Download or read book Efficient Simultaneous Task and Motion Planning for Multiple Mobile Robots Using Task Reachability Graphs written by Brad Woosley and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, we consider the problem of efficient navigation by robots in initially unknown environments while performing tasks at certain locations. In initially unknown environments, the path plans might change dynamically as the robot discovers obstacles aong its route. Because robots have limited energy, adaptations to the task schedule of the robot in conjuntion with updates to its path plan are required so that the robot can perform its tasks while reducing time and energy expended. However, mosty existing techniques consider robot path planning and task planning as separate problems. This thesis plans to bridge this gap by developing a unified approach for navigating multiple robots in uncertain environments. We first formalize this as a problem called task ordering with path uncertainty (TOP-U) where robots are provided with a set of task locations to visit in a bounded environment, but the length of the path between a pair of task locations is initially known only coarsely by the robots. The robots must find the order of tasks that reduces the path length to visit the task locations. We then propose an abstraction called a task reachability graph (TRG) that integrates the robots task ordering and path planning. The TRG is updated dynamically based on inter-task path costs calculated by the path planner. A Hidden Markov Model-based technique calculates the belief in the current path costs based on the environment perceived by the robot's sensors. We then describe a Markov Decision-based algorithm used by each robot in a distributed manner to reason about the path lengths between tasks and select the paths that reduce the overall path length to vist the task locations. We have evaluated our algorithm in simulated and hardware robots. Our results show that the TRG-based approach performs up to 60% better in planning and locomotion times with 44% fewer replans, while traveling almost-similar distances as compared to a greedy, nearest task-first selection algorithm.

Download or read book Generative Multi robot Task and Motion Planning Over Long Horizons written by Enrique Fernández González (Ph. D.) and published by . This book was released on 2018 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: The state of the art practice in robotics planning is to script behaviors manually, where each behavior is typically precomputed in advance. However, in order for robots to be able to act robustly and adapt to novel situations, they need to be able to plan sequences of behaviors and activities autonomously. Since the conditions and effects of these behaviors are tightly coupled through time, state and control variables, many problems require that the tasks of activity planning and trajectory optimization are considered together. There are two key issues underlying effective hybrid activity and trajectory planning: the sufficiently accurate modeling of robot dynamics and the capability of planning over long horizons. Hybrid activity and trajectory planners that employ mixed integer programming within a discrete time formulation are able to accurately model complex dynamics for robot vehicles, but are often restricted to relatively short horizons. On the other hand, current hybrid activity planners that employ continuous time formulations can handle longer horizons but they only allow actions to have continuous effects with constant rate of change, and restrict the allowed state constraints to linear inequalities. This greatly limits the expressivity of the problems that these approaches can solve. In this work we present Scotty, a planning system for hybrid activity and trajectory planning problems. Unlike other continuous time planners, Scotty can solve a broad class of expressive robotic planning problems by supporting convex quadratic constraints on state variables and control variables that are jointly constrained and that affect multiple state variables simultaneously. In order to efficiently generate practical plans for coordinated mobile robots over long horizons, our approach employs recent methods in convex optimization combined with methods for planning with relaxed planning graphs and heuristic forward search. The contributions of this thesis are threefold. First, we introduce a convex, goal-directed scheduling and trajectory planning problem. To solve this problem, we present the ScottyConvexPath planner, which reformulates the problem as a Second Order Cone Program (SOCP). Our formulation allows us to efficiently compute robot trajectories with first order dynamics over long horizons. While straightforward formulations are not convex, we present a convex model that does not require state, control or time discretization. Second, we introduce the ScottyActivity planner, a state of the art hybrid activity and trajectory planner that interleaves heuristic forward search with delete relaxations and consistency checks using our convex model. Finally, we present ScottyPath, a qualitative state plan planner that computes control and obstacle-free state trajectories for robots in order to satisfy the temporally extended goals and constraints that ScottyActivity imposes. ScottyPath finds obstacle-free paths in which all robots are guaranteed to always remain within obstacle-free safe regions, which are computed in advance. We introduce several new robotic planning domains, that we use to evaluate the scalability of our planning system and compare the performance of our approach against other prior methods. Our results show that ScottyActivity performs similarly to other state of the art heuristic forward search activity planners, while solving much more expressive robotic planning problems. On the other hand, ScottyPath can generate obstacle-free paths where robots are contained in obstacle-free convex regions more than two orders of magnitude faster than alternative mixed-integer approaches.

