Download or read book Multi agent UAV Planning Using Belief Space Hierarchical Planning in the Now written by Caris Moses and published by . This book was released on 2015 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt: Planning long duration missions for unmanned aerial vehicles (UAVs) in dynamic environments has proven to be a very challenging problem. Tactical UAVs must be able to reason about how to best accomplish mission objectives in the face of evolving mission conditions. Examples of UAV missions consist of executing multiple tasks such as: locating, identifying, and prosecuting targets; avoiding dynamic (i.e. pop-up) threats; geometric path planning with kinematic and dynamic constraints; and/or acting as a communication relay. The resulting planning problem is then one over a large and stochastic state space due to the size of the mission environment and the number of objects within that environment. The world state is also only partially observable due to sensor noise, and requires us to plan in the belief space, which is a probability distribution over all possible states. Some a priori contextual knowledge, like target and threat locations, is available via satellite imagery based maps. However, it is possible this will be "old" data by execution time. This makes classic approaches to a priori task, or symbolic, planning a poor choice of tool. In addition, task planners traditionally do not have methods for handling geometric planning problems as they focus on high level tasks. However, modern belief space geometric planning tools become intractable for large state spaces, such as ours. Recent tools in the domain of robotic manipulation have approached this problem by combining symbolic and geometric planning paradigms. One in particular, Hierarchical Planning-in-the-Now in belief space (BHPN) is a hierarchical planning technique that tightly couples geometric motion planning in belief spaces with symbolic task planning, providing a method for turning large-scale intractable belief space problems into smaller tractable ones. In addition to all of the complexities associated with UAV mission planning discussed above, it is also common for multiple UAVs to work as a team to accomplish a mission objective. This is due to the fact that some vehicles may have certain sensor capabilities that others lack. It could also simply be to spread out and achieve sufficient coverage of an environment. We take a decentralized planning approach to enabling UAV teaming. BHPN provides a flexible method of implementing this loosely-coupled multi-agent planning effort.
Download or read book Multi UAV Planning and Task Allocation written by Yasmina Bestaoui Sebbane and published by CRC Press. This book was released on 2020-03-27 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multi-robot systems are a major research topic in robotics. Designing, testing, and deploying aerial robots in the real world is a possibility due to recent technological advances. This book explores different aspects of cooperation in multiagent systems. It covers the team approach as well as deterministic decision-making. It also presents distributed receding horizon control, as well as conflict resolution, artificial potentials, and symbolic planning. The book also covers association with limited communications, as well as genetic algorithms and game theory reasoning. Multiagent decision-making and algorithms for optimal planning are also covered along with case studies. Key features: Provides a comprehensive introduction to multi-robot systems planning and task allocation Explores multi-robot aerial planning; flight planning; orienteering and coverage; and deployment, patrolling, and foraging Includes real-world case studies Treats different aspects of cooperation in multiagent systems Both scientists and practitioners in the field of robotics will find this text valuable.
Download or read book Multi agent Pathfinding for Unmanned Aerial Vehicles written by Kymry Burwell and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Unmanned aerial vehicles (UAVs), commonly known as drones, have become more and more prevalent in recent years. In particular, governmental organizations and companies around the world are starting to research how UAVs can be used to perform tasks such as package deliver, disaster investigation and surveillance of key assets such as pipelines, railroads and bridges. NASA is currently in the early stages of developing an air traffic control system specifically designed to manage UAV operations in low-altitude airspace. Companies such as Amazon and Rakuten are testing large-scale drone deliver services in the USA and Japan. To perform these tasks, safe and conflict-free routes for concurrently operating UAVs must be found. This can be done using multi-agent pathfinding (mapf) algorithms, although the correct choice of algorithms is not clear. This is because many state of the art mapf algorithms have only been tested in 2D space in maps with many obstacles, while UAVs operate in 3D space in open maps with few obstacles. In addition, when an unexpected event occurs in the airspace and UAVs are forced to deviate from their original routes while inflight, new conflict-free routes must be found. Planning for these unexpected events is commonly known as contingency planning. With manned aircraft, contingency plans can be created in advance or on a case-by-case basis while inflight. The scale at which UAVs operate, combined with the fact that unexpected events may occur anywhere at any time make both advanced planning and planning on a case-by-case basis impossible. Thus, a new approach is needed. Online multi-agent pathfinding (online mapf) looks to be a promising solution. Online mapf utilizes traditional mapf algorithms to perform path planning in real-time. That is, new routes for UAVs are found while inflight. The primary contribution of this thesis is to present one possible approach to UAV contingency planning using online multi-agent pathfinding algorithms, which can be used as a baseline for future research and development. It also provides an in-depth overview and analysis of offline mapf algorithms with the goal of determining which ones are likely to perform best when applied to UAVs. Finally, to further this same goal, a few different mapf algorithms are experimentally tested and analyzed.
