Guoqiang Wang

This book provides the current research on optimization methods for cooperative task allocation problems in drone fleets. Drone fleets, with their advantages of low cost, small size, and strong maneuverability, have been widely used in logistics, inspections, and other fields. However, it is a great challenge to cooperative task allocation when drone fleets are in large-scale tasks with task time constraints, temporal relationships, and performance requirements. To avoid the waste of resources and the conflicts of task, time, and space in drone fleets, it is important to ensure scientific and efficient task allocation in drone fleets. Various optimization problems in practical application scenarios are presented, including the heuristic method for task allocation in multirotor drones under wind influence, a multi-objective optimization method for task allocation in heterogeneous drones, a game-theoretic method for task allocation in the drone fleet, and a cooperative task allocation method for drone fleets with the assistance of a vehicle. This book provides a pedagogical lesson for students, particularly Ph. D. students in operational research, but also acts as a research reference for senior researchers. Non-experts who are interested in the management of drone fleets can also gain interesting insights from this book.

This book provides the current research on optimization methods for cooperative task allocation problems in drone fleets. Drone fleets, with their advantages of low cost, small size, and strong maneuverability, have been widely used in logistics, inspections, and other fields. However, it is a great challenge to cooperative task allocation when drone fleets are in large-scale tasks with task time constraints, temporal relationships, and performance requirements. To avoid the waste of resources and the conflicts of task, time, and space in drone fleets, it is important to ensure scientific and efficient task allocation in drone fleets. Various optimization problems in practical application scenarios are presented, including the heuristic method for task allocation in multirotor drones under wind influence, a multi-objective optimization method for task allocation in heterogeneous drones, a game-theoretic method for task allocation in the drone fleet, and a cooperative task allocation method for drone fleets with the assistance of a vehicle. This book provides a pedagogical lesson for students, particularly Ph. D. students in operational research, but also acts as a research reference for senior researchers. Non-experts who are interested in the management of drone fleets can also gain interesting insights from this book.

This book is the first overview book on video surveillance issues within airport grounds. It delves into the distinctive design principles underpinning computer vision algorithms tailored specifically for airport ground video surveillance, while also documenting the latest advancements within this domain. The initial section of this book introduces a comprehensive array of airport ground video surveillance datasets, encompassing the AGVS series alongside other pertinent datasets. Subsequently, the second section delves into the ongoing research endeavors on airport-ground-oriented computer vision algorithms, examining segmentation, recognition, and tracking methodologies. Lastly, the book’s third segment elucidates the design methodologies and exemplary use cases of airport ground video surveillance applications. Written by the authors intimately involved in creating the datasets, algorithms, and applications outlined within, this book stands as an indispensable reference for researchers and graduate students operating within the realm of intelligent transportation.