DocumentCode
1778057
Title
Evolving look ahead controllers for energy optimal driving and path planning
Author
Gaier, Adam ; Asteroth, Alexander
Author_Institution
Bonn-Rhein-Sieg Univ. of Appl. Sci., St. Augustin, Germany
fYear
2014
fDate
23-25 June 2014
Firstpage
138
Lastpage
145
Abstract
An evolved neural network controller is presented to solve the optimal control problem for energy optimal driving. A controller is produced which computes equivalent control commands to traditional graph searching approaches, while able to adapt to varied constraints and conditions. Furthermore, after training, trivial amounts of computation time and memory are required, making the approach applicable for embedded systems and path planning applications.
Keywords
graph theory; neurocontrollers; optimal control; path planning; road vehicles; embedded systems; energy optimal driving; evolved neural network controller; evolving look ahead controllers; optimal control problem; path planning; Aerospace electronics; Complexity theory; Optimal control; Roads; Search problems; Technological innovation; Vehicles;
fLanguage
English
Publisher
ieee
Conference_Titel
Innovations in Intelligent Systems and Applications (INISTA) Proceedings, 2014 IEEE International Symposium on
Conference_Location
Alberobello
Print_ISBN
978-1-4799-3019-7
Type
conf
DOI
10.1109/INISTA.2014.6873610
Filename
6873610
Link To Document