マルチ スケール キノウ ヲ ユウスル コウソク ジドウ マイクロ マニピュレーション システム

Ebubekir Avci, Chanh-Nghiem Nguyen, Kenichi Ohara, Yasushi Mae, Tatsuo Arai, Analysis and suppression of residual vibration in microhand for high-speed single-cell manipulation, International Journal of Mechatronics and Automation, 2013-Vol.3, No.2, pp.110-11

Hybrid optical and magnetic manipulation of microrobots

Microrobotic systems have the potential to provide precise manipulation on cellular level for diagnostics, drug delivery and surgical interventions. These systems vary from tethered to untethered microrobots with sizes below a micrometer to a few microns. However, their main disadvantage is that they do not have the same capabilities in terms of degrees-of-freedom, sensing and control as macroscale robotic systems. In particular, their lack of on-board sensing for pose or force feedback, their control methods and interface for automated or manual user control are limited as well as their geometry has few degrees-of-freedom making three-dimensional manipulation more challenging. This PhD project is on the development of a micromanipulation framework that can be used for single cell analysis using the Optical Tweezers as well as a combination of optical trapping and magnetic actuation for recon gurable microassembly. The focus is on untethered microrobots with sizes up to a few tens of microns that can be used in enclosed environments for ex vivo and in vitro medical applications. The work presented investigates the following aspects of microrobots for single cell analysis: i) The microfabrication procedure and design considerations that are taken into account in order to fabricate components for three-dimensional micromanipulation and microassembly, ii) vision-based methods to provide 6-degree-offreedom position and orientation feedback which is essential for closed-loop control, iii) manual and shared control manipulation methodologies that take into account the user input for multiple microrobot or three-dimensional microstructure manipulation and iv) a methodology for recon gurable microassembly combining the Optical Tweezers with magnetic actuation into a hybrid method of actuation for microassembly.Open Acces

Design and realization of a microassembly workstation

With the miniaturization of products to the levels of micrometers and the recent developments in microsystem fabrication technologies, there is a great need for an assembly process for the formation of complex hybrid microsystems. Integration of microcomponents made up of different materials and manufactured using different micro fabrication techniques is still a primary challenge since some of the fundamental problems originating from the small size of parts to be manipulated, high precision necessity and specific problems of the microworld in that field are still not fully investigated. In this thesis, design and development of an open-architecture and reconfigurable microassembly workstation for efficient and reliable assembly of micromachined parts is presented. The workstation is designed to be used as a research tool for investigation of the problems in microassembly. The development of such a workstation includes the design of: (i) a manipulation system consisting of motion stages providing necessary travel range and precision for the realization of assembly tasks, (ii) a vision system to visualize the microworld and the determination of the position and orientation of micro components to be assembled, (iii) a robust control system and necessary fixtures for the end effectors that allow easy change of manipulation tools and make the system ready for the desired task. In addition tele-operated and semi-automated assembly concepts are implemented. The design is verified by implementing tasks in various ranges for micro-parts manipulation. The versatility of the workstation is demonstrated and high accuracy of positioning is shown

Automatic Microassembly of Tissue Engineering Scaffold

Ph.DDOCTOR OF PHILOSOPH

Microrobots for wafer scale microfactory: design fabrication integration and control.

Future assembly technologies will involve higher automation levels, in order to satisfy increased micro scale or nano scale precision requirements. Traditionally, assembly using a top-down robotic approach has been well-studied and applied to micro-electronics and MEMS industries, but less so in nanotechnology. With the bloom of nanotechnology ever since the 1990s, newly designed products with new materials, coatings and nanoparticles are gradually entering everyone’s life, while the industry has grown into a billion-dollar volume worldwide. Traditionally, nanotechnology products are assembled using bottom-up methods, such as self-assembly, rather than with top-down robotic assembly. This is due to considerations of volume handling of large quantities of components, and the high cost associated to top-down manipulation with the required precision. However, the bottom-up manufacturing methods have certain limitations, such as components need to have pre-define shapes and surface coatings, and the number of assembly components is limited to very few. For example, in the case of self-assembly of nano-cubes with origami design, post-assembly manipulation of cubes in large quantities and cost-efficiency is still challenging. In this thesis, we envision a new paradigm for nano scale assembly, realized with the help of a wafer-scale microfactory containing large numbers of MEMS microrobots. These robots will work together to enhance the throughput of the factory, while their cost will be reduced when compared to conventional nano positioners. To fulfill the microfactory vision, numerous challenges related to design, power, control and nanoscale task completion by these microrobots must be overcome. In this work, we study three types of microrobots for the microfactory: a world’s first laser-driven micrometer-size locomotor called ChevBot，a stationary millimeter-size robotic arm, called Solid Articulated Four Axes Microrobot (sAFAM), and a light-powered centimeter-size crawler microrobot called SolarPede. The ChevBot can perform autonomous navigation and positioning on a dry surface with the guidance of a laser beam. The sAFAM has been designed to perform nano positioning in four degrees of freedom, and nanoscale tasks such as indentation, and manipulation. And the SolarPede serves as a mobile workspace or transporter in the microfactory environment

