DocumentCode :
1798296
Title :
Dynamic modeling of an ostraciiform robotic fish based on angle of attack theory
Author :
Wei Wang ; Guangming Xie ; Hong Shi
Author_Institution :
Intell. Control Lab., Peking Univ., Beijing, China
fYear :
2014
fDate :
6-11 July 2014
Firstpage :
3944
Lastpage :
3949
Abstract :
This paper focuses on the dynamic modeling of a self-propelled, multimodal ostraciiform robotic fish, whose three active joints (two pectoral fins and one caudal fin) are actuated by a Central Pattern Generator (CPG) controller. Compared with other dynamic modes for robotic fish, we introduce angle of attack (AoA) theory on the fish modeling, which can be used to further explore the relationship between swimming efficiency and AoA of robotic fish. First, by using the quasi-steady wing theory, AoA of the oscillatory fins are explicitly derived. Then, with the simplification of the robot as a multi-rigid-body mechanism, AoA-based fluid forces acting on the oscillatory fins of the robot are further approximately calculated in a three-dimensional context. Next, by importing the driving signals (generated by CPG control law) into a Lagrangian function, the differential-algebraic equations are employed to establish a hydrodynamic model for steady swimming of the ostraciiform robotic fish for the first time. Finally, comparative results between simulations and experiments for forward and turning gaits of the robot are systematically conducted to show the effectiveness of the built AoA-based dynamic model.
Keywords :
differential algebraic equations; mobile robots; robot dynamics; AoA theory; AoA-based dynamic model; CPG control law; CPG controller; angle of attack theory; central pattern generator; differential algebraic equations; dynamic modeling; fish modeling; hydrodynamic model; multimodal ostraciiform robotic fish; multirigid body mechanism; oscillatory fins; quasisteady wing theory; robot turning gaits; Drag; Dynamics; Mathematical model; Robot kinematics; Robot sensing systems; Turning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks (IJCNN), 2014 International Joint Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-6627-1
Type :
conf
DOI :
10.1109/IJCNN.2014.6889881
Filename :
6889881
Link To Document :
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