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Dean Pomerleau's trainable road tracker, ALVINN, is arguably the world's most famous neural net application. It currently holds the world's record for distance traveled by an autonomous robot without interruption: 21.2 miles along a highway, in traffic, at speedsofup to 55 miles per hour. Pomerleau's work has received worldwide attention, including articles in Business Week (March 2, 1992), Discover (July, 1992), and German and Japanese science magazines. It has been featured in two PBS series, "The Machine That Changed the World" and "By the Year 2000," and appeared in news segments on CNN, the Canadian news and entertainment program "Live It Up", and the Danish science program "Chaos". What makes ALVINN especially appealing is that it does not merely drive - it learns to drive, by watching a human driver for roughly five minutes. The training inputstothe neural networkare a video imageoftheroad ahead and thecurrentposition of the steering wheel. ALVINN has learned to drive on single lane, multi-lane, and unpaved roads. It rapidly adapts to other sensors: it learned to drive at night using laser reflectance imaging, and by using a laser rangefinder it learned to swerve to avoid obstacles and maintain a fixed distance from a row of parked cars. It has even learned to drive backwards.
Deep Learning for Robot Perception and Cognition introduces a broad range of topics and methods in deep learning for robot perception and cognition together with end-to-end methodologies. The book provides the conceptual and mathematical background needed for approaching a large number of robot perception and cognition tasks from an end-to-end learning point-of-view. The book is suitable for students, university and industry researchers and practitioners in Robotic Vision, Intelligent Control, Mechatronics, Deep Learning, Robotic Perception and Cognition tasks. Presents deep learning principles and methodologies Explains the principles of applying end-to-end learning in robotics applications Presents how to design and train deep learning models Shows how to apply deep learning in robot vision tasks such as object recognition, image classification, video analysis, and more Uses robotic simulation environments for training deep learning models Applies deep learning methods for different tasks ranging from planning and navigation to biosignal analysis
"This thesis deals with computationally efficient fuzzy and neural algorithms for mobile robot navigation in an unknown environment. Compared with burdensome and time consuming calculation of normal neural netwok, the RAM-based network and fuzzy logic methods require little computational load and are well suited to a microcontroller based system."--Abstract, leaf iii.
Mobile robots are playing an increasingly important role in our world. Remotely operated vehicles are in everyday use for hazardous tasks such as charting and cleaning up hazardous waste spills, construction work of tunnels and high rise buildings, and underwater inspection of oil drilling platforms in the ocean. A whole host of further applications, however, beckons robots capable of autonomous operation without or with very little intervention of human operators. Such robots of the future will explore distant planets, map the ocean floor, study the flow of pollutants and carbon dioxide through our atmosphere and oceans, work in underground mines, and perform other jobs we cannot even imagine; perhaps even drive our cars and walk our dogs. The biggest technical obstacles to building mobile robots are vision and navigation-enabling a robot to see the world around it, to plan and follow a safe path through its environment, and to execute its tasks. At the Carnegie Mellon Robotics Institute, we are studying those problems both in isolation and by building complete systems. Since 1980, we have developed a series of small indoor mobile robots, some experimental, and others for practical applicationr Our outdoor autonomous mobile robot research started in 1984, navigating through the campus sidewalk network using a small outdoor vehicle called the Terregator. In 1985, with the advent of DARPA's Autonomous Land Vehicle Project, we constructed a computer controlled van with onboard sensors and researchers. In the fall of 1987, we began the development of a six-legged Planetary Rover.
Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described.
This collection of papers explore the variety of techniques used to equip robots with the capacity to improve their behaviour over time, based upon their incoming experiences. The contributions are interdisciplinary in nature and combine research from the field of robotics, computer science and biology.
The mathematics employed by genetic algorithms (GAs)are among the most exciting discoveries of the last few decades. But what exactly is a genetic algorithm? A genetic algorithm is a problem-solving method that uses genetics as its model of problem solving. It applies the rules of reproduction, gene crossover, and mutation to pseudo-organism
This book surveys the history of automatic vehicle guidance based on the processing of visual information, starting from the very first projects worldwide up to the latest developments. It also presents the ARGO prototype vehicle, developed at the University of Parma (Italy), and describes its equipment, setup, and performance. ARGO has been equipped with cameras and processing systems to drive autonomously in real traffic conditions. The complete system has been tested on public roads, during a tour in which ARGO drove itself along the Italian highway network for more than 2000 km. A detailed analysis of this trip is also included.
The proceeding is a collection of research papers presented, at the 9th International Conference on Robotics, Vision, Signal Processing & Power Applications (ROVISP 2016), by researchers, scientists, engineers, academicians as well as industrial professionals from all around the globe to present their research results and development activities for oral or poster presentations. The topics of interest are as follows but are not limited to: • Robotics, Control, Mechatronics and Automation • Vision, Image, and Signal Processing • Artificial Intelligence and Computer Applications • Electronic Design and Applications • Telecommunication Systems and Applications • Power System and Industrial Applications • Engineering Education
This is the 70th encyclopaedia of library and information science. It covers topics such as: intelligent systems for problem analysis in organizations; interactive system design; international models of school library development; lexicalization in natural language generation; and more.