Bio-inspired Soft Underwater Robot that Swims via Jet Propulsion

Biologically inspired underwater robots simulate the swimming motions of marine organisms. Jet propulsion, a locomotion mode in cephalopods and tunicates such as squids, cuttlefish, and salps, serves as a key inspiration for our designs. We aim to develop soft bio-inspired robots to study locomotion mechanisms in marine creatures, with the focus of achieving greater propulsion efficiencies through design optimization and active control strategies.

Fast swimming of a squid (https://www.youtube.com/watch?v=9OIjaHIrM0U)

Salps colony swimming (https://www.youtube.com/shorts/BNISf9e2JM8)

Design and Characterization of an Origami Swimmer

This project designs a soft underwater robot inspired by cephalopods. Traditional soft robots use flexible materials for shape change but require complex fabrication. To simplify this, we used an origami-inspired design, which allows the robot to fold from flat sheets into 3D shapes, making it easier to store, transport, and assemble in just a few hours. The robot mimics squid locomotion using the origami magic ball pattern, which transforms between an ellipsoid and a sphere. This enables it to expand and contract, pulling in and expelling water to create a jet for propulsion. A tendon mechanism in its spine controls its length. Ongoing effects focus on evaluating different configurations’ effect (e.g. adding a front nozzle) on the performance of the robot and understanding the underlying dynamics of it.

Mechanism demo

Robot swimming

Leveraging Fluid-Structure Interactions for Efficient Control in Geophysical Flows1

Micro-vehicles are cost-effective platforms for robotics and automation, excelling in maneuverability and adaptability in diverse environments. However, their lightweight and limited computational capacity pose control challenges. This research aims to understand fluid-structure interactions to enhance design and control, resulting in more efficient micro-vehicles with extended lifespans. The interdisciplinary effort focuses on fluid dynamics, control theory, and reconfiguration planning. The project aims to leverage environmental forces for power efficiency, investigating morphological adaptations and passive transport properties. It seeks to synthesize motion control strategies considering inertial effects and fluid-structure interactions while exploring efficiency trade-offs. Ultimately, it aims to enhance micro-autonomous vehicles’ capabilities for long-term operations and future large-scale deploy.

Salp-Inspired Reconfigurable Robot Platform for Long-Term Distributed Sensing

The design of bio-inspired autonomous systems aims to derive the concepts of sensorimotor control, biomechanics, and fluid dynamics of underwater propulsion from aquatic species. The goal of this program is to expand the operational envelope of Navy underwater and amphibious vehicles and enable enhanced underwater manipulation. We are interested in designing a salp-inspired robot to simulate the locomotion of salps and develop a system for distributed sensing. Salps are barrel-shaped marine invertebrates that swim via jet propulsion. They move forward by rapidly changing the volumes of their body cavity, drawing water into their muscular mantle cavity through the front aperture, and then expelling it under high pressure through the rear funnel. We aim to leverage the unique biomechanics of salps to inform the development of energy-efficient, maneuverable underwater robots capable of environmental sensing in complex marine environments.

Investigating Efficient Underwater Propulsion with the Multi-Robot SALP System

Salps can swim either as solitary jet-propelled individuals or while physically connected in a multi-jet colony, commonly known as a “salp chain”. Inspired by salps, we develop the SALP (Salp-inspired Approach to Low-energy Propulsion) robot, a soft underwater robot that swims via jet propulsion similarly to a biological salp. The SALPs can be physically connected into SALP chains and coordinate their jets to achieve various propulsion modes. We are interested in investigating how physical arrangement and jet coordination between two robots affect the swimming performance and energy efficiency of a two-SALP robotic system. We aim to gain insights into the potential hydrodynamic benefits of multi-jet propulsion by exploring how different coordination strategies influence the surrounding flow environment.

Related Publications

Effect of Jet Coordination on Underwater Propulsion with the Multi-Robot SALP System

Yang, Zhiyuan; Zhang, Yipeng; Herbert, Matthew; Hsieh, M. Ani; Sung, Cynthia

Effect of Jet Coordination on Underwater Propulsion with the Multi-Robot SALP System (Conference)

8th IEEE-RAS International Conference on Soft Robotics (RoboSoft 2025), Forthcoming.

(Abstract | BibTeX | Links: )

Drag coefficient characterization of the origami magic ball

Chen, Guanyu; Chen, Dongsheng; Weakly, Jessica; Sung, Cynthia

Drag coefficient characterization of the origami magic ball (Proceedings Article)

In: ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC/CIE), pp. DETC2023-117182, 2023.

(Abstract | BibTeX | Links: )

Origami-inspired robot that swims via jet propulsion

Yang, Zhiyuan; Chen, Dongsheng; Levine, David J.; Sung, Cynthia

Origami-inspired robot that swims via jet propulsion (Journal Article)

In: IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 7145-7152, 2021.

(Abstract | BibTeX | Links: )

Current Personnel

  • Dongsheng Chen (MEAM PhD)
  • Zhiyuan (Annie) Yang (MEAM PhD)
  • Ryan Stanford (MEAM Undergrad)
  • Benedict Onyekwe (ROBO Master's)
  • Jingshuo Li (MEAM Master's)
  • Neel Mulay (MEAM Master's)
  • Yipeng Zhang (MEAM Master's)

Acknowledgments

The “Leveraging Fluid-Structure Interactions for Efficient Control in Geophysical Flows” project is supported by the NSF Grant No. 2121887. The “Salp-Inspired Reconfigurable Robot Platform for Long-Term Distributed Sensing” project is supported by ONR award #N00014-23-1-2068.

  1. This project is in collaboration with Ani Hsieh’s lab from University of Pennsylvania, Eric Forgoston’s lab from Montclair State University, and Philip Yecko’s lab from The Cooper Union. ↩︎