The author has maintained two open-source MATLAB Toolboxes for more than 10 years: one for robotics and one for vision. The key strength of the Toolboxes provide a set of tools that allow the user to work with real problems, not trivial examples. For the student the book makes the algorithms accessible, the Toolbox code can be read to gain understanding, and the examples illustrate how it can be used —instant gratification in just a couple of lines of MATLAB code. The code can also be the starting point for new work, for researchers or students, by writing programs based on Toolbox functions, or modifying the Toolbox code itself. The purpose of this book is to expand on the tutorial material provided with the toolboxes, add many more examples, and to weave this into a narrative that covers robotics and computer vision separately and together. The author shows how complex problems can be decomposed and solved using just a few simple lines of code, and hopefully to inspire up and coming researchers. The topics covered are guided by the real problems observed over many years as a practitioner of both robotics and computer vision. It is written in a light but informative style, it is easy to read and absorb, and includes a lot of Matlab examples and figures. The book is a real walk through the fundamentals of robot kinematics, dynamics and joint level control, then camera models, image processing, feature extraction and epipolar geometry, and bring it all together in a visual servo system. Additional material is provided at http://www.petercorke.com/RVC

The field of mechatronics (which is the synergistic combination of precision mechanical engineering, electronic control and systems thinking in the design of products and manufacturing processes) is gaining much attention in industries and academics. It was detected that the topics of computer vision, control and robotics are imperative for the successful of mechatronics systems. This book includes several chapters which report successful study cases about computer vision, control and robotics. The readers will have the latest information related to mechatronics, that contains the details of implementation, and the description of the test scenarios.

The goal ofthe symposium, "Computer Vision and Sensor-Based Robots," held at the General Motors Research Laboratories on September 2S and 26, 1978, was to stimulate a closer interaction between people working in diverse areas and to discuss fundamental issues related to vision and robotics. This book contains the papers and general discussions of that symposium, the 22nd in an annual series covering different technical disciplines that are timely and of interest to General Motors as well as the technical community at large. The subject of this symposium remains timely because the cost of computer vision hardware continues to drop and there is increasing use of robots in manufacturing applications. Current industrial applications of computer vision range from simple systems that measure or compare to sophisticated systems for part location determination and inspection. Almost all industrial robots today work with known parts in known posi tions, and we are just now beginning to see the emergence of programmable automa tion in which the robot can react to its environment when stimulated by visual and force-touch sensor inputs. As discussed in the symposium, future advances will depend largely on research now underway in several key areas. Development of vision systems that can meet industrial speed and resolution requirements with a sense of depth and color is a necessary step.

Robotic systems consist of object or scene recognition, vision-based motion control, vision-based mapping, and dense range sensing, and are used for identification and navigation. As these computer vision and robotic connections continue to develop, the benefits of vision technology including savings, improved quality, reliability, safety, and productivity are revealed. Robotic Vision: Technologies for Machine Learning and Vision Applications is a comprehensive collection which highlights a solid framework for understanding existing work and planning future research. This book includes current research on the fields of robotics, machine vision, image processing and pattern recognition that is important to applying machine vision methods in the real world.

In this book, we look at how cluster technology can be leveraged to build better robots. Algorithms and approaches in key areas of robotics and computer vision, such as map building, path planning, target tracking, action selection and learning, are reviewed and cluster implementations for these are presented. The objective of the book is to give professionals working in the beowulf cluster or robotics and computer vision fields a concrete view of the strong synergy between the areas as well as to spur further fruitful exploitation of this connection. The book is written at a level appropriate for an advanced undergraduate or graduate student. The key concepts in robotics, computer vision and cluster computing are introduced before being used to make the text useful to a wide audience in these fields.

Describes the significance towards developing intelligent robots and industrial automation with applications. The book presents state-of-the-art technologies on robotics research; and includes an array of recent investigations on navigation, motion planning of mobile robots, control of underactuated systems such as flexible robots.

Robot vision refers to the capability of a robot to visually perceive the environment and use this information for execution of various tasks. Visual feedback has been used extensively for robot navigation and obstacle avoidance. In the recent years, there are also examples that include interaction with people and manipulation of objects. In this paper, we review some of the work that goes beyond of using artificial landmarks and fiducial markers for the purpose of implementing visionbased control in robots. We discuss different application areas, both from the systems perspective and individual problems such as object tracking and recognition.