3D hydrodynamic analysis of a biomimetic robot fish

This paper presents a three-dimensional (3D) computational fluid dynamic simulation of a biomimetic robot fish. Fluent and user-defined function (UDF) is used to define the movement of the robot fish and the Dynamic Mesh is used to mimic the fish swimming in water. Hydrodynamic analysis is done in this paper too. The aim of this study is to get comparative data about hydrodynamic properties of those guidelines to improve the design, remote control and flexibility of the underwater robot fish

3D locomotion biomimetic robot fish with haptic feedback

This thesis developed a biomimetic robot fish and built a novel haptic robot fish system based on the kinematic modelling and three-dimentional computational fluid dynamic (CFD) hydrodynamic analysis. The most important contribution is the successful CFD simulation of the robot fish, supporting users in understanding the hydrodynamic properties around it

Developing High Performance Linear Carangiform Swimming

This thesis examines the linear swimming motion of Carangiform fish, and investigates how to improve the swimming performance of robotic fish within the fields of kinematic modeling and mechanical engineering, in a successful attempt to replicate the high performance of real fish. Intensive research was conducted in order to study the Carangiform swimming motion, where observational studies of the common carp were undertaken. Firstly, a full-body length Carangiform swimming motion is proposed to coordinate the anterior, mid-body and posterior displacements in an attempt to reduce the large kinematic errors in the existing free swimming robotic fish. It optimizes the forces around the centre of mass and initiates the starting moment of added mass upstream therefore increasing performance, in terms of swimming speed. The introduced pattern is experimentally tested against the traditional approach (of posterior confined body motion). A first generation robotic fish is devised with a novel mechanical drive system operating in the two swimming patterns. It is shown conclusively that by coordinating the full-body length of the Carangiform swimming motion a significant increase in linear swimming speed is gained over the traditional posterior confined wave form and reduces the large kinematic errors seen in existing free swimming robotic fish (Achieving the cruising speeds of real fish). Based on the experimental results of the first generation, a further three robotic fish are developed: (A) iSplash-OPTIMIZE: it becomes clear that further tuning of the kinematic parameters may provide a greater performance increase in the distance travelled per tail beat. (B) iSplash-II: it shows that combining the critical aspects of the mechanical drive system of iSplash-I with higher frequencies and higher productive forces can significantly increase maximum velocity. This prototype is able to outperform real Carangiform fish in terms of average maximum velocity (measured in body lengths/ second) and endurance, the duration that top speed is maintained. (C) iSplash-MICRO: it verifies that the mechanical drive system could be reduced in scale to improve navigational exploration, whilst retaining high-speed swimming performance. A small robotic fish is detailed with an equivalent maximum velocity (BL/s) to real fish

Review of Computational Fluid Dynamics Analysis in Biomimetic Applications for Underwater Vehicles

Biomimetics, which draws inspiration from nature, has emerged as a key approach in the development of underwater vehicles. The integration of this approach with computational fluid dynamics (CFD) has further propelled research in this field. CFD, as an effective tool for dynamic analysis, contributes significantly to understanding and resolving complex fluid dynamic problems in underwater vehicles. Biomimetics seeks to harness innovative inspiration from the biological world. Through the imitation of the structure, behavior, and functions of organisms, biomimetics enables the creation of efficient and unique designs. These designs are aimed at enhancing the speed, reliability, and maneuverability of underwater vehicles, as well as reducing drag and noise. CFD technology, which is capable of precisely predicting and simulating fluid flow behaviors, plays a crucial role in optimizing the structural design of underwater vehicles, thereby significantly enhancing their hydrodynamic and kinematic performances. Combining biomimetics and CFD technology introduces a novel approach to underwater vehicle design and unveils broad prospects for research in natural science and engineering applications. Consequently, this paper aims to review the application of CFD technology in the biomimicry of underwater vehicles, with a primary focus on biomimetic propulsion, biomimetic drag reduction, and biomimetic noise reduction. Additionally, it explores the challenges faced in this field and anticipates future advancements

Recent Advances in Multi Robot Systems

To design a team of robots which is able to perform given tasks is a great concern of many members of robotics community. There are many problems left to be solved in order to have the fully functional robot team. Robotics community is trying hard to solve such problems (navigation, task allocation, communication, adaptation, control, ...). This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field. It is focused on the challenging issues of team architectures, vehicle learning and adaptation, heterogeneous group control and cooperation, task selection, dynamic autonomy, mixed initiative, and human and robot team interaction. The book consists of 16 chapters introducing both basic research and advanced developments. Topics covered include kinematics, dynamic analysis, accuracy, optimization design, modelling, simulation and control of multi robot systems

Efficient collective swimming by harnessing vortices through deep reinforcement learning

Fish in schooling formations navigate complex flow-fields replete with mechanical energy in the vortex wakes of their companions. Their schooling behaviour has been associated with evolutionary advantages including collective energy savings. How fish harvest energy from their complex fluid environment and the underlying physical mechanisms governing energy-extraction during collective swimming, is still unknown. Here we show that fish can improve their sustained propulsive efficiency by actively following, and judiciously intercepting, vortices in the wake of other swimmers. This swimming strategy leads to collective energy-savings and is revealed through the first ever combination of deep reinforcement learning with high-fidelity flow simulations. We find that a `smart-swimmer' can adapt its position and body deformation to synchronise with the momentum of the oncoming vortices, improving its average swimming-efficiency at no cost to the leader. The results show that fish may harvest energy deposited in vortices produced by their peers, and support the conjecture that swimming in formation is energetically advantageous. Moreover, this study demonstrates that deep reinforcement learning can produce navigation algorithms for complex flow-fields, with promising implications for energy savings in autonomous robotic swarms.Comment: 26 pages, 14 figure