Computer Vision Measurements for Automated Microrobotic Paper Fiber Studies

The mechanical characterization of paper fibers and paper fiber bonds determines the key parameters affecting the mechanical properties of paper. Although bulk measurements from test sheets can give average values, they do not yield any real fiber-level data. The current, state-of-the-art methods for fiberlevel measurements are slow and laborious, requiring delicate manual handling of microscopic samples. There are commercial microrobotic actuators that allow automated or tele-operated manipulation of microscopic objects such as fibers, but it is challenging to acquire the data needed to guide such demanding manipulation. This thesis presents a solution to the illumination problem and computer vision algorithms for obtaining the required data. The solutions are designed for a microrobotic platform that comprises actuators for manipulating the fibers and one or two microscope cameras for visual feedback.The algorithms have been developed both for wet fibers, which can be treated as 2D objects, and for dry fibers and fiber bonds, which are treated as 3D objects. The major innovations in the algorithms are the rules for the micromanipulation of the curly fiber strands and the automated 3D measurements of microscale objects with random geometries. The solutions are validated by imaging and manipulation experiments with wet and dry paper fibers and dry paper fiber bonds. In the imaging experiments, the results are compared with the reference data obtained either from an experienced human or another imaging device. The results show that these solutions provide morphological data about the fibers which is accurate and precise enough to enable automated fiber manipulation. Although this thesis is focused on the manipulation of paper fibers and paper fiber bonds, both the illumination solution and the computer vision algorithms are applicable to other types of fibrous materials

Force Sensing and Control in Micromanipulation

Ph.DDOCTOR OF PHILOSOPH

Automatic Microassembly System for tissue engineering- Assisted with top-view and force control

Master'sMASTER OF ENGINEERIN

The development of optical nanomachines for studying molecules : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Mechatronics Engineering at Massey University, Palmerston North, New Zealand

Chapter 3 is ©2020 IEEE. Accepted manuscript is reprinted, with permission, from 2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). Chapter 5 is ©2022 IEEE. Accepted manuscript is reprinted, with permission, from 2022 International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS).Optical tweezers have been used for a number of applications since their invention by Arthur Ashkin in 1986, and are particularly useful for biological and biophysical studies due to their exceptionally high spatial and force-based resolution. The same intense laser focus that allows light to be used as a tool for micro-nanoscale manipulation also has the potential to damage the objects being studied, and the extremely high force resolution is coupled with the limitation of very low forces. There is potential to overcome these drawbacks of optical manipulation through making use of another laser based technique: two-photon absorption polymerisation (TPAP). This thesis has brought these together to demonstrate the uses of optical nanomachines as helpful tools for optical tweezer studies. The project was highly interdisciplinary, concerning the intersection of optical trapping, 3D micromachine design and development, and DNA stretching. The thesis was based around the strategy of first developing microrobots and demonstrating their manipulation using optical tweezers, then adjusting the design for specific applications. Microlevers were developed for lever-assisted DNA stretching and amplification of optical forces. The influence of design features and TPAP parameters on microlever functionality was investigated; particularly the influence of overlapping area and presence of supports, and the effects of differently shaped "trapping handles". These features were important as lever functionality was tested in solutions of different ionic strength, and stable trapping of the levers was required for force amplification. DNA stretching was chosen as a target application for distanced-application of optical forces due to its status as a well-known and characterised example of single-molecule studies with optical tweezers. Amplification of optical forces was also seen as an application that could demonstrate the utility of optical micromachines, and microlevers with a 2:1 lever arm ratio were developed to produce consistent, two-fold amplification of optical forces, in a first for unsupported, pin-jointed optical microrobotics. It is hoped that in the future fully-remote, micromachine-assisted studies will extend optical tweezer studies of laser-sensitive subjects, as well as increasing the forces that can be applied, and the results obtained in this thesis are encouraging. All in all, the thesis confirms the potential of optical micromachines for aiding studies using optical tweezers, and demonstrates concrete progress in both design and application

