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Passivity-Based Model Predictive Control for Mobile Vehicle Motion Planning by A

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Item specifics

Condition: Brand New: A new, unread, unused book in perfect condition with no missing or damaged pages. See all condition definitionsopens in a new window or tab

ISBN-13: 9781447150480

Book Title: Passivity-Based Model Predictive Control for Mobile Vehicle Motio

ISBN: 9781447150480

Subject Area: Technology & Engineering

Publication Name: Passivity-Based Model Predictive Control for Mobile Vehicle Motion Planning

Publisher: Springer London, The Limited

Item Length: 9.3 in

Subject: Automation, Electrical, Automotive, Robotics, Aeronautics & Astronautics

Publication Year: 2013

Series: Springerbriefs in Electrical and Computer Engineering Ser.

Type: Textbook

Format: Trade Paperback

Language: English

Author: Adnan Tahirovic, Gianantonio Magnani

Item Weight: 41.7 Oz

Item Width: 6.1 in

Number of Pages: Xi, 56 Pages

About this product

Product Identifiers

Publisher

Springer London, The Limited

ISBN-10

1447150481

ISBN-13

9781447150480

eBay Product ID (ePID)

159965959

Product Key Features

Number of Pages

Xi, 56 Pages

Language

English

Publication Name

Passivity-Based Model Predictive Control for Mobile Vehicle Motion Planning

Subject

Automation, Electrical, Automotive, Robotics, Aeronautics & Astronautics

Publication Year

2013

Type

Textbook

Subject Area

Technology & Engineering

Author

Adnan Tahirovic, Gianantonio Magnani

Series

Springerbriefs in Electrical and Computer Engineering Ser.

Format

Trade Paperback

Dimensions

Item Weight

41.7 Oz

Item Length

9.3 in

Item Width

6.1 in

Additional Product Features

Intended Audience

Scholarly & Professional

Dewey Edition

Number of Volumes

1 vol.

Illustrated

Yes

Dewey Decimal

629.046

Synopsis

Passivity-based Model Predictive Control for Mobile Vehicle Navigation represents a complete theoretical approach to the adoption of passivity-based model predictive control (MPC) for autonomous vehicle navigation in both indoor and outdoor environments. The brief also introduces analysis of the worst-case scenario that might occur during the task execution. Some of the questions answered in the text include: * how to use an MPC optimization framework for the mobile vehicle navigation approach; * how to guarantee safe task completion even in complex environments including obstacle avoidance and sideslip and rollover avoidance; and * what to expect in the worst-case scenario in which the roughness of the terrain leads the algorithm to generate the longest possible path to the goal. The passivity-based MPC approach provides a framework in which a wide range of complex vehicles can be accommodated to obtain a safer and more realizable tool during the path-planning stage. During task execution, the optimization step is continuously repeated to take into account new local sensor measurements. These ongoing changes make the path generated rather robust in comparison with techniques that fix the entire path prior to task execution. In addition to researchers working in MPC, engineers interested in vehicle path planning for a number of purposes: rescued mission in hazardous environments; humanitarian demining; agriculture; and even planetary exploration, will find this SpringerBrief to be instructive and helpful., Passivity-based Model Predictive Control for Mobile Vehicle Navigation represents a complete theoretical approach to the adoption of passivity-based model predictive control (MPC) for autonomous vehicle navigation in both indoor and outdoor environments. The brief also introduces analysis of the worst-case scenario that might occur during the task execution. Some of the questions answered in the text include: - how to use an MPC optimization framework for the mobile vehicle navigation approach; - how to guarantee safe task completion even in complex environments including obstacle avoidance and sideslip and rollover avoidance; and - what to expect in the worst-case scenario in which the roughness of the terrain leads the algorithm to generate the longest possible path to the goal. The passivity-based MPC approach provides a framework in which a wide range of complex vehicles can be accommodated to obtain a safer and more realizable tool during the path-planning stage. During task execution, the optimization step is continuously repeated to take into account new local sensor measurements. These ongoing changes make the path generated rather robust in comparison with techniques that fix the entire path prior to task execution. In addition to researchers working in MPC, engineers interested in vehicle path planning for a number of purposes: rescued mission in hazardous environments; humanitarian demining; agriculture; and even planetary exploration, will find this SpringerBrief to be instructive and helpful.

LC Classification Number

TJ212-225