Demonstration of a Novel Phase Lag Controlled Roll Rotation Mechanism using a Two-DOF Soft Swimming Robot
Bangyuan Liu, Frank L. Hammond
- Year
- 2020
- Citations
- 3
Abstract
Underwater roll rotation is a basic but essential maneuver that allows many biological swimmers to achieve high maneuverability and complex locomotion patterns. In particular, sea mammals (e.g., sea otter) with flexible vertebra structures have a unique mechanism to efficiently achieve roll rotation, not propelled mainly by inter-digital webbing or fin, but by bending and twisting their body.In this work, we attempt to implement and effectively control the roll rotation by mimicking this kind of efficient biomorphic roll mechanism on our two degrees of freedom (DOF) soft modular swimming robot. The robot also allows the achievement of other common maneuvers, such as pitch/yaw rotation and linear swimming patterns. The proposed 2DOF soft swimming robot platform includes an underactuated, cable-driven design that mimics the flexible cascaded skeletal structure of soft spine tissue and hard spine bone seen in many fish species. The cable-driven actuation mechanism is oriented laterally for forwarding motion and steering in a 3D plane. The robot can perform a steady and controllable roll rotation with a maximum angular speed of 41.6 deg/s. A hypothesis explaining this novel roll rotation mechanism is set forth, and the phenomenon is systematically studied at different frequencies and phase lag gait conditions. Preliminary results show a linear relationship between roll angular velocity and frequency within a specific range. Additionally, the roll rotation can be controlled independently in some special conditions. These abilities form the foundation for future research on 3D underwater locomotion with adaptive, controllable maneuvering capabilities.
Keywords
Related papers
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Artificial intelligence: a modern approach
1995
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
A new optimizer using particle swarm theory
R.C. Eberhart, James Kennedy
2002