Mobile robots range from the Mars Pathfinder mission's teleoperated Sojourner to the cleaning robots in the Paris Metro. This text offers students and other interested readers an introduction to the fundamentals of mobile robotics, spanning the mechanical, motor, sensory, perceptual, and cognitive layers the field comprises. The text focuses on mobility itself, offering an overview of the mechanisms that allow a mobile robot to move through a real world environment to perform its tasks, including locomotion, sensing, localization, and motion planning. It synthesizes material from such fields as kinematics, control theory, signal analysis, computer vision, information theory, artificial intelligence, and probability theory. The book presents the techniques and technology that enable mobility in a series of interacting modules. Each chapter treats a different aspect of mobility, as the book moves from low-level to high-level details. It covers all aspects of mobile robotics, including software and hardware design considerations, related technologies, and algorithmic techniques.] This second edition has been revised and updated throughout, with 130 pages of new material on such topics as locomotion, perception, localization, and planning and navigation. Problem sets have been added at the end of each chapter. Bringing together all aspects of mobile robotics into one volume, Introduction to Autonomous Mobile Robots can serve as a textbook or a working tool for beginning practitioners.

http://www.gbv.de/dms/hebis-darmstadt/toc/120387220.pdf

Introduction to Autonomous Mobile Robots

Multiagent systems are made up of multiple interacting intelligent agents -- computational entities to some degree autonomous and able to cooperate, compete, communicate, act flexibly, and exercise control over their behavior within the frame of their objectives They are the enabling technology for a wide range of advanced applications relying on distributed and parallel processing of data, information, and knowledge relevant in domains ranging from industrial manufacturing to e-commerce to health care This book offers a state-of-the-art introduction to multiagent systems, covering the field in both breadth and depth, and treating both theory and practice It is suitable for classroom use or independent study This second edition has been completely revised, capturing the tremendous developments in multiagent systems since the first edition appeared in 1999 Sixteen of the book's seventeen chapters were written for this edition; all chapters are by leaders in the field, with each author contributing to the broad base of knowledge and experience on which the book rests The book covers basic concepts of computational agency from the perspective of both individual agents and agent organizations; communication among agents; coordination among agents; distributed cognition; development and engineering of multiagent systems; and background knowledge in logics and game theory Each chapter includes references, many illustrations and examples, and exercises of varying degrees of difficulty The chapters and the overall book are designed to be self-contained and understandable without additional material Supplemental resources are available on the book's Web site Contributors:Rafael Bordini, Felix Brandt, Amit Chopra, Vincent Conitzer, Virginia Dignum, Jurgen Dix, Ed Durfee, Edith Elkind, Ulle Endriss, Alessandro Farinelli, Shaheen Fatima, Michael Fisher, Nicholas R Jennings, Kevin Leyton-Brown, Evangelos Markakis, Lin Padgham, Julian Padget, Iyad Rahwan, Talal Rahwan, Alex Rogers, Jordi Sabater-Mir, Yoav Shoham, Munindar P Singh, Kagan Tumer, Karl Tuyls, Wiebe van der Hoek, Laurent Vercouter, Meritxell Vinyals, Michael Winikoff, Michael Wooldridge, Shlomo Zilberstein

Multiagent Systems

The majority of the work carried out in the formal methods community throughout the last three decades has (for good reasons) been devoted to special languages designed to make it easier to experiment with mechanized formal methods such as theorem provers and model checkers. In this paper, we give arguments for why we believe it is time for the formal methods community to shift some of its attention towards the analysis of programs written in modern programming languages. In keeping with this philosophy, we have developed a verification and testing environment for Java, called Java PathFinder (JPF), which integrates model checking, program analysis and testing. Part of this work has consisted of building a new Java Virtual Machine that interprets Java bytecode. JPF uses state compression to handle large states, and partial order reduction, slicing, abstraction and run-time analysis techniques to reduce the state space. JPF has been applied to a real-time avionics operating system developed at Honeywell, illustrating an intricate error, and to a model of a spacecraft controller, illustrating the combination of abstraction, run-time analysis and slicing with model checking.

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Model checking programs

Fast Downward is a classical planning system based on heuristic search. It can deal with general deterministic planning problems encoded in the propositional fragment of PDDL2.2, including advanced features like ADL conditions and effects and derived predicates (axioms). Like other well-known planners such as HSP and FF, Fast Downward is a progression planner, searching the space of world states of a planning task in the forward direction. However, unlike other PDDL planning systems, Fast Downward does not use the propositional PDDL representation of a planning task directly. Instead, the input is first translated into an alternative representation called multivalued planning tasks, which makes many of the implicit constraints of a propositional planning task explicit. Exploiting this alternative representation, Fast Downward uses hierarchical decompositions of planning tasks for computing its heuristic function, called the causal graph heuristic, which is very different from traditional HSP-like heuristics based on ignoring negative interactions of operators.

In this article, we give a full account of Fast Downward's approach to solving multivalued planning tasks. We extend our earlier discussion of the causal graph heuristic to tasks involving axioms and conditional effects and present some novel techniques for search control that are used within Fast Downward's best-first search algorithm: preferred operators transfer the idea of helpful actions from local search to global best-first search, deferred evaluation of heuristic functions mitigates the negative effect of large branching factors on search performance, and multiheuristic best-first search combines several heuristic evaluation functions within a single search algorithm in an orthogonal way. We also describe efficient data structures for fast state expansion (successor generators and axiom evaluators) and present a new non-heuristic search algorithm called focused iterative-broadening search, which utilizes the information encoded in causal graphs in a novel way.