Download or read book Task Based Global Motion Planning of Multiple Manipulators in Time varying Environments written by Achint Aggarwal and published by . This book was released on 2009 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The field of robotics is clearly seen to be heading towards a world in which humans and robots can exist together safely and coherently be it in industries, households or battlefields. The primary obstacle for this harmonistic coexistence is the inability of robots to interact effectively and safely with the environment. Human machine interaction still seems to be far off until the development of effective and reliable robots that understand their environment and interact with it safely. The application of robots in industrial environments has over the last two decades received a significant impetus, such that the robots can now perform pre-programmed and repetitive low-valued tasks almost to perfection. However, the need of the hour is to make robots go beyond this level and practically start augmenting human capability and reducing human drudgery even outside the factory floor. This makes robot motion planning one of the most important areas in robotics research. Without a motion planner for manipulators and mobile robots, human operators have to constantly monitor and define their detailed motion. An automatic motion planner will relieve the operators from this tedious job and enable them to exercise control at a supervisory level. This in turn, increases efficiency by eliminating human errors. Further, path planning can be considered to be the backbone of any motion planning algorithm. An efficient path planner with added performance criteria and constraints makes an efficient motion planner. This specific research effort of developing a motion planner falls within the broader goal of developing a safety architecture that enables high performance intelligent machines that coexist and cooperate safely with other systems and humans. A large body of research in the area of robotics focuses on the path planning of mobile robots while the manipulators' ability to effectively operate autonomously in cluttered and dynamically changing environments stays relatively under explored. This work will rely on the past achievements in collision detection, obstacle avoidance and motion planning of the Robotics Research Group (RRG) at the University of Texas at Austin (UT Austin). This area of research offers chances for a wide breadth of advancement in robotics as it can be applied to traditional stationary robots, both redundant and non-redundant manipulators, as well as to mobile manipulators where both the path of the manipulator itself and the path of the manipulator's mobile base must be accounted for when attempting to plan collision free paths.

Download or read book Mobile Robots Navigation written by Luis Payá and published by MDPI. This book was released on 2020-11-13 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: The presence of mobile robots in diverse scenarios is considerably increasing to perform a variety of tasks. Among them, many developments have occurred in the fields of ground, underwater, and flying robotics. Independent of the environment where they move, navigation is a fundamental ability of mobile robots so that they can autonomously complete high-level tasks. This problem can be efficiently addressed through the following actions: First, it is necessary to perceive the environment in which the robot has to move, and extract some relevant information (mapping problem). Second, the robot must be able to estimate its position and orientation within this environment (localization problem). With this information, a trajectory toward the target points must be planned (path planning), and the vehicle must be reactively guided along this trajectory considering either possible changes or interactions with the environment or with the user (control). Given this information, this book introduces current frameworks in these fields (mapping, localization, path planning, and control) and, in general, approaches to any problem related to the navigation of mobile robots, such as odometry, exploration, obstacle avoidance, and simulation.

Download or read book Cooperative Navigation for Teams of Mobile Robots written by Mike Peasgood and published by . This book was released on 2007 with total page 143 pages. Available in PDF, EPUB and Kindle. Book excerpt: Teams of mobile robots have numerous applications, such as space exploration, underground mining, warehousing, and building security. Multi-robot teams can provide a number of practical benefits in such applications, including simultaneous presence in multiple locations, improved system performance, and greater robustness and redundancy compared to individual robots. This thesis addresses three aspects of coordination and navigation for teams of mobile robots: localization, the estimation of the position of each robot in the environment; motion planning, the process of finding collision-free trajectories through the environment; and task allocation, the selection of appropriate goals to be assigned to each robot. Each of these topics are investigated in the context of many robots working in a common environment.