Download or read book Interactive Collaborative Robotics written by Andrey Ronzhin and published by Springer Nature. This book was released on 2020-09-30 with total page 343 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 5th International Conference on Interactive Collaborative Robotics, ICR 2020, held in St. Petersburg, Russia, in October 2020. The 31 papers presented were carefully reviewed and selected from 62 submissions. Challenges of human-robot interaction, robot control and behavior in social robotics and collaborative robotics, as well as applied robotic and cyber-physical systems are mainly discussed in the papers.
Download or read book Approximate Multi agent Planning in Dynamic and Uncertain Environments written by Joshua David Redding and published by . This book was released on 2012 with total page 131 pages. Available in PDF, EPUB and Kindle. Book excerpt: Teams of autonomous mobile robotic agents will play an important role in the future of robotics. Efficient coordination of these agents within large, cooperative teams is an important characteristic of any system utilizing multiple autonomous vehicles. Applications of such a cooperative technology stretch beyond multi-robot systems to include satellite formations, networked systems, traffic flow, and many others. The diversity of capabilities offered by a team, as opposed to an individual, has attracted the attention of both researchers and practitioners in part due to the associated challenges such as the combinatorial nature of joint action selection among interdependent agents. This thesis aims to address the issues of the issues of scalability and adaptability within teams of such inter-dependent agents while planning, coordinating, and learning in a decentralized environment. In doing so, the first focus is the integration of learning and adaptation algorithms into a multi-agent planning architecture to enable online adaptation of planner parameters. A second focus is the development of approximation algorithms to reduce the computational complexity of decentralized multi-agent planning methods. Such a reduction improves problem scalability and ultimately enables much larger robot teams. Finally, we are interested in implementing these algorithms in meaningful, real-world scenarios. As robots and unmanned systems continue to advance technologically, enabling a self-awareness as to their physical state of health will become critical. In this context, the architecture and algorithms developed in this thesis are implemented in both hardware and software flight experiments under a class of cooperative multi-agent systems we call persistent health management scenarios.
Download or read book Model based Reinforcement Learning for Cooperative Multi agent Planning written by Aaron Ma and published by . This book was released on 2020 with total page 151 pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous unmanned vehicles (UxVs) can be useful in many scenarios including disaster relief, production and manufacturing, as well as carrying out Naval missions such as surveillance, mapping of unknown regions and pursuit of other hostile vehicles. When considering these scenarios, one of the most difficult challenges is determining which actions or tasks the vehicles should take in order to most efficiently satisfy the objectives. This challenge becomes more difficult with the inclusion of multiple vehicles, because the action and state space scale exponentially with the number of agents. Many planning algorithms suffer from the curse of dimensionality as more agents are included, sampling for suitable actions in the joint action space becomes infeasible within a reasonable amount of time. To enable autonomy, methods that can be applied to a variety of scenarios are invaluable because they reduce human involvement and time. Recently, advances in technology enable algorithms that require more computational power to be effective but work in broader frameworks. We offer three main approaches to multi-agent planning which are all inspired by model-based reinforcement learning. First, we address the curse of dimensionality and investigate how to spatially reduce the state space of massive environments where agents are deployed. We do this in a hierarchical fashion by searching subspaces of the environment, called sub-environments, and creating plans to optimally take actions in those sub-environments. Next, we utilize game-theoretic techniques paired with simulated annealing as an approach for agent cooperation when planning in a finite time horizon. One problem with this approach is that agents are capable of breaking promises with other agents right before execution. To address this, we propose several variations that discourage agents from changing plans in the near future and encourages joint planning in the long term. Lastly, we propose a tree-search algorithm that is aided by a convolutional neural network. The convolutional neural network takes advantage of spatial features that are natural in UxV deployment and offers recommendations for action selection during tree search. In addition, we propose some design features for the tree search that target multi-agent deployment applications.
