Magnus Egerstedt 
Robot Ecology [EPUB ebook] 
Constraint-Based Design for Long-Duration Autonomy

الدعم

A revolutionary new framework that draws on insights from ecology for the design and analysis of long-duration robots
Robots are increasingly leaving the confines of laboratories, warehouses, and manufacturing facilities, venturing into agriculture and other settings where they must operate in uncertain conditions over long timescales. This multidisciplinary book draws on the principles of ecology to show how robots can take full advantage of the environments they inhabit, including as sources of energy.
Magnus Egerstedt introduces a revolutionary new design paradigm—robot ecology—that makes it possible to achieve long-duration autonomy while avoiding catastrophic failures. Central to ecology is the idea that the richness of an organism’s behavior is a function of the environmental constraints imposed by its habitat. Moving beyond traditional strategies that focus on optimal policies for making robots achieve targeted tasks, Egerstedt explores how to use survivability constraints to produce both effective and provably safe robot behaviors. He blends discussions of ecological principles with the development of control barrier functions as a formal approach to constraint-based control design, and provides an in-depth look at the design of the Sloth Bot, a slow and energy-efficient robot used for environmental monitoring and conservation.
Visionary in scope, Robot Ecology presents a comprehensive and unified methodology for designing robots that can function over long durations in diverse natural environments.

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عن المؤلف

Magnus Egerstedt is the Stacey Nicholas Dean of Engineering in the Samueli School of Engineering at the University of California, Irvine. He is the coauthor of
Graph Theoretic Methods in Multiagent Networks and
Control Theoretic Splines: Optimal Control, Statistics, and Path Planning (both Princeton).

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لغة الإنجليزية ● شكل EPUB ● صفحات 360 ● ISBN 9780691230078 ● حجم الملف 48.2 MB ● الناشر Princeton University Press ● مدينة Princeton ● بلد US ● نشرت 2021 ● للتحميل 24 الشهور ● دقة EUR ● هوية شخصية 7850737 ● حماية النسخ Adobe DRM
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