A Novel Propeller Design for Micro-Swimming robot

The applications of a micro-swimming robot such as minimally invasive surgery, liquid pipeline robot etc. are widespread in recent years. The potential application fields are so inspiring, and it is becoming more and more achievable with the development of microbiology and Micro-Electro-Mechanical Systems (MEMS). The aim of this study is to improve the performance of micro-swimming robot through redesign the structure. To achieve the aim, this study reviewed all of the modelling methods of low Reynolds number flow including Resistive-force Theory (RFT), Slender Body Theory (SBT), and Immersed Boundary Method (IBM) etc. The swimming model with these methods has been analysed. Various aspects e.g. hydrodynamic interaction, design, development, optimisation and numerical methods from the previous researches have been studied. Based on the previous design of helix propeller for micro-swimmer, this study has proposed a novel propeller design for a micro-swimming robot which can improve the velocity with simplified propulsion structure. This design has adapted the coaxial symmetric double helix to improve the performance of propulsion and to increase stability. The central lines of two helical tails overlap completely to form a double helix structure, and its tail radial force is balanced with the same direction and can produce a stable axial motion. The verification of this design is conducted using two case studies. The first one is a pipe inspection robot which is in mm scale and swims in high viscosity flow that satisfies the low Reynolds number flow condition. Both simulation and experiment analysis are conducted for this case study. A cross-development method is adopted for the simulation analysis and prototype development. The experiment conditions are set up based on the simulation conditions. The conclusion from the analysis of simulation results gives suggestions to improve design and fabrication for the prototype. Some five revisions of simulation and four revisions of the prototype have been completed. The second case study is the human blood vessel robot. For the limitations of fabrication technology, only simulation is conducted, and the result is compared with previous researches. The results show that the proposed propeller design can improve velocity performance significantly. The main outcomes of this study are the design of a micro-swimming robot with higher velocity performance and the validation from both simulation and experiment

The dynamics and kinematics of bio-in swimming systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2013.Cataloged from PDF version of thesis. Page 168 blank.Includes bibliographical references (p. 155-167).The motion of biological systems in fluids is inherently complex, even for the simplest organisms. In this thesis, we develop methods of analyzing locomotion of both mechanical and biological systems with the aim of rationalizing biology and informing robotic design. We begin by building on existing visualization framework by studying an idealized swimmer: Purcell's three-link swimmer, at low Reynolds number. This framework allows us to illustrate the complete dynamics of the system, design gaits for motion planning and identify optimal gaits in terms of efficiency and speed. We extend the three-link swimmer case to include effects such as the interaction between the links. By studying several systems, we broaden the applicability of our framework. These systems include a two-link swimmer at low Reynolds number with offset centers of buoyancy and mass and a swimmer with a continuously deformable shape, the serpenoid swimmer. Drawing on the principles behind the serpenoid swimmer, we develop the kinematic decomposition, a method using a singular value decomposition (SVD) that describes the motion of complex systems in a low order manner. We show that with only two degrees of freedom, one can adequately describe an animal's motion. We apply this method to species in both high and low Reynolds number environments to elucidate different phenomena, including chemotaxing and species comparison in spermatozoa, gait changes in eels (steady versus accelerating), kinematic responses to viscosity and viscoelasticity in C. elegans (nematodes), and the Kirmin gait in trout. Combined with our visualization framework, we successfully illustrate the generalized utility of the kinematic decomposition method to explore and understand fundamental kinematics of a wide range of both natural and man-made systems.by Lisa Janelle Burton.Ph.D

Engineering derivatives from biological systems for advanced aerospace applications

The present study consisted of a literature survey, a survey of researchers, and a workshop on bionics. These tasks produced an extensive annotated bibliography of bionics research (282 citations), a directory of bionics researchers, and a workshop report on specific bionics research topics applicable to space technology. These deliverables are included as Appendix A, Appendix B, and Section 5.0, respectively. To provide organization to this highly interdisciplinary field and to serve as a guide for interested researchers, we have also prepared a taxonomy or classification of the various subelements of natural engineering systems. Finally, we have synthesized the results of the various components of this study into a discussion of the most promising opportunities for accelerated research, seeking solutions which apply engineering principles from natural systems to advanced aerospace problems. A discussion of opportunities within the areas of materials, structures, sensors, information processing, robotics, autonomous systems, life support systems, and aeronautics is given. Following the conclusions are six discipline summaries that highlight the potential benefits of research in these areas for NASA's space technology programs

