Fast ADMM Algorithm for Distributed Optimization with Adaptive Penalty

We propose new methods to speed up convergence of the Alternating Direction Method of Multipliers (ADMM), a common optimization tool in the context of large scale and distributed learning. The proposed method accelerates the speed of convergence by automatically deciding the constraint penalty needed for parameter consensus in each iteration. In addition, we also propose an extension of the method that adaptively determines the maximum number of iterations to update the penalty. We show that this approach effectively leads to an adaptive, dynamic network topology underlying the distributed optimization. The utility of the new penalty update schemes is demonstrated on both synthetic and real data, including a computer vision application of distributed structure from motion.Comment: 8 pages manuscript, 2 pages appendix, 5 figure

Autonomous planning and control of soft untethered grippers in unstructured environments

The use of small, maneuverable, untethered and reconfigurable robots could provide numerous advantages in various micromanipulation tasks. Examples include microassembly, pick-and-place of fragile microobjects for lab-on-a-chip applications, assisted hatching for in-vitro fertilization and minimally invasive surgery. This study assesses the potential of soft untethered magnetic grippers as alternatives or complements to conventional tethered or rigid micromanipulators. We demonstrate closed-loop control of untethered grippers and automated pick-and-place of biological material on porcine tissue in an unstructured environment. We also demonstrate the ability of the soft grippers to recognize and sort non-biological micro-scale objects. The fully autonomous nature of the experiments is made possible by the integration of planning and decision-making algorithms, as well as by closed-loop temperature and electromagnetic motion control. The grippers are capable of completing pick-and-place tasks of biological material at an average velocity of 1.8±0.71 mm/s and a drop-off error of 0.62±0.22 mm. Color-sensitive sorting of three micro-scale objects is completed at a velocity of 1.21±0.68 mm/s and a drop-off error of 0.85±0.41 mm. Our findings suggest that improved autonomous un-tethered grippers could augment the capabilities of current soft-robotic instruments especially in advanced tasks involving manipulation

Magnetic motion control and planning of untethered soft grippers using ultrasound image feedback

Soft miniaturized untethered grippers can be used to manipulate and transport biological material in unstructured and tortuous environments. Previous studies on control of soft miniaturized grippers employed cameras and optical images as a feedback modality. However, the use of cameras might be unsuitable for localizing miniaturized agents that navigate within the human body. In this paper, we demonstrate the wireless magnetic motion control and planning of soft untethered grippers using feedback extracted from B-mode ultrasound images. Results show that our system employing ultrasound images can be used to control the miniaturized grippers with an average tracking error of 0.4±0.13 mm without payload and 0.36±0.05 mm when the agent performs a transportation task with a payload. The proposed ultrasound feedback magnetic control system demonstrates the ability to control miniaturized grippers in situations where visual feedback cannot be provided via cameras

Control of untethered soft grippers for pick-and-place tasks

In order to handle complex tasks in hard-toreach environments, small-scale robots have to possess suitable dexterous and untethered control capabilities. The fabrication and manipulation of soft small- scale grippers complying to these requirements is now made possible by advances in material science and robotics. In this paper, we use soft small-scale grippers to demonstrate pick-and-place tasks. The precise remote control is obtained by altering both the magnetic field gradient and the temperature in the workspace. This allows us to regulate the position and grasping configuration of the soft thermally-responsive hydrogel-nanoparticle composite magnetic grippers. The magnetic closed-loop control achieves precise localization with an average region-of-convergence of the gripper of 0.12±0.05 mm. The micro-sized payload can be placed with a positioning error of 0.57±0.33 mm. The soft grippers move with an average velocity of 0.72±0.13 mm/s without a micro-sized payload, and at 1.09±0.07 mm/s with a micro-sized payloa

DESIGN, CHARACTERIZATION & APPLICATION OF STIMULI RESPONSIVE SELF-FOLDING SOFT MICROSYSTEMS

This dissertation demonstrates design, characterization, and application of stimuli responsive self-folding soft microsystem. Stimuli responsive self-folding soft robotics is an emerging attractive field to mimic motions of biological systems by utilizing hydrogels, polymer or hybrid combination of them, which guide three dimensional (3D) shape change. The stimuli responsive soft robotics have numerous distinct advantages such as lightweight, inexpensive, flexible, easy to design, and able to operate in aqueous environments with no aids of complex feedback sensors, wires, tethers or batteries, as different from conventional electrically or pneumatically driven metals or/and ceramic based robotics. To establish the foundations of collective innovations and integrated intelligent biomimetic stimuli responsive microsystems, a self-folding strategy can be adapted. Self-folding is a new paradigm to manipulate 2D thin film structures transforming to 3D when triggered by external stimuli such as heat, pH, light, ionic strengths, mechanical stress, magnetic or electrical fields etc. In order to broadly implement this strategy, nanoelectromechanical systems (NEMS) and microelectromechanical systems (MEMS) inspired high throughput photolithography can be adapted. Conventional VLSI fabrication such as photolithography, etching, physical vapor deposition (PVD) can provide reliable and reproducible approaches to pattern particular designs of polymer or/and hydrogels in a cost-effective manner. Finally, this dissertation demonstrates the design principles, materials characterization and high applicability of those reconfigurable stimuli responsive self-folding soft microsystems for a variety of applications such as soft robotic actuators, biological biopsy tools, and reconfigurable opto-electrical sensors etc

