Cui, X;
Sun, B;
Zhu, Y;
Yang, N;
Zhang, H;
Cui, W;
Fan, D;
(2024)
Enhancing efficiency and propulsion in bio-mimetic robotic fish through end-to-end deep reinforcement learning.
Physics of Fluids
, 36
(3)
, Article 031910. 10.1063/5.0192993.
![[thumbnail of 031910_1_5.0192993.pdf]](https://discovery.ucl.ac.uk/style/images/fileicons/text.png) |
Text
031910_1_5.0192993.pdf - Published Version Access restricted to UCL open access staff until 21 March 2025. Download (4MB) |
Abstract
Aquatic organisms are known for their ability to generate efficient propulsion with low energy expenditure. While existing research has sought to leverage bio-inspired structures to reduce energy costs in underwater robotics, the crucial role of control policies in enhancing efficiency has often been overlooked. In this study, we optimize the motion of a bio-mimetic robotic fish using deep reinforcement learning (DRL) to maximize propulsion efficiency and minimize energy consumption. Our novel DRL approach incorporates extended pressure perception, a transformer model processing sequences of observations, and a policy transfer scheme. Notably, significantly improved training stability and speed within our approach allow for end-to-end training of the robotic fish. This enables agiler responses to hydrodynamic environments and possesses greater optimization potential compared to pre-defined motion pattern controls. Our experiments are conducted on a serially connected rigid robotic fish in a free stream with a Reynolds number of 6000 using computational fluid dynamics simulations. The DRL-trained policies yield impressive results, demonstrating both high efficiency and propulsion. The policies also showcase the agent's embodiment, skillfully utilizing its body structure and engaging with surrounding fluid dynamics, as revealed through flow analysis. This study provides valuable insights into the bio-mimetic underwater robots optimization through DRL training, capitalizing on their structural advantages, and ultimately contributing to more efficient underwater propulsion systems.
| Type: | Article |
|---|---|
| Title: | Enhancing efficiency and propulsion in bio-mimetic robotic fish through end-to-end deep reinforcement learning |
| DOI: | 10.1063/5.0192993 |
| Publisher version: | https://doi.org/10.1063/5.0192993 |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10190128 |
Archive Staff Only
 |
View Item |