Download or read book Hybrid Concurrent Planning with Heterogeneous Robot Teams for Timed Goals written by Jingkai Chen and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robotic techniques in terms of mechanical design, perception, and planning have been improving at a dramatic rate. However, these advances for single autonomous systems will not be sufficient for many real-world applications, in which robots need to collaboratively complete tasks together given their different capabilities, while avoiding conflicts in terms of competing energy resources and occupied space, such as a cooperative truck-and-drone delivery system and smart warehouse. In these scenarios, we need not only to assign timed goals to robots but also plan all the robots to achieve these goals. Planning and coordinating for robot teams to complete multiple timed goals in a long horizon is challenging: (1) reasoning over hybrid systems with both discrete and continuous specifications; (2) coordinating multiple robots and timed goals; (3) optimizing towards high-quality solutions. As a result, the solution space of this problem is huge and features combinatorial and nonlinear behaviours, and finding feasible solutions is nontrivial not to say finding high-quality ones. There are two streams of research that address this problem: (1) hybrid activity planning, which plans both the symbolic and numeric parts of a system; (2) multi-robot motion planning, which targets at coordinating collision-free motions of a large fleet of vehicles. While the first line of research mainly considers limited numeric parts, multi-robot motion planning lacks the ability to reason over high-level activities. Few of them can completely solve our problems. In this thesis, I address this problem by adopting a two-stage hierarchical planning framework, which combines the strengths of both lines of research mentioned above. This planning framework divides the planning procedure into two stages: (1) high-level hybrid activity planning: optimally plan the activities of all the robots by using centralized algorithms, while partially considering robot dynamics, such as approximating nonlinear dynamics to be linear; (2) low-level multi-robot motion planning in support of activities and deadlines: given the planned activities, plan safe, executable control trajectories of all the robots in a decoupled way. This two-stage design is efficient, while being reasonably effective and complete: (1) to be efficient, the high-level planner properly approximates robot dynamics, and the low-level planner decouples the planning problem to single-activity subproblems and only coordinates them on demand; (2) the high-level planner is guaranteed to generate optimal decisions over activities and the low-level planner grounds these planned activities with an optimization purpose; (3) to mitigate the incompleteness due to the hierarchy, we provide rich information for making high-level decisions. Under this framework, this thesis has three major contributions: (1) an optimal hybrid activity planner, cKongming, that represents all the possible robot trajectories as a temporal hybrid flow graph and further encode and solve as a Mixed Integer Linear Program (MILP). Key to cKongming's efficiency and effectiveness is its graph representation, which combines the hybrid flow graphs of the PDDL-K planner Kongming, to be effective, and the adaptive-duration action representation in PDDL 2.1 planners, to be efficient. (2) a scalable, effective multi-robot motion coordinator for activity plans, which extends the priority-based single-goal coordination that coordinates by specifying priorities between agents, to handle multiple activities with temporal constraints. Key to achieving this is to specify priorities between individual activities, rather than the complete activity plans of different robots. (3) an assembly planner that automatically plans multiple high-dimensional manipulators for assembly tasks. The planner is built under the same two-stage hierarchical planning framework. The activity planner simplifies the hybrid planner to a temporal planner and leverages an analogy to solving Vehicle Routing Problem with Time Windows to achieve efficiency. The task-directed motion planner tailors our general motion coordinator by allowing more aggressive single-activity replanning and look-ahead for future reachability. In the demonstration, we can plan assembly plans for up to three robots and 23 objects in a couple minutes.

Download or read book Motion Planning for Multiple Mobile Robots Using Time Scaling written by István Komlósi and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Robotics Research written by Nancy M. Amato and published by Springer Nature. This book was released on 2019-11-28 with total page 1058 pages. Available in PDF, EPUB and Kindle. Book excerpt: ISRR, the "International Symposium on Robotics Research", is one of robotics pioneering Symposia, which has established over the past two decades some of the field's most fundamental and lasting contributions. This book presents the results of the eighteenth edition of "Robotics Research" ISRR17, offering a collection of a broad range of topics in robotics. This symposium took place in Puerto Varas, Chile from December 11th to December 14th, 2017. The content of the contributions provides a wide coverage of the current state of robotics research, the advances and challenges in its theoretical foundation and technology basis, and the developments in its traditional and new emerging areas of applications. The diversity, novelty, and span of the work unfolding in these areas reveal the field's increased maturity and expanded scope and define the state of the art of robotics and its future direction.

