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Paperback. Condition: new. Paperback. Current attempts to prolong a robot's battery life focus on outdated techniques that have high overhead and are not built in to the underlying robotic architecture. In this thesis, battery life is extended through development of a behavior-based power management system, including a Markov decision process (MDP) power planner. This system examines sensors needed by the currently active behavior set and powers down those not required. Predictive power planning models the domain as an MDP problem in the Deliberator. The planner creates a power policy that accounts for current and future power requirements in stochastic domains. This provides a power plan that uses lower-power consuming devices at the start of a goal sequence in order to save power for the areas where higher-power consuming sensors are needed. Power savings are observed in two case studies: Low and high sensor intensity environments. Testing reveals that in a real life scenario involving multiple goals and multiple sensors, the robot's battery charge can be extended up to 96% longer when using this system over robots that rely on traditional power management.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Published by BiblioScholar 2012-10, 2012
ISBN 10: 124959362X ISBN 13: 9781249593621
Language: English
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Hardcover. Condition: new. Hardcover. Current attempts to prolong a robot's battery life focus on outdated techniques that have high overhead and are not built in to the underlying robotic architecture. In this thesis, battery life is extended through development of a behavior-based power management system, including a Markov decision process (MDP) power planner. This system examines sensors needed by the currently active behavior set and powers down those not required. Predictive power planning models the domain as an MDP problem in the Deliberator. The planner creates a power policy that accounts for current and future power requirements in stochastic domains. This provides a power plan that uses lower-power consuming devices at the start of a goal sequence in order to save power for the areas where higher-power consuming sensors are needed. Power savings are observed in two case studies: Low and high sensor intensity environments. Testing reveals that in a real life scenario involving multiple goals and multiple sensors, the robot's battery charge can be extended up to 96% longer when using this system over robots that rely on traditional power management.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Published by Creative Media Partners, LLC, 2025
ISBN 10: 1025090799 ISBN 13: 9781025090795
Language: English
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ISBN 10: 1025090799 ISBN 13: 9781025090795
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ISBN 10: 1025088638 ISBN 13: 9781025088631
Language: English
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ISBN 10: 1025090799 ISBN 13: 9781025090795
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Condition: New. KlappentextrnrnCurrent attempts to prolong a robot s battery life focus on outdated techniques that have high overhead and are not built in to the underlying robotic architecture. In this thesis, battery life is extended through development of a.
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Published by Creative Media Partners, LLC Okt 2012, 2012
ISBN 10: 124959362X ISBN 13: 9781249593621
Language: English
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Taschenbuch. Condition: Neu. Neuware - Current attempts to prolong a robot's battery life focus on outdated techniques that have high overhead and are not built in to the underlying robotic architecture. In this thesis, battery life is extended through development of a behavior-based power management system, including a Markov decision process (MDP) power planner. This system examines sensors needed by the currently active behavior set and powers down those not required. Predictive power planning models the domain as an MDP problem in the Deliberator. The planner creates a power policy that accounts for current and future power requirements in stochastic domains. This provides a power plan that uses lower-power consuming devices at the start of a goal sequence in order to save power for the areas where higher-power consuming sensors are needed. Power savings are observed in two case studies: Low and high sensor intensity environments. Testing reveals that in a real life scenario involving multiple goals and multiple sensors, the robot's battery charge can be extended up to 96% longer when using this system over robots that rely on traditional power management.
Published by Creative Media Partners, LLC, 2025
ISBN 10: 1025088638 ISBN 13: 9781025088631
Language: English
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ISBN 10: 1025088638 ISBN 13: 9781025088631
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