Download or read book Multi objective Path planning for Autonomous Agents Using Dynamic Game Theory written by Jhanani Selvakumar and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous systems which are designed to assist humans in complex environments, are often required to reliably operate under uncertainty. When probabilistic models for uncertainty are not available, the game-theoretic framework for adversarial/cooperative interactions allows us to solve problems for autonomous systems, such as control of uncertain dynamical systems, modeling biological systems, and deployment of sensor networks. This work focuses on decision-making and control problems for autonomous agents in uncertain environments. Characteristic sources of such uncertainty are wind or oceanic flows, radiation fields, and moving obstacles. In our approach, we model the agent-environment interactions induced by these sources of uncertainty as the actions of an adversary, which tries to prevent the agent from achieving its objective (e.g., reaching a target location). This modeling naturally leads to the formulation of a dynamic game between the autonomous agent and its environment. Control problems of autonomous agents that are subject to uncertain dynamic influences such as strong winds, fit into the structure of two-player zero-sum differential games. Many modern decision-making problems, however, cannot be put under the umbrella of zero-sum games because they involve complex interplay between multiple agents, which is not purely antagonistic. In this context, we address a special class of decision-making and path-planning problems, for autonomous agents that aim to reach a specified target set while avoiding multiple adversarial elements (such as mobile agents or obstacles). This class of problems, referred to as reach-avoid problems, corresponds to multi-player non-zero-sum dynamic games. Multi-player dynamic games typically require solving coupled partial differential equations, which is computationally and temporally expensive, if at all tractable. This intractability is particularly true, for problems of high dimensionality, and if there are agents in the game which have multiple objectives. For this reason, approximate solutions to dynamic multi-agent games are desirable in practice. Considering the binary objective of our agent of interest, we propose three approaches to the path-planning problem. Each approach is based on the characterization of risk to the agent, and uses a distinct method to determine a feasible solution to the multi-agent game. First, we propose an approximate divide-and-conquer approach that allows us to compute the global path for the agent of interest by concatenating local paths computed on a dynamic graph-abstraction of the environment. Through extensive simulations, we have demonstrated the effectiveness of the proposed approach. However, the proposed method does not guarantee global optimality or completeness of the solution, and also incurs considerable computational cost at each step. To improve computational tractability of the path-planning problem, next, we propose a feedback strategy based on greedy minimization of risk, where the risk metric is characterized with regard to the dual objective of the agent of interest. The same risk metric also aids us in partitioning the state-space of the game, which is useful to infer the outcome of the game from its initial conditions. The feedback strategy is computationally simple. Further, through numerical simulations, this approach has been found to be effective in a large number of cases, in guiding the autonomous vehicle to its target set. In order to further improve the target-reaching capability of the autonomous agent, we propose a third approach, a reduction of the dynamic multi-player game to a sequence of single-act games, one played at each time step. The proposed approach is also easy to implement and also does not incur significant loss of optimality. At each step, the optimal set of player strategies can be calculated efficiently and reliably via convex programming tools. More importantly, the proposed sequential formulation of the dynamic game allows us to account for the effect of the current actions of the agents on the final outcome of the original dynamic game. However, the payoffs of future games are altered by the past games and consequently, the equilibria for the single-act games (stage-wise equilibria) might not be optimal when the dynamic game is viewed as a whole. The choice of stage-wise equilibria can be improved by recording past actions and their effect on future payoffs. Drawing upon the history of actions and outcome patterns if any, we can learn to make better choices in the present. For multi-agent games with multiple non-aligned objectives for each agent, learning processes can aid in high-level switching between the optimal strategies corresponding to individual objectives. We propose the use of model-free reinforcement learning methods to obtain a feedback policy for the agent of interest. The challenges here, are to characterize an appropriate reward function, particularly under consideration of multiple objectives for the agent, and also to optimize parameters of the learning process. The goal of this thesis is to contribute a solid framework, which is based on game theory, and combines analytical and computational techniques, to address the problem of path-planning for an autonomous agent with multiple objectives in uncertain environments

Download or read book Progress in Artificial Intelligence written by Carlos Bento and published by Springer Science & Business Media. This book was released on 2005-11-30 with total page 708 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 12th Portuguese Conference on Artificial Intelligence, EPIA 2005, held in Covilhã, Portugal in December 2005 as nine integrated workshops. The 58 revised full papers presented were carefully reviewed and selected from a total of 167 submissions. In accordance with the nine constituting workshops, the papers are organized in topical sections on general artificial intelligence (GAIW 2005), affective computing (AC 2005), artificial life and evolutionary algorithms (ALEA 2005), building and applying ontologies for the semantic Web (BAOSW 2005), computational methods in bioinformatics (CMB 2005), extracting knowledge from databases and warehouses (EKDB&W 2005), intelligent robotics (IROBOT 2005), multi-agent systems: theory and applications (MASTA 2005), and text mining and applications (TEMA 2005).