Learning-based robotic manipulation for dynamic object handling : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Mechatronic Engineering at the School of Food and Advanced Technology, Massey University, Turitea Campus, Palmerston North, New Zealand

Figures are re-used in this thesis with permission of their respective publishers or under a Creative Commons licence.Recent trends have shown that the lifecycles and production volumes of modern products are shortening. Consequently, many manufacturers subject to frequent change prefer flexible and reconfigurable production systems. Such schemes are often achieved by means of manual assembly, as conventional automated systems are perceived as lacking flexibility. Production lines that incorporate human workers are particularly common within consumer electronics and small appliances. Artificial intelligence (AI) is a possible avenue to achieve smart robotic automation in this context. In this research it is argued that a robust, autonomous object handling process plays a crucial role in future manufacturing systems that incorporate robotics—key to further closing the gap between manual and fully automated production. Novel object grasping is a difficult task, confounded by many factors including object geometry, weight distribution, friction coefficients and deformation characteristics. Sensing and actuation accuracy can also significantly impact manipulation quality. Another challenge is understanding the relationship between these factors, a specific grasping strategy, the robotic arm and the employed end-effector. Manipulation has been a central research topic within robotics for many years. Some works focus on design, i.e. specifying a gripper-object interface such that the effects of imprecise gripper placement and other confounding control-related factors are mitigated. Many universal robotic gripper designs have been considered, including 3-fingered gripper designs, anthropomorphic grippers, granular jamming end-effectors and underactuated mechanisms. While such approaches have maintained some interest, contemporary works predominantly utilise machine learning in conjunction with imaging technologies and generic force-closure end-effectors. Neural networks that utilise supervised and unsupervised learning schemes with an RGB or RGB-D input make up the bulk of publications within this field. Though many solutions have been studied, automatically generating a robust grasp configuration for objects not known a priori, remains an open-ended problem. An element of this issue relates to a lack of objective performance metrics to quantify the effectiveness of a solution—which has traditionally driven the direction of community focus by highlighting gaps in the state-of-the-art. This research employs monocular vision and deep learning to generate—and select from—a set of hypothesis grasps. A significant portion of this research relates to the process by which a final grasp is selected. Grasp synthesis is achieved by sampling the workspace using convolutional neural networks trained to recognise prospective grasp areas. Each potential pose is evaluated by the proposed method in conjunction with other input modalities—such as load-cells and an alternate perspective. To overcome human bias and build upon traditional metrics, scores are established to objectively quantify the quality of an executed grasp trial. Learning frameworks that aim to maximise for these scores are employed in the selection process to improve performance. The proposed methodology and associated metrics are empirically evaluated. A physical prototype system was constructed, employing a Dobot Magician robotic manipulator, vision enclosure, imaging system, conveyor, sensing unit and control system. Over 4,000 trials were conducted utilising 100 objects. Experimentation showed that robotic manipulation quality could be improved by 10.3% when selecting to optimise for the proposed metrics—quantified by a metric related to translational error. Trials further demonstrated a grasp success rate of 99.3% for known objects and 98.9% for objects for which a priori information is unavailable. For unknown objects, this equated to an improvement of approximately 10% relative to other similar methodologies in literature. A 5.3% reduction in grasp rate was observed when removing the metrics as selection criteria for the prototype system. The system operated at approximately 1 Hz when contemporary hardware was employed. Experimentation demonstrated that selecting a grasp pose based on the proposed metrics improved grasp rates by up to 4.6% for known objects and 2.5% for unknown objects—compared to selecting for grasp rate alone. This project was sponsored by the Richard and Mary Earle Technology Trust, the Ken and Elizabeth Powell Bursary and the Massey University Foundation. Without the financial support provided by these entities, it would not have been possible to construct the physical robotic system used for testing and experimentation. This research adds to the field of robotic manipulation, contributing to topics on grasp-induced error analysis, post-grasp error minimisation, grasp synthesis framework design and general grasp synthesis. Three journal publications and one IEEE Xplore paper have been published as a result of this research