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Bridging the Robot Perception Gap with Mid-Level Vision Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Unifying Robot Trajectory Tracking with Control Contraction Metrics

Authors : Ian R. Manchester, Justin Z. Tang, Jean-Jacques E. Slotine

Published in: Robotics Research

Publisher: Springer International Publishing

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Abstract

In this paper we present new results for nonlinear tracking control design based on the search for a “control contraction metric” (CCM). We show that for fully-actuated robots, CCMs can be found analytically that correspond to classical methods of robot control including sliding and energy-based designs. We also show that, for underactuated robots, the CCM approach extends more recent optimization-based methods such as LQR-Trees to tracking of arbitrary feasible trajectories. Therefore the CCM methodology represents a bridge between these methods. We illustrate our results with calculations and simulations on the single and double inverted pendulums and the underactuated cart-pole system.

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previous chapter Small and Adrift with Self-Control: Using the Environment to Improve Autonomy
next chapter Inference-Enabled Information-Theoretic Exploration of Continuous Action Spaces
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Metadata
Title
Unifying Robot Trajectory Tracking with Control Contraction Metrics
Authors
Ian R. Manchester
Justin Z. Tang
Jean-Jacques E. Slotine
Copyright Year
2018
Book
Robotics Research

Print ISBN: 978-3-319-60915-7

Electronic ISBN: 978-3-319-60916-4

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-60916-4_23