Download or read book Mobile Robot Motion Control and Path Planning written by Ahmad Taher Azar and published by Springer Nature. This book was released on 2023-06-30 with total page 670 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the recent research advances in linear and nonlinear control techniques. From both a theoretical and practical standpoint, motion planning and related control challenges are key parts of robotics. Indeed, the literature on the planning of geometric paths and the generation of time-based trajectories, while accounting for the compatibility of such paths and trajectories with the kinematic and dynamic constraints of a manipulator or a mobile vehicle, is extensive and rich in historical references. Path planning is vital and critical for many different types of robotics, including autonomous vehicles, multiple robots, and robot arms. In the case of multiple robot route planning, it is critical to produce a safe path that avoids colliding with objects or other robots. When designing a safe path for an aerial or underwater robot, the 3D environment must be considered. As the number of degrees of freedom on a robot arm increases, so does the difficulty of path planning. As a result, safe pathways for high-dimensional systems must be developed in a timely manner. Nonetheless, modern robotic applications, particularly those requiring one or more robots to operate in a dynamic environment (e.g., human–robot collaboration and physical interaction, surveillance, or exploration of unknown spaces with mobile agents, etc.), pose new and exciting challenges to researchers and practitioners. For instance, planning a robot's motion in a dynamic environment necessitates the real-time and online execution of difficult computational operations. The development of efficient solutions for such real-time computations, which could be offered by specially designed computational architectures, optimized algorithms, and other unique contributions, is thus a critical step in the advancement of present and future-oriented robotics.

Download or read book Learning to Guide Task and Motion Planning written by Beomjoon Kim and published by . This book was released on 2020 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: How can we enable robots to efficiently reason both at the discrete task-level and the continuous motion-level to achieve high-level goals such as tidying up a room or constructing a building? This is a challenging problem that requires integrated reasoning about the combinatoric aspects of the problem, such as deciding which object to manipulate, and continuous aspects of the problem, such as finding collision-free manipulation motions, to achieve goals. The classical robotics approach is to design a planner that, given an initial state, goal, and transition model, computes a plan. The advantage of this approach is its immense generalization capability. For any given state and goal, a planner will find a solution if there is one. The inherent drawback, however, is that a planner does not typically make use of planning experience, and computes a plan from scratch every time it encounters a new problem. For complex problems, this renders planners extremely inefficient. Alternatively, we can take a pure learning approach where the system learns, from either reinforcement signals or demonstrations, a policy that maps states to actions. The advantage of this approach is that computing the next action to execute becomes much cheaper than pure planning because it is simply making a prediction using a function approximator. The drawback, however, is that it is brittle. If a policy encounters a state that is very different from the ones seen in the training set, then it is likely to make mistakes and might get into a situation from which it does not know how to proceed. Our approach is to take the middle ground between these two extremes. More concretely, this thesis introduces several algorithms that learn to guide a planner from planning experience. We propose state representations, neural network architectures, and data-efficient algorithms for learning to perform both task and motion level reasoning using neural networks. We then use these neural networks to guide a planner and show that it performs more efficiently than pure planning and pure learning algorithms.

