Passivity-based model predictive control for mobile vehicle motion planning
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 wor...
Enregistré dans:
| Auteurs principaux: | , |
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| Format: | Livre numérique |
| Sprog: | Anglais |
| Udgivet: |
London :
Springer London
2013.
Cham : Springer Nature |
| Serier: | SpringerBriefs in Control, Automation and Robotics
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| Online adgang: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Kommentar: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Passivity-Based Model Predictive Control for Mobile Vehicle Motion Planning, Texte imprimé, 9781447150480 • Passivity-Based Model Predictive Control for Mobile Vehicle Motion Planning, Texte imprimé, 9781447150503 |
| Summary: | 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. |
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| Emne beskrivelse: | Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| ISBN: | 9781447150497 |
| ISSN: | 2192-6794 |
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