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
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