Download or read book Efficient Robot Motion Planning in Cluttered Environments written by Aditya Vamsikrishna Mandalika and published by . This book was released on 2021 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robotics has become a part of the solution in various applications today: autonomous vehicles navigating busy streets, articulated robots tirelessly sorting packages in warehouses, feeding people in care homes and mobile robots assisting in rescue operations. Central to any robot that needs to navigate its environment, is Motion Planning: the task of computing a collision-free motion for a (robotic) system between given start and goal states in an environment cluttered with obstacles. As tasks become more complex, there is a need to develop more sophisticated motion planning algorithms that can compute high quality solutions for the robot quickly. This thesis primarily address the challenge in three phases: First, we approximate the optimal motion planning problem in a continuous space to a search for the shortest path on a discrete graph abstraction. We investigate the computational bottlenecks in search: graph operations and collision evaluations and propose GLS, an algorithmic framework to balance the computational effort between the two operations. In addition to showing that GLS captures an oracular behaviour that minimizes planning time, we propose strategies to balance the computational effort and approximate such an oracle. Second, we focus on the graph abstraction. A desirable graph is sparse allowing for fast search (fewer graph operations and edge evaluations) but locally dense in cluttered regions such that a feasible low-cost path exists. We note that there is structural similarity in the environments that a robot typically operates in. To this end, we propose LEGO, to leverage a robot's experience in similar environments and learn to generate sparse graphs that adequately sample bottleneck regions in the environment while ensuring a high quality solution exists. Third, we relax the assumption of a fixed discrete graph abstraction to compute the optimal solution in the continuous space. We extend the computational efficiency that GLS provides to an incremental asymptotically-optimal sampling-based algorithm. IGLS computes the optimal solution in an anytime manner by iteratively sampling increasingly dense graphs and minimizing the planning time at each iteration. We further investigate improving the convergence rate of such algorithms. Asymptotically-optimal planners typically compute an initial solution and subsequently focus sampling graphs in an Informed Set defined by the current solution cost. We propose GuILD which leverages the search tree constructed in every iteration to further inform sampling for a faster convergence to optimality. Finally, we test our algorithms and evaluate their efficacy on a suite of robotic platforms and planning problems. Our results demonstrate our algorithms to significantly reduce planning times across domains and outperform competing planners.

Download or read book Path Planning of Cooperative Mobile Robots Using Discrete Event Models written by Cristian Mahulea and published by John Wiley & Sons. This book was released on 2020-01-09 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: Offers an integrated presentation for path planning and motion control of cooperative mobile robots using discrete-event system principles Generating feasible paths or routes between a given starting position and a goal or target position—while avoiding obstacles—is a common issue for all mobile robots. This book formulates the problem of path planning of cooperative mobile robots by using the paradigm of discrete-event systems. It presents everything readers need to know about discrete event system models—mainly Finite State Automata (FSA) and Petri Nets (PN)—and methods for centralized path planning and control of teams of identical mobile robots. Path Planning of Cooperative Mobile Robots Using Discrete Event Models begins with a brief definition of the Path Planning and Motion Control problems and their state of the art. It then presents different types of discrete models such as FSA and PNs. The RMTool MATLAB toolbox is described thereafter, for readers who will need it to provide numerical experiments in the last section. The book also discusses cell decomposition approaches and shows how the divided environment can be translated into an FSA by assigning to each cell a discrete state, while the adjacent relation together with the robot's dynamics implies the discrete transitions. Highlighting the benefits of Boolean Logic, Linear Temporal Logic, cell decomposition, Finite State Automata modeling, and Petri Nets, this book also: Synthesizes automatic strategies based on Discrete Event Systems (DES) for path planning and motion control and offers software implementations for the involved algorithms Provides a tutorial for motion planning introductory courses or related simulation-based projects using a MATLAB package called RMTool (Robot Motion Toolbox) Includes simulations for problems solved by methodologies presented in the book Path Planning of Cooperative Mobile Robots Using Discrete Event Models is an ideal book for undergraduate and graduate students and college and university professors in the areas of robotics, artificial intelligence, systems modeling, and autonomous control.

