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

Two less addressed issues of deep reinforcement learning are (1) lack of generalization capability to new goals, and (2) data inefficiency, i.e., the model requires several (and often costly) episodes of trial and error to converge, which makes it impractical to be applied to real-world scenarios. In this paper, we address these two issues and apply our model to target-driven visual navigation. To address the first issue, we propose an actor-critic model whose policy is a function of the goal as well as the current state, which allows better generalization. To address the second issue, we propose the AI2-THOR framework, which provides an environment with high-quality 3D scenes and a physics engine. Our framework enables agents to take actions and interact with objects. Hence, we can collect a huge number of training samples efficiently. We show that our proposed method (1) converges faster than the state-of-the-art deep reinforcement learning methods, (2) generalizes across targets and scenes, (3) generalizes to a real robot scenario with a small amount of fine-tuning (although the model is trained in simulation), (4) is end-to-end trainable and does not need feature engineering, feature matching between frames or 3D reconstruction of the environment.

Target-driven visual navigation in indoor scenes using deep reinforcement learning

Mobile robot vision-based navigation has been the source of countless research contributions, from the domains of both vision and control. Vision is becoming more and more common in applications such as localization, automatic map construction, autonomous navigation, path following, inspection, monitoring or risky situation detection. This survey presents those pieces of work, from the nineties until nowadays, which constitute a wide progress in visual navigation techniques for land, aerial and autonomous underwater vehicles. The paper deals with two major approaches: map-based navigation and mapless navigation. Map-based navigation has been in turn subdivided in metric map-based navigation and topological map-based navigation. Our outline to mapless navigation includes reactive techniques based on qualitative characteristics extraction, appearance-based localization, optical flow, features tracking, plane ground detection/tracking, etc... The recent concept of visual sonar has also been revised.

Visual Navigation for Mobile Robots: A Survey

An autonomous robot offers a challenging and ideal field for the study of intelligent architectures. Autonomy within a rational be havior could be evaluated by the robot's effectiveness and robust ness in carrying out tasks in different and ill-known environments. It raises major requirements on the control architecture. Further more, a robot as a programmable machine brings up other archi tectural needs, such as the ease and quality of its specification and programming.This article describes an integrated architecture that allows a mobile robot to plan its tasks—taking into account temporal and domain constraints, to perform corresponding actions and to con trol their execution in real-time—while being reactive to possible events. The general architecture is composed of three levels: a de cision level, an execution level, and a functional level. The latter is composed of modules that embed the functions achieving sensor- data processing and effector control. The decision level is goal and event driven, a...

/pdf/an-architecture-for-autonomy-3qsc6mltdi.pdf

An Architecture for Autonomy

Developing software for mobile robot applications is a tedious and error-prone task. Modern mobile robot systems are distributed systems, and their designs exhibit large heterogeneity in terms of hardware, operating systems, communications protocols, and programming languages. Vendor-provided programming environments have not kept pace with recent developments in software technology. Also, standardized modules for certain robot functionalities are beginning to emerge. Furthermore, the seamless integration of mobile robot applications into enterprise information processing systems is mostly an open problem. We suggest the construction and use of object-oriented robot middleware to make the development of mobile robot applications easier and faster, and to foster portability and maintainability of robot software. With Miro, we present such a middleware, which meets the aforementioned requirements and has been ported to three different mobile platforms with little effort. Miro also provides generic abstract services like localization or behavior engines, which can be applied on different robot platforms with virtually no modifications.

Miro - middleware for mobile robot applications

A method combining planning and reactive control for car-like nonholonomic mobile robots is discussed. Firstly, a "bubble" for a car-like mobile robot is defined as the locally reachable space from a given configuration considering the obstacles and using the appropriate metric. Then a flexible feasible trajectory, based on the elastic band concepts, is constructed. This trajectory is smoothed using Bezier curves satisfying a minimum curvature constraint, and a parameterization is proposed which satisfies the robot kinematics constraints.

Dynamic path modification for car-like nonholonomic mobile robots

The paper presents an approach to reactive navigation in cross-country terrains. The approach relies on a particular probabilistic obstacle detection procedure, that describes the area perceived by a pair of stereo cameras as a set of polygonal cells. To generate the motion commands on the basis of this terrain description, we present some improvements and adaptations to the classical potential fields technique. Results on real stereo data illustrate our contribution throughout the paper, and simulated long range traverses are discussed.

/pdf/reactive-navigation-in-outdoor-environments-using-potential-2i088vohyi.pdf

Reactive navigation in outdoor environments using potential fields

This paper presents on operational framework to bridge the gap between planning with uncertainty and real-time sensor-based motion control. The environment being known, a planner produces a plan composed of free space and sensor-based motion commands. The representations of uncertainty and its evolution, environment landmarks, and actions generated at the planning level are discussed. Sensor-based actions and command definitions for a nonholonomic mobile robot based on a task-potential field approach are developed. These various elements are integrated in a system that actually generates the motions of the Hilare2 mobile robot.

Indoor navigation with uncertainty using sensor-based motions

In previous papers (11 we have argued that the LAAS architecture’ is one of the most suited for mobile robot control. This statement may seem over-optimistic, not to say pretentious and unverifiable. After all, can we compare architectures? can we set up benchmarks? or can we measure how good an architecture is compared to another? An architecture defines organization principles, integration methods and supporting tools. Comparing those tools, methods and principles may sometime end up in sterile controversies. However, we think there are means to measure the overall quality (or interest) of an architecture. Development time is for example one relevant criterion [5]. Basically, using a specific architecture, how long does it take to integrate a complete demonstration, including non trivial decisional capabilities, from the low level functional modules up to the supervisory level? This may seem a rather weak measure of an architecture quality, however, it encompasses properties such as: genericity and adaptability, ease of design and programming, extensibility and robustness. In this paper we describe our recent experience in integrating a complete demonstration from scratch in 40 days using the LAAS architecture.

Around the Lab in 40 days ...

We present a general concept for the control of a large fleet of autonomous mobile robots which has been developed, implemented and validated through various experiments.

/pdf/a-general-framework-for-multi-robot-cooperation-and-its-2d8kjrw072.pdf

Maher Khatib

Papers

Dynamic path modification for car-like nonholonomic mobile robots

Reactive navigation in outdoor environments using potential fields

Indoor navigation with uncertainty using sensor-based motions

Around the Lab in 40 days ...

A General Framework For Multi-Robot Cooperation and Its Implementation on a Set of Three Hilare Robots