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Author: Publisher: ISBN: Category : Languages : en Pages : 277
Book Description
This thesis is concerned with the development of an autonomous system to search a dynamic target in the minimum possible time in uncertain environments, that is, to solve the minimum time search problem, which is presented as an especial problem within the optimal search theory. This work proposes a Bayesian approach to nd the target using several moving agents with constrained dynamics and equipped with sensors that provide information about the environment. The minimum time search involves two process: the target location estimation using the information collected by the agents, and the planning of the searching routes that the agents must follow to nd the target. The target location estimation is tackled using Bayesian techniques, more precisely, the recursive Bayesian lter. Moreover, an improved information lter, based on the extended Kalman lter, that deals with the team communication delays (i.e. out of sequence problem) is presented. The agents trajectory planning is faced as a sequential decision making problem where, given the a priori target location estimation, the best actions that the agents have to perform are computed. For that purpose, three Bayesian strategies are proposed: minimizing the local expected time of detection, maximizing the discounted time probability of detection, and optimizing a probabilistic function that integrates an heuristic that approximates the expected observation. To implement the strategies, three solutions are proposed. The rst one, based on constraint programming, provides exact solutions in the discrete case when the target is static and the number of decision variables is small. The second one is an approximated algorithm stood on the cross entropy optimization method that tackles the discrete case for dynamic targets. The third solution is a gradient-based decentralized algorithm that achieves non-myopic solutions for the continuous case. The minimum time search problems are found inside the core of many real applications, such as search and rescue emergency operations (e.g. shipwreck accidents) or pollution substances di usion control (e.g. oil spill monitoring). This thesis reveals how to reduce the searching time of a moving target e ciently, determining which searching strategies take into account the time and under which conditions are valid, and providing approximated polynomial algorithms to compute the actions that the agents must perform to find the target.
Author: Publisher: ISBN: Category : Languages : en Pages : 277
Book Description
This thesis is concerned with the development of an autonomous system to search a dynamic target in the minimum possible time in uncertain environments, that is, to solve the minimum time search problem, which is presented as an especial problem within the optimal search theory. This work proposes a Bayesian approach to nd the target using several moving agents with constrained dynamics and equipped with sensors that provide information about the environment. The minimum time search involves two process: the target location estimation using the information collected by the agents, and the planning of the searching routes that the agents must follow to nd the target. The target location estimation is tackled using Bayesian techniques, more precisely, the recursive Bayesian lter. Moreover, an improved information lter, based on the extended Kalman lter, that deals with the team communication delays (i.e. out of sequence problem) is presented. The agents trajectory planning is faced as a sequential decision making problem where, given the a priori target location estimation, the best actions that the agents have to perform are computed. For that purpose, three Bayesian strategies are proposed: minimizing the local expected time of detection, maximizing the discounted time probability of detection, and optimizing a probabilistic function that integrates an heuristic that approximates the expected observation. To implement the strategies, three solutions are proposed. The rst one, based on constraint programming, provides exact solutions in the discrete case when the target is static and the number of decision variables is small. The second one is an approximated algorithm stood on the cross entropy optimization method that tackles the discrete case for dynamic targets. The third solution is a gradient-based decentralized algorithm that achieves non-myopic solutions for the continuous case. The minimum time search problems are found inside the core of many real applications, such as search and rescue emergency operations (e.g. shipwreck accidents) or pollution substances di usion control (e.g. oil spill monitoring). This thesis reveals how to reduce the searching time of a moving target e ciently, determining which searching strategies take into account the time and under which conditions are valid, and providing approximated polynomial algorithms to compute the actions that the agents must perform to find the target.
Author: Sara Pérez Carabaza Publisher: Springer Nature ISBN: 3030765598 Category : Technology & Engineering Languages : en Pages : 183
Book Description
This book proposes some novel approaches for finding unmanned aerial vehicle trajectories to reach targets with unknown location in minimum time. At first, it reviews probabilistic search algorithms that have been used for dealing with the minimum time search (MTS) problem, and discusses how metaheuristics, and in particular the ant colony optimization algorithm (ACO), can help to find high-quality solutions with low computational time. Then, it describes two ACO-based approaches to solve the discrete MTS problem and the continuous MTS problem, respectively. In turn, it reports on the evaluation of the ACO-based discrete and continuous approaches to the MTS problem in different simulated scenarios, showing that the methods outperform in most all the cases over other state-of-the-art approaches. In the last part of the thesis, the work of integration of the proposed techniques in the ground control station developed by Airbus to control ATLANTE UAV is reported in detail, providing practical insights into the implementation of these methods for real UAVs.
