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Picture 1 of 1
Passivity-Base
US $66.79
ApproximatelyEUR 60.01
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New
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eBay item number:364686924548
Item specifics
- Condition
- 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
- 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
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
23
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
Item description from the seller
Business seller information
Premier Books LLC
David Taylor
26C Trolley Sq
19806-3356 Wilmington, DE
United States
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