Challenges in flexible microsystem manufacturing : fabrication, robotic assembly, control, and packaging.

Microsystems have been investigated with renewed interest for the last three decades because of the emerging development of microelectromechanical system (MEMS) technology and the advancement of nanotechnology. The applications of microrobots and distributed sensors have the potential to revolutionize micro and nano manufacturing and have other important health applications for drug delivery and minimal invasive surgery. A class of microrobots studied in this thesis, such as the Solid Articulated Four Axis Microrobot (sAFAM) are driven by MEMS actuators, transmissions, and end-effectors realized by 3-Dimensional MEMS assembly. Another class of microrobots studied here, like those competing in the annual IEEE Mobile Microrobot Challenge event (MMC) are untethered and driven by external fields, such as magnetic fields generated by a focused permanent magnet. A third class of microsystems studied in this thesis includes distributed MEMS pressure sensors for robotic skin applications that are manufactured in the cleanroom and packaged in our lab. In this thesis, we discuss typical challenges associated with the fabrication, robotic assembly and packaging of these microsystems. For sAFAM we discuss challenges arising from pick and place manipulation under microscopic closed-loop control, as well as bonding and attachment of silicon MEMS microparts. For MMC, we discuss challenges arising from cooperative manipulation of microparts that advance the capabilities of magnetic micro-agents. Custom microrobotic hardware configured and demonstrated during this research (such as the NeXus microassembly station) include micro-positioners, microscopes, and controllers driven via LabVIEW. Finally, we also discuss challenges arising in distributed sensor manufacturing. We describe sensor fabrication steps using clean-room techniques on Kapton flexible substrates, and present results of lamination, interconnection and testing of such sensors are presented

単一運動性微生物の刺激応答計測のためのマイクロロボティックプラットホーム

九州工業大学博士学位論文 学位記番号:生工博甲第355号 学位授与年月日:令和元年9月20日1 Introduction|2 Observation Platform|3 Stimulation Platform|4 Application to Actual Motile Microorganisms|5 Conclusion九州工業大学令和元年

Oceanic Challenges to Technological Solutions : A Review of Autonomous Underwater Vehicle Path Technologies in Biomimicry, Control, Navigation, and Sensing

Autonomous Underwater Vehicles (AUVs) epitomize a revolutionary stride in underwater exploration, seamlessly assuming tasks once exclusive to manned vehicles. Their collaborative prowess within joint missions has inaugurated a new epoch of intricate applications in underwater domains. This study’s primary aim is to scrutinize recent technological advancements in AUVs and their role in navigating the complexities of underwater environments. Through a meticulous review of literature and empirical studies, this review synthesizes recent technological strides, spotlighting developments in biomimicry models, cutting-edge control systems, adaptive navigation algorithms, and pivotal sensor arrays crucial for exploring and mapping the ocean floor. The article meticulously delineates the profound impact of AUVs on underwater robotics, offering a comprehensive panorama of advancements and illustrating their far-reaching implications for underwater exploration and mapping. This review furnishes a holistic comprehension of the current landscape of AUV technology. This condensed overview furnishes a swift comparative analysis, aiding in discerning the focal points of each study while spotlighting gaps and intersections within the existing body of knowledge. It efficiently steers researchers toward complementary sources, enabling a focused examination and judicious allocation of time to the most pertinent studies. Furthermore, it functions as a blueprint for comprehensive studies within the AUV domain, pinpointing areas where amalgamating multiple sources would yield a more comprehensive understanding. By elucidating the purpose, employing a robust methodology, and anticipating comprehensive results, this study endeavors to serve as a cornerstone resource that not only encapsulates recent technological strides but also provides actionable insights and directions for advancing the field of underwater robotics.© 2024 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/fi=vertaisarvioitu|en=peerReviewed

Bioinspired Soft Robotics: state of the art, challenges, and future directions

Purpose of Review: This review provides an overview of the state of the art in bioinspired soft robotics with by examining advancements in actuation, functionality, modeling, and control. Recent Findings: Recent research into actuation methods, such as artificial muscles, have expanded the functionality and potential use of bioinspired soft robots. Additionally, the application of finite dimensional models has improved computational efficiency for modeling soft continuum systems, and garnered interest as a basis for controller formulation. Summary: Bioinspiration in the field of soft robotics has led to diverse approaches to problems in a range of task spaces. In particular, new capabilities in system simplification, miniaturization, and untethering have each contributed to the field's growth. There is still significant room for improvement in the streamlining of design and manufacturing for these systems, as well as in their control

Weakly Supervised Caveline Detection For AUV Navigation Inside Underwater Caves

Underwater caves are challenging environments that are crucial for water resource management, and for our understanding of hydro-geology and history. Mapping underwater caves is a time-consuming, labor-intensive, and hazardous operation. For autonomous cave mapping by underwater robots, the major challenge lies in vision-based estimation in the complete absence of ambient light, which results in constantly moving shadows due to the motion of the camera-light setup. Thus, detecting and following the caveline as navigation guidance is paramount for robots in autonomous cave mapping missions. In this paper, we present a computationally light caveline detection model based on a novel Vision Transformer (ViT)-based learning pipeline. We address the problem of scarce annotated training data by a weakly supervised formulation where the learning is reinforced through a series of noisy predictions from intermediate sub-optimal models. We validate the utility and effectiveness of such weak supervision for caveline detection and tracking in three different cave locations: USA, Mexico, and Spain. Experimental results demonstrate that our proposed model, CL-ViT, balances the robustness-efficiency trade-off, ensuring good generalization performance while offering 10+ FPS on single-board (Jetson TX2) devices