Bio-inspired Dynamic Control Systems with Time Delays

The world around us exhibits a rich and ever changing environment of startling, bewildering and fascinating complexity. Almost everything is never as simple as it seems, but through the chaos we may catch fleeting glimpses of the mechanisms within. Throughout the history of human endeavour we have mimicked nature to harness it for our own ends. Our attempts to develop truly autonomous and intelligent machines have however struggled with the limitations of our human ability. This has encouraged some to shirk this responsibility and instead model biological processes and systems to do it for us. This Thesis explores the introduction of continuous time delays into biologically inspired dynamic control systems. We seek to exploit rich temporal dynamics found in physical and biological systems for modelling complex or adaptive behaviour through the artificial evolution of networks to control robots. Throughout, arguments have been presented for the modelling of delays not only to better represent key facets of physical and biological systems, but to increase the computational potential of such systems for the synthesis of control. The thorough investigation of the dynamics of small delayed networks with a wide range of time delays has been undertaken, with a detailed mathematical description of the fixed points of the system and possible oscillatory modes developed to fully describe the behaviour of a single node. Exploration of the behaviour for even small delayed networks illustrates the range of complex behaviour possible and guides the development of interesting solutions. To further exploit the potential of the rich dynamics in such systems, a novel approach to the 3D simulation of locomotory robots has been developed focussing on minimising the computational cost. To verify this simulation tool a simple quadruped robot was developed and the motion of the robot when undergoing a manually designed gait evaluated. The results displayed a high degree of agreement between the simulation and laser tracker data, verifying the accuracy of the model developed. A new model of a dynamic system which includes continuous time delays has been introduced, and its utility demonstrated in the evolution of networks for the solution of simple learning behaviours. A range of methods has been developed for determining the time delays, including the novel concept of representing the time delays as related to the distance between nodes in a spatial representation of the network. The application of these tools to a range of examples has been explored, from Gene Regulatory Networks (GRNs) to robot control and neural networks. The performance of these systems has been compared and contrasted with the efficacy of evolutionary runs for the same task over the whole range of network and delay types. It has been shown that delayed dynamic neural systems are at least as capable as traditional Continuous Time Recurrent Neural Networks (CTRNNs) and show significant performance improvements in the control of robot gaits. Experiments in adaptive behaviour, where there is not such a direct link between the enhanced system dynamics and performance, showed no such discernible improvement. Whilst we hypothesise that the ability of such delayed networks to generate switched pattern generating nodes may be useful in Evolutionary Robotics (ER) this was not borne out here. The spatial representation of delays was shown to be more efficient for larger networks, however these techniques restricted the search to lower complexity solutions or led to a significant falloff as the network structure becomes more complex. This would suggest that for anything other than a simple genotype, the direct method for encoding delays is likely most appropriate. With proven benefits for robot locomotion and the open potential for adaptive behaviour delayed dynamic systems for evolved control remain an interesting and promising field in complex systems research

From cell to robot : A bio-inspired locomotion device

Bionics or biomimetics is an interdisciplinary research field, a scientific approach to applicate naturally developed biological systems, methods and solutions to the study and design of technology and engineering systems. Therefore bionics is based on an exclusive mutuality between life sciences and technology and its associated sciences, such as robotics. Robots are special artificial agents, and they have much in common with biological agents in case of the need to adapt to their environment. A popular trend in robotics is the development of soft robots – artificial agents with a rather flexible skin or shape, propulsing itself with some type of crawling movement. These robots are able to deform and adapt to obstacles during locomotion, which is an advantage over classical wheeled or legged propulsion. Bionics is helpful in developing locomotion devices for robots, e. g. bio-inspired climbing robots, such as geckobots, utilise the biological gecko adhesion model for climbing. Most of these bio-inspired climbing robots have the disadvantage of using legs for locomotion. The idea is to find a new biological model for a bionic robotic locomotion device that is using an adhesion-dependent crawling locomotion, which allows the robot to climb (or at least be able to master inclinations) and still has a rather soft and deformable shape providing the flexibility of adaptation to obstacles or a changing environment. Surprisingly, single cells, such as amoebae or animal tissue cells, provide these required properties: the ability to crawl on surfaces by formation of adhesion bonds and a very deformable shape – a perfect model for such robots. These cells are reorganising their cytoskeletal cortex and create a visco-elastic gradient which is polarising the cell with a sol-like "sloppy" leading edge at the front and a gel-like "stiff" rear end. This work demonstrates that it is possible to transfer the biophysical locomotion mechanism of cell migration to a simulation model of soft robots, which use an adhesion-dependent mechanism to autonomously create a polarising elasticity gradient during motion. It introduces and analyses three robot models, which are able to move on surfaces with different built-in integrations of this polarisation mechanism. Simulations show that the robots are flexible enough to adapt to changing environments, such as rough surfaces. One model is even able to crawl on walls and ceilings against the direction of gravity. Finally, this work offers some ideas for possible constructions and usability of these robots, and what insights their analysis might give into principles of biological cell migration