Download or read book Motion Planning written by Edgar A. Martínez García and published by BoD – Books on Demand. This book was released on 2022-01-26 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt: Motion planning is a fundamental function in robotics and numerous intelligent machines. The global concept of planning involves multiple capabilities, such as path generation, dynamic planning, optimization, tracking, and control. This book has organized different planning topics into three general perspectives that are classified by the type of robotic applications. The chapters are a selection of recent developments in a) planning and tracking methods for unmanned aerial vehicles, b) heuristically based methods for navigation planning and routes optimization, and c) control techniques developed for path planning of autonomous wheeled platforms.

Download or read book Multi objective Mapping and Path Planning Using Visual SLAM and Object Detection written by Ami Woo and published by . This book was released on 2019 with total page 93 pages. Available in PDF, EPUB and Kindle. Book excerpt: Path planning of the autonomous robots is one of the crucial tasks that need to be achieved for mobile robots to navigate through the environment intelligently. The robot paths are typically planned utilizing map that is accessible at the time with a certain optimization objective such as to minimizing the travel distance, or time. This thesis proposes a multi-objective path planning approach by integrating Simultaneous Localization And Mapping (SLAM) with a graph based optimization approach and an object detection algorithm. The proposed approach aims not only to nd a path that minimizes travel distance but also to minimize the number of obstacles in the path to be followed. This thesis uses Visual SLAM (VSLAM) as the basis to generate graphs for global path planning. VSLAM generates a trajectory network which is usually in the form of a spare graph (if odometry based) or probabilistic relations on landmark estimates relative to the robot. An object detection algorithm is run in parallel to provide additional information on trajectory network graphs generated by the VSLAM, to be used in multi-objective path planning. The VSLAM, object detection, and path planning elds are typically studied independently, but this thesis links the these elds to solve the multi-objective path planning problem. The rst part of the thesis presents the connections and methodology on using the VSLAM and object detection to generate trajectory network graphs. The nodes are inserted to the graph when a new keyframe is needed in VSLAM. The distance travelled between the nodes is the rst criterion to minimize and is computed while traversing. In parallel to VSLAM, the object detection component quanti es the number of objects detected between the nodes. Only the pre-trained objects to detect are quanti ed and the trained objects in the thesis are cars and trucks. The number of objects are the two additional edge information added to the graph. Later in the thesis, the multi-objective path planning on the generated graphs is presented. The objective of path planning on graph is not just on minimizing the distance to travel but also on minimizing the number of cars and trucks it passes. The proposed design is tested using KITTI dataset which is specialized for autonomous driving and consists of many cars and trucks. The design is not limited to autonomous driving applications, but can be applied to other elds such as surveillance, rescuing, and many more with di erent objects to detect.

Download or read book Harvest time Optimal Path Planning in Dynamic Flows written by Manmeet Singh Bhabra and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The past decade has seen an increasing use of autonomous vehicles (propelled AUVs, ocean gliders, solar-vehicles, etc.) in marine applications. For the operation of these vehicles, efficient methods for path planning are critical. Path planning, in the most general sense, corresponds to a set of rules to be provided to an autonomous robot for navigating from one configuration to another in some optimal fashion. Increasingly, having autonomous vehicles that optimally collect/harvest external fields from highly dynamic environments has grown in relevance. Autonomously maximizing the harvest in minimum time is our present path planning objective. Such optimization has numerous impactful applications. For instance, in the case of energy optimal path planning where long endurance and low power are crucial, it is important to be able to optimally harvest energy (solar, wind, wave, thermal, etc.) along the way and/or leverage the environment (winds, currents, etc.) to reduce energy expenditure. Similarly, autonomous marine cleanup or collection vehicles, tasked with harvesting plastic waste, oil spills, or seaweed fields, need to be able to plan paths that maximize the amount of material harvested in order to optimize the cleanup or collection process. In this work, we develop an exact partial differential equation-based methodology that predicts harvest-time optimal paths for autonomous vehicles navigating in dynamic environments. The governing differential equations solve the multi-objective optimization problem of navigating a vehicle autonomously in a highly dynamic flow field to any destination with the goal of minimizing travel time while also maximizing the amount harvested by the vehicle. Using Hamilton-Jacobi theory for reachability, our methodology computes the exact set of Pareto optimal solutions to the multiobjective path planning problem. This is completed by numerically solving a reachability problem for the autonomous vehicle in an augmented state space consisting of the vehicle's position in physical space as well as its harvest state. Our approach is applicable to path planning in various environments, however we primarily present examples of navigating in dynamic ocean flows. The following cases, in particular, are studied. First, we validate our methodology using a benchmark case of planning paths through a region with a harvesting field present in a halfspace, as this case admits a semi-analytical solution that we compare to the results of our method. We next consider a more complex unsteady environment as we solve for harvest-time optimal missions in a quasi-geostrophic double-gyre ocean flow field. Following this, we provide harvest-time optimal paths to the highly relevant issue of collecting harmful algae blooms. Our final case considers an application to next generation offshore aquaculture technologies. In particular, we consider in this case path planning of an offshore moving fish farm that accounts for optimizing fish growth. Overall, we find that our exact planning equations and efficient schemes are promising to address several pressing challenges for our planet.