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 A Mission Planning System for Multiple Mobile Robots in Unknown Unstructured and Changing Environments written by Barry L. Brumitt and published by . This book was released on 1998 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "Research in autonomous mobile robots has reached a level of maturity where robotic systems can be expected to efficiently perform complex missions involving multiple agents in unstructured environments. Across a wide space of real-world tasks, particularly those which are expensive or risk-intensive, efficient teams of autonomous cooperative mobile robots could provide a valuable alternative to current solutions. Through the distribution of computation, perception, and action, a cooperative robot team is more capable than the sum of its parts, as this team exhibits increased reliability and the ability to complete physically distributed tasks. For multiple mobile robots to be effective in real-world applications, more than one robot must be able to safely share a potentially unknown workspace. Complicated missions with interdependencies between these robots must be feasible. Finally, robotic systems must accommodate an operational environment which is not necessarily static, certain, or known in advance. Many tasks which are likely candidates for robotic automation (such as hazardous waste site remediation, planetary exploration, materials handling and military reconnaissance), require a robot team to perform an essentially mobile mission which involves robots moving between significant locations. It is important that these missions be completed efficiently, appropriately minimizing the cost of the task. The similarities among these tasks indicate that a single general system could support coordinated mission execution for many scenarios. To this end, GRAMMPS (a General Robotic Autonomous Mobile Mission Planning System) has been developed. GRAMMPS supports the optimization of real-world missions involving multiple robots and multiple concurrent goals. The largest component of GRAMMPS is its central planner, which continuously optimizes the execution of a multi-robot mission as information about the world is acquired. GRAMMPS distributes its computation, gracefully degrades from optimal performance when presented with computationally intractable missions, and performs efficient replanning in an unknown, unstructured, and changing environment. This system has been demonstrated on two autonomous outdoor mobile robots and extensively validated in simulation."

Download or read book Combining Task and Motion Planning for Mobile Manipulators written by Aliakbar Akbari and published by . This book was released on 2020 with total page 159 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis addresses the combination of task and motion planning which deals with different types of robotic manipulation problems. Manipulation problems are referred to as mobile manipulation, collaborative multiple mobile robots tasks, and even higher dimensional tasks (like bi-manual robots or mobile manipulators). Task and motion planning problems needs to obtain a geometrically feasible manipulation plan through symbolic and geometric search space. The combination of task and motion planning levels has emerged as a challenging issue as the failure leads robots to dead-end tasks due to geometric constraints. In addition, task planning is combined with physics-based motion planning and information to cope with manipulation tasks in which interactions between robots and objects are required, or also a low-cost feasible plan in terms of power is looked for. Moreover, combining task and motion planning frameworks is enriched by introducing manipulation knowledge. It facilitates the planning process and aids to provide the way of executing symbolic actions. Combining task and motion planning can be considered under uncertain information and with human-interaction. Uncertainty can be viewed in the initial state of the robot world or the result of symbolic actions. To deal with such issues, contingent-based task and motion planning is proposed using a perception system and human knowledge. Also, robots can ask human for those tasks which are difficult or infeasible for the purpose of collaboration.An implementation framework to combine different types of task and motion planning is presented. All the required modules and tools are also illustrated. As some task planning algorithms are implemented in Prolog or C++ languages and our geometric reasoner is developed in C++, the flow of information between different languages is explained.

Download or read book RoboCup 2003 Robot Soccer World Cup VII written by Daniel Polani and published by Springer Science & Business Media. This book was released on 2004-09-02 with total page 782 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the seventh official archival publication devoted to RoboCup. It documents the achievements presented at the 7th Robot World Cup Soccer and Rescue Competition and Conferences held in Padua, Italy, in July 2003. The 39 revised full papers and 35 revised poster papers presented together with an overview and roadmap for the RoboCup initiative and 3 invited papers were carefully reviewed and selected from 125 symposium paper submissions. This book is mandatory reading for the rapidly growing RoboCup community as well as a valuable source of reference and inspiration for R&D professionals interested in robotics, distributed artificial intelligence, and multi-agent systems.

Download or read book Introduction to Autonomous Mobile Robots second edition written by Roland Siegwart and published by MIT Press. This book was released on 2011-02-18 with total page 473 pages. Available in PDF, EPUB and Kindle. Book excerpt: The second edition of a comprehensive introduction to all aspects of mobile robotics, from algorithms to mechanisms. 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. Curriculum developed by Dr. Robert King, Colorado School of Mines, and Dr. James Conrad, University of North Carolina-Charlotte, to accompany the National Instruments LabVIEW Robotics Starter Kit, are available. Included are 13 (6 by Dr. King and 7 by Dr. Conrad) laboratory exercises for using the LabVIEW Robotics Starter Kit to teach mobile robotics concepts.