Author: Ziyi Wang Publisher: OAE Publishing Inc. ISBN: Category : Computers Languages : en Pages : 27
Book Description
This paper focuses on the problem of regional cooperative search using multiple unmanned aerial vehicles (UAVs) for targets that have the ability to perceive and evade. When UAVs search for moving targets in a mission area, the targets can perceive the positions and flight direction of UAVs within certain limits and take corresponding evasive actions, which makes the search more challenging than traditional search problems. To address this problem, we first define a detailed motion model for such targets and design various search information maps and their update methods to describe the environmental information based on the prediction of moving targets and the search results of UAVs. We then establish a multi-UAV search path planning optimization model based on the model predictive control, which includes various newly designed objective functions of search benefits and costs. We propose a priority-encoded improved genetic algorithm with a fine-adjustment mechanism to solve this model. The simulation results show that the proposed method can effectively improve the cooperative search efficiency, and more targets can be found at a much faster rate compared to traditional search methods.
Author: Michael Kamp Publisher: Springer Nature ISBN: 3030937364 Category : Computers Languages : en Pages : 895
Book Description
This two-volume set constitutes the refereed proceedings of the workshops which complemented the 21th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in September 2021. Due to the COVID-19 pandemic the conference and workshops were held online. The 104 papers were thoroughly reviewed and selected from 180 papers submited for the workshops. This two-volume set includes the proceedings of the following workshops:Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence (AIMLAI 2021)Workshop on Parallel, Distributed and Federated Learning (PDFL 2021)Workshop on Graph Embedding and Mining (GEM 2021)Workshop on Machine Learning for Irregular Time-series (ML4ITS 2021)Workshop on IoT, Edge, and Mobile for Embedded Machine Learning (ITEM 2021)Workshop on eXplainable Knowledge Discovery in Data Mining (XKDD 2021)Workshop on Bias and Fairness in AI (BIAS 2021)Workshop on Workshop on Active Inference (IWAI 2021)Workshop on Machine Learning for Cybersecurity (MLCS 2021)Workshop on Machine Learning in Software Engineering (MLiSE 2021)Workshop on MIning Data for financial applications (MIDAS 2021)Sixth Workshop on Data Science for Social Good (SoGood 2021)Workshop on Machine Learning for Pharma and Healthcare Applications (PharML 2021)Second Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning (EDML 2020)Workshop on Machine Learning for Buildings Energy Management (MLBEM 2021)
Author: Lawrence D. Stone Publisher: Springer ISBN: 3319268996 Category : Business & Economics Languages : en Pages : 222
Book Description
This book begins with a review of basic results in optimal search for a stationary target. It then develops the theory of optimal search for a moving target, providing algorithms for computing optimal plans and examples of their use. Next it develops methods for computing optimal search plans involving multiple targets and multiple searchers with realistic operational constraints on search movement. These results assume that the target does not react to the search. In the final chapter there is a brief overview of mostly military problems where the target tries to avoid being found as well as rescue or rendezvous problems where the target and the searcher cooperate. Larry Stone wrote his definitive book Theory of Optimal Search in 1975, dealing almost exclusively with the stationary target search problem. Since then the theory has advanced to encompass search for targets that move even as the search proceeds, and computers have developed sufficient capability to employ the improved theory. In this book, Stone joins Royset and Washburn to document and explain this expanded theory of search. The problem of how to search for moving targets arises every day in military, rescue, law enforcement, and border patrol operations.
Author: Adam Safron Publisher: Frontiers Media SA ISBN: 2832536166 Category : Science Languages : en Pages : 392
Book Description
Even before the deep learning revolution, the landscape of artificial intelligence (AI) was already changing drastically in the 90s. Embodied intelligence, it was proposed, must play a crucial role in the design of intelligent machines. This new wave was inspired by what is today known as Embodied and Enactive Cognitive Science or E-Cognition, which considers that cognitive activity does not reduce to the intellectual capacities of agents being able to represent their environments. E-cognition set AI and robotics in a new direction, in which intelligent machines are required to interact with the environment, and where this interaction does not reduce to explicit representations or prespecified algorithms. These ideas revolutionized the way we think about intelligent machines and cognition, but these theoretical advances are only partially reflected in modern approaches to AI and machine learning (ML). Despite deeply impressive achievements, AI/ML still struggles to recapitulate the kinds of intelligence we find in natural systems, whether we are considering individual insects (e.g. simultaneous localization and mapping), or swarm behaviour (e.g. forum sensing and ensemble inferences), and especially the kinds of flexibility and high-level reasoning characteristic of human cognition.