Download or read book From Active Data Management to Event Based Systems and More written by Kai Sachs and published by Springer. This book was released on 2010-11-18 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data management has evolved over the years from being strictly associated with database systems, through active databases, to become a topic that has grown beyond the scope of a single field encompassing a large range of subjects, such as distributed systems, event-driven systems, and peer-to-peer and streaming systems. The present collection of works, which sheds light on various facets of data management, is dedicated to Prof. Alejandro Buchmann on the occasion of his 60th birthday. His scientific path looks back on more than thirty years of successful academic life and high-impact research. With this book we celebrate Prof. Buchmann's vision and achievements.

Download or read book Human Robot Interaction written by Christoph Bartneck and published by Cambridge University Press. This book was released on 2020-05-07 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: This broad overview for graduate students introduces multidisciplinary topics from robotics to sociology which are needed to understand the area.

Download or read book Interdisciplinary Approaches to the Structure and Performance of Interdependent Autonomous Human Machine Teams and Systems A HMT S written by William Frere Lawless and published by Frontiers Media SA. This book was released on 2023-03-30 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Safety And Reliability In Cooperating Unmanned Aerial Systems written by Camille Alain Rabbath and published by World Scientific. This book was released on 2010-01-25 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive overview of recent advances in the analysis and design of health management systems for cooperating unmanned aerial vehicles. Such systems rely upon monitoring and fault adaptation schemes. Motivation for their study comes from the fact that, despite the use of fault-tolerant control software and hardware embedded onboard air vehicles, overall fleet performance may still be degraded after the occurrence of anomalous events such as systems faults and failures. Cooperative health management (CHM) systems seek to provide adaptation to the presence of faults by capitalizing on the availability of interconnected computing, sensing and actuation resources.This monograph complements the proposed CHM concepts by means of case studies and application examples. It presents fundamental principles and results encompassing optimization, systems theory, information theory, dynamics, modeling and simulation. Written by pioneers in cooperative control, health management and fault-tolerant control for unmanned systems, this book is a unique source of information for designers, researchers and practitioners interested in the field.

Download or read book ECAI 2023 written by K. Gal and published by IOS Press. This book was released on 2023-10-18 with total page 3328 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence, or AI, now affects the day-to-day life of almost everyone on the planet, and continues to be a perennial hot topic in the news. This book presents the proceedings of ECAI 2023, the 26th European Conference on Artificial Intelligence, and of PAIS 2023, the 12th Conference on Prestigious Applications of Intelligent Systems, held from 30 September to 4 October 2023 and on 3 October 2023 respectively in Kraków, Poland. Since 1974, ECAI has been the premier venue for presenting AI research in Europe, and this annual conference has become the place for researchers and practitioners of AI to discuss the latest trends and challenges in all subfields of AI, and to demonstrate innovative applications and uses of advanced AI technology. ECAI 2023 received 1896 submissions – a record number – of which 1691 were retained for review, ultimately resulting in an acceptance rate of 23%. The 390 papers included here, cover topics including machine learning, natural language processing, multi agent systems, and vision and knowledge representation and reasoning. PAIS 2023 received 17 submissions, of which 10 were accepted after a rigorous review process. Those 10 papers cover topics ranging from fostering better working environments, behavior modeling and citizen science to large language models and neuro-symbolic applications, and are also included here. Presenting a comprehensive overview of current research and developments in AI, the book will be of interest to all those working in the field.

Download or read book Multi objective Intent based Path Planning for Robots for Static and Dynamic Environments written by Meher Talat Shaikh and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Key contributions of the dissertation are: (i) design and evaluation of different user interfaces to express intent, (ii) use of two different metrics, cosine similarity and intent threshold margin, that help quantify intent, and (iii) application of the metrics in path (re)planning to detect intent mismatches for a robot navigating in a dynamic environment. A set of user studies including both controlled laboratory experiments and Amazon Mechanical Turk studies were conducted to evaluate each of these dissertation components.