Download or read book Automated Planning written by Malik Ghallab and published by Elsevier. This book was released on 2004-05-03 with total page 665 pages. Available in PDF, EPUB and Kindle. Book excerpt: Publisher Description
Download or read book Robot Motion Planning written by Jean-Claude Latombe and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 668 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the ultimate goals in Robotics is to create autonomous robots. Such robots will accept high-level descriptions of tasks and will execute them without further human intervention. The input descriptions will specify what the user wants done rather than how to do it. The robots will be any kind of versatile mechanical device equipped with actuators and sensors under the control of a computing system. Making progress toward autonomous robots is of major practical inter est in a wide variety of application domains including manufacturing, construction, waste management, space exploration, undersea work, as sistance for the disabled, and medical surgery. It is also of great technical interest, especially for Computer Science, because it raises challenging and rich computational issues from which new concepts of broad useful ness are likely to emerge. Developing the technologies necessary for autonomous robots is a formidable undertaking with deep interweaved ramifications in auto mated reasoning, perception and control. It raises many important prob lems. One of them - motion planning - is the central theme of this book. It can be loosely stated as follows: How can a robot decide what motions to perform in order to achieve goal arrangements of physical objects? This capability is eminently necessary since, by definition, a robot accomplishes tasks by moving in the real world. The minimum one would expect from an autonomous robot is the ability to plan its x Preface own motions.
Download or read book Algorithmic Foundations of Robotics XI written by H. Levent Akin and published by Springer. This book was released on 2015-04-30 with total page 750 pages. Available in PDF, EPUB and Kindle. Book excerpt: This carefully edited volume is the outcome of the eleventh edition of the Workshop on Algorithmic Foundations of Robotics (WAFR), which is the premier venue showcasing cutting edge research in algorithmic robotics. The eleventh WAFR, which was held August 3-5, 2014 at Boğaziçi University in Istanbul, Turkey continued this tradition. This volume contains extended versions of the 42 papers presented at WAFR. These contributions highlight the cutting edge research in classical robotics problems (e.g. manipulation, motion, path, multi-robot and kinodynamic planning), geometric and topological computation in robotics as well novel applications such as informative path planning, active sensing and surgical planning. This book - rich by topics and authoritative contributors - is a unique reference on the current developments and new directions in the field of algorithmic foundations.
Download or read book Cooperative Robots and Sensor Networks 2015 written by Anis Koubâa and published by Springer. This book was released on 2015-05-18 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book compiles some of the latest research in cooperation between robots and sensor networks. Structured in twelve chapters, this book addresses fundamental, theoretical, implementation and experimentation issues. The chapters are organized into four parts namely multi-robots systems, data fusion and localization, security and dependability, and mobility.
Download or read book Intelligent Systems written by Alexander M. Meystel and published by Wiley-Interscience. This book was released on 2002 with total page 728 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive treatment of the field of intelligent systems is written by two of the foremost authorities in the field. The authors clearly examine the theoretical and practical aspects of these systems. The book focuses on the NIST-RCS (Real-time Control System) model that has been used recently in the Mars Rover.
