Fish in nature shows excellent swimming ability, however, biomimetic robotic fish developed and inspired by
aquatic organisms is still different from real fish in motion performance. In order to improve the swimming efficiency and the control precision of the bionic robotic fish, the invention provides a swimming control method based on data driving. The method comprises the following steps: firstly, synchronously acquiring swimming videos and environmental parameters of real fishes through a multi-angle high-definition camera
system and an
underwater sensor, and constructing a time-space aligned multi-
modal data set; then, based on an improved DeepLabCut
algorithm and a graph convolutional network, key points of a fish body are extracted, and a
joint angle sequence is reconstructed; a driving mechanism of the bionic robotic fish is established by combining a three-joint bionic fish motion model and a propulsion curve equation. The linear propulsion effects under different fish
joint angle input are compared and analyzed through the
simulation platform, the propulsion efficiency and stability are evaluated, and the key influence of joint parameters on the overall motion performance is revealed. Experimental results show that the method can effectively reproduce movement styles of various fishes, and dynamic optimization of a control strategy is realized through self-
supervised learning. The control method gets rid of a traditional modeling mode based on a fish body
wave function, provides a new normal form for driving the
control system by using real visual data, and has good real-time performance, robustness and expansibility.