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Author: Caris Moses Publisher: ISBN: Category : Drone aircraft Languages : en Pages : 48
Book Description
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.
Author: Caris Moses Publisher: ISBN: Category : Drone aircraft Languages : en Pages : 48
Book Description
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.
Author: Yasmina Bestaoui Sebbane Publisher: CRC Press ISBN: 1000049906 Category : Computers Languages : en Pages : 264
Book Description
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.
Author: Kymry Burwell Publisher: ISBN: Category : Languages : en Pages :
Book Description
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.
Author: Andrey Ronzhin Publisher: Springer Nature ISBN: 3030603377 Category : Computers Languages : en Pages : 343
Book Description
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.
Author: Joshua David Redding Publisher: ISBN: Category : Languages : en Pages : 131
Book Description
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.
Author: Aaron Ma Publisher: ISBN: Category : Languages : en Pages : 151
Book Description
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.
Author: Johan van Benthem Publisher: Springer Science & Business Media ISBN: 9401579474 Category : Philosophy Languages : en Pages : 308
Book Description
The subject of Time has a wide intellectual appeal across different dis ciplines. This has shown in the variety of reactions received from readers of the first edition of the present Book. Many have reacted to issues raised in its philosophical discussions, while some have even solved a number of the open technical questions raised in the logical elaboration of the latter. These results will be recorded below, at a more convenient place. In the seven years after the first publication, there have been some noticeable newer developments in the logical study of Time and temporal expressions. As far as Temporal Logic proper is concerned, it seems fair to say that these amount to an increase in coverage and sophistication, rather than further break-through innovation. In fact, perhaps the most significant sources of new activity have been the applied areas of Linguistics and Computer Science (including Artificial Intelligence), where many intriguing new ideas have appeared presenting further challenges to temporal logic. Now, since this Book has a rather tight composition, it would have been difficult to interpolate this new material without endangering intelligibility.
Author: Rafael H. Bordini Publisher: John Wiley & Sons ISBN: 0470029005 Category : Technology & Engineering Languages : en Pages : 307
Book Description
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.
Author: Kikuo Fujimura Publisher: Springer Science & Business Media ISBN: 4431681655 Category : Computers Languages : en Pages : 190
Book Description
Computer Science Workbench is a monograph series which will provide you with an in-depth working knowledge of current developments in computer technology. Every volume in this series will deal with a topic of importance in computer science and elaborate on how you yourself can build systems related to the main theme. You will be able to develop a variety of systems, including computer software tools, computer graphics, computer animation, database management systems, and computer-aided design and manufacturing systems. Computer Science Workbench represents an important new contribution in the field of practical computer technology. TOSIYASU L. KUNII To my parents Kenjiro and Nori Fujimura Preface Motion planning is an area in robotics that has received much attention recently. Much of the past research focuses on static environments - various methods have been developed and their characteristics have been well investigated. Although it is essential for autonomous intelligent robots to be able to navigate within dynamic worlds, the problem of motion planning in dynamic domains is relatively little understood compared with static problems.
Author: Caris Moses
Author: Caris Moses
Author: Yasmina Bestaoui Sebbane
Author: Kymry Burwell
Author: Andrey Ronzhin
Author: Joshua David Redding
Author: Aaron Ma
Author: Malik Ghallab
Author: Johan van Benthem
Author: Rafael H. Bordini
Author: Kikuo Fujimura