Advances in Biodegradable Soft Robots

Biodegradable soft robots have been proposed for a variety of intelligent applications in soft robotics, flexible electronics, and bionics. Biodegradability offers an extraordinary functional advantage to soft robots for operations accompanying smart shape transformation in response to external stimuli such as heat, pH, and light. This review primarily surveyed the current advanced scientific and engineering strategies for integrating biodegradable materials within stimuli-responsive soft robots. It also focused on the fabrication methodologies of multiscale biodegradable soft robots, and highlighted the role of biodegradable soft robots in enhancing the multifunctional properties of drug delivery capsules, biopsy tools, smart actuators, and sensors. Lastly, the current challenges and perspectives on the future development of intelligent soft robots for operation in real environments were discussed

Assembly of a 3D Cellular Computer Using Folded E-Blocks

The assembly of integrated circuits in three dimensions (3D) provides a possible solution to address the ever-increasing demands of modern day electronic devices. It has been suggested that by using the third dimension, devices with high density, defect tolerance, short interconnects and small overall form factors could be created. However, apart from pseudo 3D architecture, such as monolithic integration, die, or wafer stacking, the creation of paradigms to integrate electronic low-complexity cellular building blocks in architecture that has tile space in all three dimensions has remained elusive. Here, we present software and hardware foundations for a truly 3D cellular computational devices that could be realized in practice. The computing architecture relies on the scalable, self-configurable and defect-tolerant cell matrix. The hardware is based on a scalable and manufacturable approach for 3D assembly using folded polyhedral electronic blocks (E-blocks). We created monomers, dimers and 2 x 2 x 2 assemblies of polyhedral E-blocks and verified the computational capabilities by implementing simple logic functions. We further show that 63.2% more compact 3D circuits can be obtained with our design automation tools compared to a 2D architecture. Our results provide a proof-of-concept for a scalable and manufacture-ready process for constructing massive-scale 3D computational devices

Model-based tracking of miniaturized grippers using particle swarm optimization

Micro-sized agents can benefit robotic minimally invasive surgery since they can be inserted into the human body and use natural pathways such as arteries and veins or the gastrointestinal tract, to reach their target for drug delivery or diagnosis. Recently, miniaturized agents with shape-changing and gripping capabilities have provided significant advantages in performing grasping, transportation, and manipulation tasks. In order to robustly perform such tasks, it is of utmost importance to properly estimate their overall configuration. This paper presents a novel solution to the problem of estimating and tracking the 3D position, orientation and configuration of the tips of miniaturized grippers from RGB marker-less visual observations obtained by a microscope. We consider this as an optimization problem, seeking for the gripper model parameters that minimize the discrepancy between hypothesized instances of the gripper model and actual observations of the miniaturized gripper. This optimization problem is solved using a variant of the Particle Swarm Optimization algorithm. The proposed approach has been evaluated on several image sequences showing the grippers moving, rotating, opening/closing and grasping biological material

4D Multiscale Origami Soft Robots: A Review

Time-dependent shape-transferable soft robots are important for various intelligent applications in flexible electronics and bionics. Four-dimensional (4D) shape changes can offer versatile functional advantages during operations to soft robots that respond to external environmental stimuli, including heat, pH, light, electric, or pneumatic triggers. This review investigates the current advances in multiscale soft robots that can display 4D shape transformations. This review first focuses on material selection to demonstrate 4D origami-driven shape transformations. Second, this review investigates versatile fabrication strategies to form the 4D mechanical structures of soft robots. Third, this review surveys the folding, rolling, bending, and wrinkling mechanisms of soft robots during operation. Fourth, this review highlights the diverse applications of 4D origami-driven soft robots in actuators, sensors, and bionics. Finally, perspectives on future directions and challenges in the development of intelligent soft robots in real operational environments are discussed

A GPU-accelerated model-based tracker for untethered submillimeter grippers

Miniaturized grippers that possess an untethered structure are suitable for a wide range of tasks, ranging from micromanipulation and microassembly to minimally invasive surgical interventions. In order to robustly perform such tasks, it is critical to properly estimate their overall configuration. Previous studies on tracking and control of miniaturized agents estimated mainly their 2D pixel position, mostly using cameras and optical images as a feedback modality. This paper presents a novel solution to the problem of estimating and tracking the 3D position, orientation and configuration of the tips of submillimeter grippers from marker-less visual observations. We consider this as an optimization problem, which is solved using a variant of the Particle Swarm Optimization algorithm. The proposed approach has been implemented in a Graphics Processing Unit (GPU) which allows a user to track the submillimeter agents online. The proposed approach has been evaluated on several image sequences obtained from a camera and on B-mode ultrasound images obtained from an ultrasound probe. The sequences show the grippers moving, rotating, opening/closing and grasping biological material. Qualitative results obtained using both hydrogel (soft) and metallic (hard) grippers with different shapes and sizes ranging from 750 microns to 4 mm (tip to tip), demonstrate the capability of the proposed method to track the agent in all the video sequences. Quantitative results obtained by processing synthetic data reveal a tracking position error of 25 ±7μm and orientation error of 1.7 ± 1.3 degrees. We believe that the proposed technique can be applied to different stimuli responsive miniaturized agents, allowing the user to estimate the full configuration of complex agents from visual marker-less observations