Fishes are masters of locomotion in fluids owing to their highly integrated biological sensing, computing and motor systems. They are adept at collecting and exploiting rich information from the surrounding fluids for underwater sensing and locomotion control. Inspired and informed by fish swimming, this research aims to develop a novel bio-inspired cyber-physical system (CPS) that integrates the "physical" robot fish and fluid environment with the "cyber" robot control & machine learning algorithms. Specifically, this CPS system includes i) a pressure sensory skin with distributed sensing capability to collect flow information, ii) control and learning algorithms that compute robot motor signals, output by central pattern generators (CPGs) which receive pressure sensory feedback, iii) a robot fish platform to implement and validate the CPS framework for underwater sensing and control tasks, and iv) experimental and computational methods to investigate and model the underlying fluid physics. This CPS system will have immediate impacts on the core CPS research areas such as design, control, data analytics, autonomy, and real-time systems. It will also significantly impact a wide range of engineering applications which demand distributed sensing, control and adaptive actuation. Examples include human-machine interactions, medical robots, unmanned aerial/underwater vehicles, drug dosing, medical therapeutics, and space deployable structures among others. Leveraging the multidisciplinary nature of this research, this award will support a variety of educational and outreach activities. In particular, a list of activities in broadening participation in engineering will be carried out.
This research project integrates multiple CPS technologies to develop bio-inspired technologies for swarm control of fish. These include innovation in sensing modality via a stretchable, pressure sensitive skin, physics inspired learning and swarm control. The project will first develop a distributed pressure sensitive synthetic skin, which will be installed on robotic fishes to map the pressure distribution on their body and caudal-fin surfaces. The distributed pressure information will then be used in a feedback control policy that modulates CPGs to produce caudal-fin motion patterns of the robotic fishes. The control policy and the caudal-fin motion patterns will be optimized via reinforcement learning first in a surrogate fluid environment and then in the true fluid environment. The surrogate fluid environment will be developed using data-driven non-parametric models informed by physics-based hydrodynamic models of fish swimming, trained using combined experimental and Computational Fluid Dynamics (CFD) simulation data. The above control-learning methods will also be used to achieve efficient schooling in a group of robotic fishes, individually controlled by a CPG, which interacts with each other through surrounding fluids and pressure sensory feedback. The optimized swimming/schooling performance of robotic fishes and the underlying physics will be studied using CFD simulation. Together, this research will advance CPS knowledge on: 1) the design and creation of electronic and sensor materials and devices for robot skin applications; 2) the development of data-efficient, physics-informed learning methods for robotic systems that operate in complex environments, especially leveraging the recent progress on deep learning to exploit the spatial and temporal richness of the pressure data for underwater sensing and robot control; and 3) the flow physics and modeling of fish swimming.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
