Title: Robot Ecology: Constraint-Based Design for Long-Duration Autonomy (eng)
Publisher: Princeton University Press
Publication Date: 2021
Language: English
ISBN 10: 069121168X
ISBN 13: 9780691211688
Binding: Hardcover
Condition: new
Weight: 1.05 kilograms

About this title

Synopsis

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 SlothBot, 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.

About the Author

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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