Download or read book Programming Multi Agent Systems in AgentSpeak using Jason written by Rafael H. Bordini and published by John Wiley & Sons. This book was released on 2007-11-12 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: Jason is an Open Source interpreter for an extended version of AgentSpeak – a logic-based agent-oriented programming language – written in JavaTM. It enables users to build complex multi-agent systems that are capable of operating in environments previously considered too unpredictable for computers to handle. Jason is easily customisable and is suitable for the implementation of reactive planning systems according to the Belief-Desire-Intention (BDI) architecture. Programming Multi-Agent Systems in AgentSpeak using Jason provides a brief introduction to multi-agent systems and the BDI agent architecture on which AgentSpeak is based. The authors explain Jason’s AgentSpeak variant and provide a comprehensive, practical guide to using Jason to program multi-agent systems. Some of the examples include diagrams generated using an agent-oriented software engineering methodology particularly suited for implementation using BDI-based programming languages. The authors also give guidance on good programming style with AgentSpeak. Programming Multi-Agent Systems in AgentSpeak using Jason Describes and explains in detail the AgentSpeak extension interpreted by Jason and shows how to create multi-agent systems using the Jason platform. Reinforces learning with examples, problems, and illustrations. Includes two case studies which demonstrate the use of Jason in practice. Features an accompanying website that provides further learning resources including sample code, exercises, and slides This essential guide to AgentSpeak and Jason will be invaluable to senior undergraduate and postgraduate students studying multi-agent systems. The book will also be of interest to software engineers, designers, developers, and programmers interested in multi-agent systems.
Download or read book Introduction to Stochastic Control Theory written by Karl J. Åström and published by Courier Corporation. This book was released on 2006-01-06 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unabridged republication of the edition published by Academic Press, 1970.
Download or read book Automated Planning and Acting written by Malik Ghallab and published by Cambridge University Press. This book was released on 2016-08-09 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the most recent and advanced techniques for creating autonomous AI systems capable of planning and acting effectively.
Download or read book Planning Algorithms written by Steven M. LaValle and published by Cambridge University Press. This book was released on 2006-05-29 with total page 844 pages. Available in PDF, EPUB and Kindle. Book excerpt: Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. Written for computer scientists and engineers with interests in artificial intelligence, robotics, or control theory, this is the only book on this topic that tightly integrates a vast body of literature from several fields into a coherent source for teaching and reference in a wide variety of applications. Difficult mathematical material is explained through hundreds of examples and illustrations.
Download or read book Shepherding UxVs for Human Swarm Teaming written by Hussein A. Abbass and published by Springer Nature. This book was released on 2021-03-19 with total page 339 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book draws inspiration from natural shepherding, whereby a farmer utilizes sheepdogs to herd sheep, to inspire a scalable and inherently human friendly approach to swarm control. The book discusses advanced artificial intelligence (AI) approaches needed to design smart robotic shepherding agents capable of controlling biological swarms or robotic swarms of unmanned vehicles. These smart shepherding agents are described with the techniques applicable to the control of Unmanned X Vehicles (UxVs) including air (unmanned aerial vehicles or UAVs), ground (unmanned ground vehicles or UGVs), underwater (unmanned underwater vehicles or UUVs), and on the surface of water (unmanned surface vehicles or USVs). This book proposes how smart ‘shepherds’ could be designed and used to guide a swarm of UxVs to achieve a goal while ameliorating typical communication bandwidth issues that arise in the control of multi agent systems. The book covers a wide range of topics ranging from the design of deep reinforcement learning models for shepherding a swarm, transparency in swarm guidance, and ontology-guided learning, to the design of smart swarm guidance methods for shepherding with UGVs and UAVs. The book extends the discussion to human-swarm teaming by looking into the real-time analysis of human data during human-swarm interaction, the concept of trust for human-swarm teaming, and the design of activity recognition systems for shepherding. Presents a comprehensive look at human-swarm teaming; Tackles artificial intelligence techniques for swarm guidance; Provides artificial intelligence techniques for real-time human performance analysis.
Download or read book Autonomous Horizons written by Greg Zacharias and published by Independently Published. This book was released on 2019-04-05 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dr. Greg Zacharias, former Chief Scientist of the United States Air Force (2015-18), explores next steps in autonomous systems (AS) development, fielding, and training. Rapid advances in AS development and artificial intelligence (AI) research will change how we think about machines, whether they are individual vehicle platforms or networked enterprises. The payoff will be considerable, affording the US military significant protection for aviators, greater effectiveness in employment, and unlimited opportunities for novel and disruptive concepts of operations. Autonomous Horizons: The Way Forward identifies issues and makes recommendations for the Air Force to take full advantage of this transformational technology.