Doctor of Philosophy

dissertationThis dissertation presents results documenting advancements on the control of untethered magnetic devices, such as magnetic \microrobots" and magnetically actuated capsuleendoscopes, motivated by problems in minimally invasive medicine. This dissertationfocuses on applying rotating magnetic elds for magnetic manipulation. The contributions include advancements in the way that helical microswimmers (devices that mimicthe propulsion of bacterial agella) are controlled in the presence of gravitational forces, advancements in ways that groups of untethered magnetic devices can be dierentiated and semi-independently controlled, advancements in the way that untethered magnetic device can be controlled with a single rotating permanent magnet, and an improved understanding in the nature of the magnetic force applied to an untethered device by a rotating magnet

Unlocking the Secrets of Multi-Flagellated Propulsion

In this work, unique high-speed imaging platforms and an array of theoretical analysis methods are used to thoroughly investigate eukaryotic multi-flagellated propulsion using Tritrichomonas foetus as a test case. Through experimental observations through our imaging system with superior resolution and capture rate exceeding that of previous studies, it was discovered for the first time that the T. foetus employs a strategy similar to that of the “run and tumble” strategies found in bacteria and Chlamydomonas; it has two distinct flagellar beating patterns that result in two different body swimming motions, linear and turning swimming. These two flagella patterns were then analyzed for the first time using two theoretical analysis methods that are often used to analyze uni-flagellated organisms; the Resistive Force Theory (RFT) and the Regularized Stokeslet Method (RSM). These theories were compared to uncover the more accurate method. Results showed that our modified-RFT model out-performed the RSM model. Due to these results, the quantitative analysis of the motion of each flagellum for both the swimming motions were carried out using the RFT method for the first time on a multi-flagellated cell, in both the 2-D and 3-D case. Digital Holographic Microscopy was used to produce the 3-D trajectory of the T.foetus for the first time. Through this method it was possible to for the first time, quantitatively analyze the thrust and energy contributions of each flagella in each direction. We find out that the turning motion dissipates approximately half as much energy as the linear swimming motion which leads to the belief that the motion is more energy efficient. The energy results coupled with the thrust results show the highly coordinated nature of multi-flagellated propulsion. Through this RFT model, it was observed that the propulsive force of the T.foetus is comparable to that of other eukaryotes with varying numbers of flagella like the sperm and Chlamydomonas, suggesting that higher thrust generation is not necessarily the goal of multi-flagellated propulsion, but these strategies result in greater maneuverability or sensing. Results from this study may serve as inspiration for biorobots due to the organism’s ideal size and finely controlled multi-flagellated propulsion

Challenges and attempts to make intelligent microswimmers

The study of microswimmers’ behavior, including their self-propulsion, interactions with the environment, and collective phenomena, has received significant attention over the past few decades due to its importance for various biological and medical applications. Microswimmers can easily access micro-fluidic channels and manipulate microscopic entities, enabling them to perform sophisticated tasks as untethered mobile microrobots inside the human body or microsize devices. Thanks to the advancements in micro/nano-technologies, a variety of synthetic and biohybrid microrobots have been designed and fabricated. Nevertheless, a key challenge arises: how to guide the microrobots to navigate through complex fluid environments and perform specific tasks. The model-free reinforcement learning (RL) technique appears to be a promising approach to address this problem. In this review article, we will first illustrate the complexities that microswimmers may face in realistic biological fluid environments. Subsequently, we will present recent experimental advancements in fabricating intelligent microswimmers using physical intelligence and biohybrid techniques. We then introduce several popular RL algorithms and summarize the recent progress for RL-powered microswimmers. Finally, the limitations and perspectives of the current studies in this field will be discussed