Download or read book Electronics Communications and Networks IV written by Amir Hussain and published by CRC Press. This book was released on 2015-07-01 with total page 1868 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 4th International Conference on Electronic, Communications and Networks (CECNet2014) inherits the fruitfulness of the past three conferences and lays a foundation for the forthcoming next year in Shanghai. CECNet2014 was hosted by Hubei University of Science and Technology, China, with the main objective of providing a comprehensive global foru

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 Energy time Optimal Path Planning in Strong Dynamic Flows written by Manan Mukesh Doshi and published by . This book was released on 2021 with total page 61 pages. Available in PDF, EPUB and Kindle. Book excerpt: We develop an exact partial differential equation-based methodology that predicts time-energy optimal paths for autonomous vehicles navigating in dynamic environments. The differential equations solve the multi-objective optimization problem of navigating a vehicle autonomously in a dynamic flow field to any destination with the goal of minimizing travel time and energy use. Based on Hamilton-Jacobi theory for reachability and the level set method, the methodology computes the exact Pareto optimal solutions to the multi-objective path planning problem, numerically solving the equations governing time-energy reachability fronts and optimal paths. Our approach is applicable to path planning in various scenarios, however we primarily present examples of navigating in dynamic marine environments. First, we validate the methodology through a benchmark case of crossing a steady front (a highway flow) for which we compare our results to semi-analytical optimal path solutions. We then consider more complex unsteady environments and solve for time-energy optimal missions in a quasi-geostrophic double-gyre ocean flow field.

Download or read book Motion in Games written by Ronan Boulic and published by Springer. This book was released on 2010-11-02 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the Second International Workshop on Motion in Games, held in Utrecht, The Netherlands, in November 2010. The 30 revised full papers presented together with 9 revised poster papers in this volume were carefully reviewed and selected. The papers are organized in topical sections on body simulation, learning movements, body control, motion planning, physically-based character control, crowds and formation, geometry, autonomous characters, navigation, motion synthesis, perception, real-time graphics, and posters.

Download or read book Chance constrained Path Planning in Unstructured Environments written by Rachit Aggarwal and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The objective of this dissertation is to develop a framework for chance-constrained path planning in autonomous agents operating in evolving unstructured environments. Path Planning is an important problem in many fields such as robotic manipulators, mobile robotics, scheduling, flight planning, and autonomous cars and aircraft. Often, the presence of external disturbances, measurement errors and/or inadequately modeled processes in the environment can cause uncertainty in characterization of the obstacles' shape, size and location. Traditionally, such unstructured environments are typically modeled using conservative safety margins and posed as constraints or included in the cost function as a penalty. There exist no systematic methods to tune the margins or the cost function with disparate physical meaning, e.g. travel time and safety margin. In this work, the inherent uncertainty in the obstacles is posed as chance-constraints (CC) bounded by the risk of violation of those constraints in an optimal control problem for path planning. Pseudospectral discretization methods are used to transcribe the optimal control problem to a nonlinear program (NLP) which is solved using off-the-shelf optimization solvers. The constrained optimization problems are heavily dependent on a suitable initial guess provided to the solver, which affects both the computation time and optimality of the solution. Triangulation and grid based discrete optimization methods are studied for their merits and employed to generate the initial guesses. It is shown that by varying the risk of violation of obstacle boundaries, a family of solutions can be generated signifying the risk associated with each solution. This approach enables the decision maker to be 'risk-aware' by providing the methodical approach to undertake missions based-on its 'risk-appetite' in the given situation. This idea is then extended to recursive planning for evolving environments. An in-depth example for path planning for small unmanned aerial vehicles (UAVs) flying in a spreading wildfire for situational awareness is studied. An extension to multi-agent operations is also developed. To validate the efficacy of the path planner in real wildfire, a modular multirotor experimental testbed was designed and developed. Field tests demonstrate the validation of the design goals and several performance objectives.

Download or read book Robot Manipulator Control written by Frank L. Lewis and published by CRC Press. This book was released on 2003-12-12 with total page 646 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robot Manipulator Control offers a complete survey of control systems for serial-link robot arms and acknowledges how robotic device performance hinges upon a well-developed control system. Containing over 750 essential equations, this thoroughly up-to-date Second Edition, the book explicates theoretical and mathematical requisites for controls design and summarizes current techniques in computer simulation and implementation of controllers. It also addresses procedures and issues in computed-torque, robust, adaptive, neural network, and force control. New chapters relay practical information on commercial robot manipulators and devices and cutting-edge methods in neural network control.