Author: Fumiya Iida Publisher: Springer Nature ISBN: 3031433602 Category : Computers Languages : en Pages : 506
Book Description
This book constitutes the refereed proceedings of the 24th Annual Conference Towards Autonomous Robotic Systems, TAROS 2023, held in Cambridge, UK, during September 13–15, 2023. The 40 full papers presented in this book were carefully reviewed and selected from 70 submissions. They cover a wide range of different topics such as: agri-food robotics; autonomy; collaborative and service robotics; locomotion and manipulation; machine vision; multi-robot systems; soft robotics; tactile sensing; and teleoperation.
Author: Joseph Carl Foraker Publisher: ISBN: Category : Operations research Languages : en Pages : 172
Book Description
We show how to formulate many continuous time-and-space search problems as generalized optimal control problems, where multiple searchers look for multiple targets. Speci cally, we formulate problems in which we minimize the probability that all of the searchers fail to detect any of the targets during the planning horizon, and problems in which we maximize the expected number of targets detected. We construct discretization schemes to solve these continuous time-and-space problems, and prove that they are consistent approximations. Consistency ensures that global minimizers, local minimizers, and stationary points of the discretized problems converge to global minimizers, local minimizers, and stationary points, respectively, of the original problems. We also investigate the rate of convergence of algorithms based on discretization schemes as a computing budget tends to in nity. We provide numerical results to show that our discretization schemes are computationally tractable, including examples with three searchers and ten targets. We develop three heuristics for real-time search planning, one based on our discretization schemes, and two based on polynomial tting methods, and compare the three methods to determine which solution technique would be best suited for use onboard unmanned platforms for automatic route generation for search missions.
Author: Kenzo Nonami Publisher: Springer Science & Business Media ISBN: 4431542760 Category : Technology & Engineering Languages : en Pages : 306
Book Description
The International Conference on Intelligent Unmanned Systems 2011 was organized by the International Society of Intelligent Unmanned Systems and locally by the Center for Bio-Micro Robotics Research at Chiba University, Japan. The event was the 7th conference continuing from previous conferences held in Seoul, Korea (2005, 2006), Bali, Indonesia (2007), Nanjing, China (2008), Jeju, Korea (2009), and Bali, Indonesia (2010). ICIUS 2011 focused on both theory and application, primarily covering the topics of robotics, autonomous vehicles, intelligent unmanned technologies, and biomimetics. We invited seven keynote speakers who dealt with related state-of-the-art technologies including unmanned aerial vehicles (UAVs) and micro air vehicles (MAVs), flapping wings (FWs), unmanned ground vehicles (UGVs), underwater vehicles (UVs), bio-inspired robotics, advanced control, and intelligent systems, among others. This book is a collection of excellent papers that were updated after presentation at ICIUS2011. All papers that form the chapters of this book were reviewed and revised from the perspective of advanced relevant technologies in the field. The aim of this book is to stimulate interactions among researchers active in the areas pertinent to intelligent unmanned systems.
Author: Toru Ishida Publisher: Springer ISBN: 0585345074 Category : Computers Languages : en Pages : 137
Book Description
Autonomous agents or multiagent systems are computational systems in which several computational agents interact or work together to perform some set of tasks. These systems may involve computational agents having common goals or distinct goals. Real-Time Search for Learning Autonomous Agents focuses on extending real-time search algorithms for autonomous agents and for a multiagent world. Although real-time search provides an attractive framework for resource-bounded problem solving, the behavior of the problem solver is not rational enough for autonomous agents. The problem solver always keeps the record of its moves and the problem solver cannot utilize and improve previous experiments. Other problems are that although the algorithms interleave planning and execution, they cannot be directly applied to a multiagent world. The problem solver cannot adapt to the dynamically changing goals and the problem solver cannot cooperatively solve problems with other problem solvers. This book deals with all these issues. Real-Time Search for Learning Autonomous Agents serves as an excellent resource for researchers and engineers interested in both practical references and some theoretical basis for agent/multiagent systems. The book can also be used as a text for advanced courses on the subject.
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Author: Sara Pérez Carabaza
Author: Ziyi Wang
Author: Michael Kamp
Author: Lawrence D. Stone
Author: Adam Safron
Author: Fumiya Iida
Author: Joseph Carl Foraker
Author: Kenzo Nonami
Author: Toru Ishida