV group. Robust Formation Control for Multiple Unmanned Aerial Vehicles is suitable for graduate students, researchers, and engineers in the system and control community, especially those engaged in the areas of robust control, UAV swarming, and multi-agent systems."--
Author: Ryan Skeele Publisher: ISBN: Category : Drone aircraft Languages : en Pages : 84
Book Description
Unmanned aerial vehicle (UAV) technology has grown out of traditional research and military applications and has captivated the commercial and consumer markets, showing the ability to perform a spectrum of autonomous functions. This technology has the capability of saving lives in search and rescue, fighting wildfires in environmental monitoring, and delivering time dependent medicine in package delivery. These examples demonstrate the potential impact this technology will have on our society. However, it is evident how sensitive UAVs are to the uncertainty of the physical world. In order to properly achieve the full potential of UAVs in these markets, robust and efficient planning algorithms are needed. This thesis addresses the challenge of planning under uncertainty for UAVs. We develop a suite of algorithms that are robust to changes in the environment and build on the key areas of research needed for utilizing UAVs in a commercial setting. Throughout this research three main components emerged: monitoring targets in dynamic environments, exploration with unreliable communication, and risk-aware path planning. We use a realistic fire simulation to test persistent monitoring in an uncertain environment. The fire is generated using the standard program for modeling wildfire, FARSITE. This model was used to validate a weighted-greedy approach to monitoring clustered points of interest (POIs) over traditional methods of tracking a fire front. We implemented the algorithm on a commercial UAV to demonstrate the deployment capability. Dynamic monitoring has limited potential if if coordinated planning is fallible to uncertainty in the world. Uncertain communication can cause critical failures in coordinated planning algorithms. We develop a method for coordinated exploration of a multi-UAV team with unreliable communication and limited battery life. Our results show that the proposed algorithm, which leverages meeting, sacrificing, and relaying behavior, increases the percentage of the environment explored over a frontier-based exploration strategy by up to 18%. We test on teams of up to 8 simulated UAVs and 2 real UAVs able to cope with communication loss and still report improved gains. We demonstrate this work with a pair of custom UAVs in an indoor office environment. We introduce a novel approach to incorporating and addressing uncertainty in planning problems. The proposed Risk-Aware Graph Search (RAGS) algorithm combines traditional deterministic search techniques with risk-aware planning. RAGS is able to trade off the number of future path options, as well as the mean and variance of the associated path cost distributions to make online edge traversal decisions that minimize the risk of executing a high-cost path. The algorithm is compared against existing graphsearch techniques on a set of graphs with randomly assigned edge costs, as well as over a set of graphs with transition costs generated from satellite imagery data. In all cases, RAGS is shown to reduce the probability of executing high-cost paths over A*, D* and a greedy planning approach. High level planning algorithms can be brittle in dynamic conditions where the environment is not modeled perfectly. In developing planners for uncertainty we ensure UAVs will be able to operate in conditions outside the scope of prior techniques. We address the need for robustness in robotic monitoring, coordination, and path planning tasks. Each of the three methods introduced were tested in simulated and real environments, and the results show improvement over traditional algorithms.
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Author: Marcelo I. Guzman
Author: Ziquan Yu
Author: John Patrick Tisdale
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Author: David R. Green
Author: Azima Motaghi
Author: Hao Liu (Of Beijing hang kong hang tian da xue)
Author: Ryan Skeele