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A computational framework for Kármán gaiting in robotic fish: spatio-temporal perception and CPG-based reinforcement
Xinqi Wang1, Ming Wang1, Xinyang Liu1
1Shandong Key Laboratory of Smart Buildings and Energy Efficiency, School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, Shandong, People's Republic of China.
Biomimetic robots can now autonomously generate the Kármán gait for efficient navigation in unsteady flows. This is achieved using a novel sensory system that mimics fish lateral lines, reducing energy expenditure in turbulent environments.
Area of Science:
- Robotics
- Fluid Dynamics
- Biomimetics
Background:
- Navigating unsteady wake flows, like Kármán vortex streets, is challenging for autonomous underwater vehicles.
- Biological fish use lateral line systems and the Kármán gait for efficient movement in such flows.
Purpose of the Study:
- To develop a computational framework for modeling and simulating a spatio-temporal sensory system.
- To autonomously generate the Kármán gait for biomimetic robots in unsteady flows.
Main Methods:
- Modeled a multi-point lateral line array with frame-stacking for flow topology reconstruction.
- Integrated a spatio-temporal perceptual twin delayed deep deterministic policy gradient (STP-TD3) algorithm.
- Used a Hopf-oscillator-based central pattern generator and high-fidelity CFD simulations.
Main Results:
- The agent autonomously generated the Kármán gait, significantly reducing mechanical effort in turbulent vortex streets.
- Demonstrated active exploitation of local wake dynamics for energy efficiency.
- Quantified energy savings compared to swimming in steady water.
Conclusions:
- The study validates the necessity of distributed spatio-temporal perception for biomimetic robots.
- Provides a blueprint for autonomous navigation in complex aquatic environments.
- Highlights the potential of bio-inspired sensory systems and control strategies.
