Gliding Motion Optimization for a Biomimetic Gliding Robotic Fish
Huijie Dong, Zhengxing Wu, Yan Meng, Min Tan, Junzhi Yu
Abstract
In this paper, we present a gliding efficiency optimization strategy based on deep reinforcement learning (DRL) for a gliding robotic fish. For the gliding motion in shallow waters, the non-steady motion strongly impacts the gliding range and also reduces efficiency. This paper presents a concept of transient gliding motion and illustrates its importance for small-scale gliding robotic fish. For better gliding performance of active fins, several pectoral fins with different sizes are designed and their hydrodynamics and optimizing capabilities are analyzed by computational fluid dynamics (CFD) simulation. Then, a double deep Q network (DQN) based optimization strategy is proposed to improve gliding efficiency by active pectoral fins, in which an adversarial model and a two-stage reward function are presented for the adequate calculation of gliding range. Simulations are conducted to validate the convergence and effectiveness of the proposed strategy. The aquatic experiments are carried out to further verify the proposed strategy. The results reveal that the optimization strategy can save about 4.88% of energy and 19.45% of travel time. This study provides clues to the design of active control surfaces and improvement of gliding efficiency for underwater vehicles. Remarkably, the proposed strategy can significantly improve the duration and endow the robot with the potential to perform complex tasks.