Fast Downward has proven remarkably successful: It won the "classical" (i. e., propositional, non-optimising) track of the 4th International Planning Competition at ICAPS 2004, following in the footsteps of planners such as FF and LPG. Our experiments show that it also performs very well on the benchmarks of the earlier planning competitions and provide some insights about the usefulness of the new search enhancements.

https://www.aaai.org/Papers/JAIR/Vol26/JAIR-2606.pdf

The fast downward planning system

Particle filters (PFs) are powerful sampling-based inference/learning algorithms for dynamic Bayesian networks (DBNs). They allow us to treat, in a principled way, any type of probability distribution, nonlinearity and non-stationarity. They have appeared in several fields under such names as "condensation", "sequential Monte Carlo" and "survival of the fittest". In this paper, we show how we can exploit the structure of the DBN to increase the efficiency of particle filtering, using a technique known as Rao-Blackwellisation. Essentially, this samples some of the variables, and marginalizes out the rest exactly, using the Kalman filter, HMM filter, junction tree algorithm, or any other finite dimensional optimal filter. We show that Rao-Blackwellised particle filters (RBPFs) lead to more accurate estimates than standard PFs. We demonstrate RBPFs on two problems, namely non-stationary online regression with radial basis function networks and robot localization and map building. We also discuss other potential application areas and provide references to some finite dimensional optimal filters.

Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks.

Remote Agent: to boldly go where no AI system has gone before

This paper describes Livingstone, an implemented kernel for a model-based reactive self-configuring autonomous system. It presents a formal characterization of Livingstone's representation formalism, and reports on our experience with the implementation in a variety of domains. Livingstone provides a reactive system that performs significant deduction in the sense/response loop by drawing on our past experience at building fast propositional conflict-based algorithms for model-based diagnosis, and by framing a model-based configuration manager as a propositional feedback controller that generates focused, optimal responses. Livingstone's representation formalism achieves broad coverage of hybrid hardware/software systems by coupling the transition system models underlying concurrent reactive languages with the qualitative representations developed in model-based reasoning. Livingstone automates a wide variety of tasks using a single model and a single core algorithm, thus making significant progress towards achieving a central goal of model-based reasoning. Livingstone, together with the HSTS planning and scheduling engine and the RAPS executive, has been selected as part of the core autonomy architecture for NASA's first New Millennium spacecraft.

/pdf/a-model-based-approach-to-reactive-self-configuring-systems-4yjq764ly7.pdf

A model-based approach to reactive self-configuring systems

A new generation of reactive, model-based executives are emerging that make extensive use of componentbased declarative models to analyze anomalous situations and generate novel sequences for the internal control of complex autonomous systems. Burton, a generative, model-based planner offers a core element that bridges the gap between current and target states within the reactive loop. Burton is a sound, complete, reactive planner that generates a single control action of a valid plan in average case constant time, and compensates for anomalies at every step. Burton will not generate irreversible, potentially damaging sequences, except to effect repairs. We present model compilation, causal analysis, and online policy construction methods that are key to Burton's performance.

https://ijcai.org/Proceedings/97-2/Papers/056.pdf

A reactive planner for a model-based executive

This paper describes the Remote Agent flight experiment for spacecraft commanding and control. In the Remote Agent approach, the operational rules and constraints are encoded in the flight software. The software may be considered to be an autonomous "remote agent" of the spacecraft operators in the sense that the operators rely on the agent to achieve particular goals. The experiment will be executed during the flight of NASA's Deep Space One technology validation mission. During the experiment, the spacecraft will not be given the usual detailed sequence of commands to execute. Instead, the spacecraft will be given a list of goals to achieve during the experiment. In flight, the Remote Agent flight software will generate a plan to accomplish the goals and then execute the plan in a robust manner while keeping track of how well the plan is being accomplished. During plan execution, the Remote Agent stays on the lookout for any hardware faults that might require recovery actions or replanning. In addition to describing the design of the remote agent, this paper discusses technology-insertion challenges and the approach used in the Remote Agent approach to address these challenges. The experiment integrates several spacecraft autonomy technologies developed at NASA Ames and the Jet Propulsion Laboratory: on-board planning, a robust multi threaded executive, and model-based failure diagnosis and recovery.

/pdf/design-of-the-remote-agent-experiment-for-spacecraft-4zmaza9dzv.pdf

Design of the Remote Agent experiment for spacecraft autonomy

This paper describes the New Millennium Remote Agent (NMRA) architecture for autonomous spacecraft control systems The architecture supports challenging requirements of the autonomous spacecraft domain not usually addressed in mobile robot architectures, including highly reliable autonomous operations over extended time periods in the presence of tight resource constraints, hard deadlines, limited observability, and concurrent activity A hybrid architecture, NMRA integrates traditional real-time monitoring and control with heterogeneous components for constraint-based planning and scheduling, robust multi-threaded execution, and model-based diagnosis and reconfiguration Novel features of this integrated architecture include support for robust closed-loop generation and execution of concurrent temporal plans and a hybrid procedural/deductive executive

P. Pandurang Nayak

Papers

Remote Agent: to boldly go where no AI system has gone before

A model-based approach to reactive self-configuring systems

A reactive planner for a model-based executive

Design of the Remote Agent experiment for spacecraft autonomy

An autonomous spacecraft agent prototype