Real-time robotic tasks for cyber-physical avatars

Although modern robots can perform complex tasks using sophisticated algorithms that are specialized to a particular task and environment, creating robots capable of completing tasks in unstructured environments without human guidance (e.g., through teleoperation) remains a challenge. In this research, we present a framework to meet this challenge for a "cyberphysical avatar," which is defined to be a semi-autonomous robotic system that adjusts to an unstructured environment and performs physical tasks subject to critical timing constraints while under human supervision. This thesis first realizes a cyberphysical avatar that integrates three key technologies: (1) whole body-compliant control, (2) skill acquisition from machine learning (neuroevolution methods and deep learning), and (3) vision-based control through visual servoing. Body-compliant control is essential for operator safety because avatars perform cooperative tasks in close proximity to humans; machine learning enables "programming" avatars such that they can be used by non-experts for a large array of tasks, some unforeseen, in an unstructured environment; the visual servoing technique is indispensable for facilitating feedback control in human avatar interaction. This thesis proposes and demonstrates a systematically incremental approach to automating robotic tasks by decomposing a non-trivial task into stages, each of which may be automated by integrating the aforementioned techniques. We design and implement the controllers for two semi-autonomous robots that integrate three key techniques for grasping and pick-and-place tasks. While a general theory is beyond reach, we present a study on the tradeoffs between three design metrics for robotic task systems: (1) the amount of training effort for the robots to perform the task, (2) the time available to complete the task when the command is given, and (3) the quality of the result of the performed task. The tradeoff study in this design space uses the imprecise computation model as a framework to evaluate specific types of tasks: (1) grasping an unknown object and (2) placing the object in a target position. We demonstrate the generality of our integration methodology by applying it to two different robots, Dreamer and Hoppy. Our approach is evaluated by the performance of the robots in trading off between task completion time, training time and task completion success rate, in an environment similar to those in the recent Amazon Picking Challenge.Computer Science

A Comparison of Thermal Deformation of Scroll Profiles inside Oil-free Scroll Vacuum Pump and Compressor via CAE/CFD Analysis

Scroll machine is simply constructed by fixed and orbit scrolls, rotary shaft, and some mechanical components. It can impressively operate at low noise level with high reliability and high efficiency. Scroll machine achieves oil-free application through reasonable clearance control, cooling solution, and the tip seal application, and has been designed and applied as vacuum pump or compressor. In order to compactly design structure and optimize the gaps or clearances of a scroll machine, the issue of heat deformation must be considered. Deformation inside a scroll machine is not easy to be discovered, but is the necessary information for scroll profile design. In this study, the internal flow fields of oil-free scroll vacuum pump and compressor are obtained by CFD analysis. Based on the results of flow fields, this study shows the basic performance of a scroll machine, including loading on structures, gas torque, volume flow rate, and the pulsation of outlet pressure. The fluid phenomena under sub-atmospheric and positive pressure are quite different. The difference would cause different heat transfer and heat deformation. Therefore, the fluid-thermal-solid coupling analysis is also carried out. The temperature distribution of scroll structures, the thermal deformation, and gap changes are also discussed in this study

Effects of epidural compression on stellate neurons and thalamocortical afferent fibers in the rat primary somatosensory cortex

A number of neurological disorders such as epidural hematoma can cause compression of cerebral cortex. We here tested the hypothesis that sustained compression of primary somatosensory cortex may affect stellate neurons and thalamocortical afferent (TCA) fibers. A rat model with barrel cortex subjected to bead epidural compression was used. Golgi‑Cox staining analyses showed the shrinkage of dendritic arbors and the stripping of dendritic spines of stellate neurons for at least 3 months post‑lesion. Anterograde tracing analyses exhibited a progressive decline of TCA fiber density in barrel field for 6 months post‑lesion. Due to the abrupt decrease of TCA fiber density at 3 days after compression, we further used electron microscopy to investigate the ultrastructure of TCA fibers at this time. Some TCA fiber terminal profiles with dissolved or darkened mitochondria and fewer synaptic vesicles were distorted and broken. Furthermore, the disruption of mitochondria and myelin sheath was observed in some myelinated TCA fibers. In addition, expressions of oxidative markers 3‑nitrotyrosine and 4‑hydroxynonenal were elevated in barrel field post‑lesion. Treatment of antioxidant ascorbic acid or apocynin was able to reverse the increase of oxidative stress and the decline of TCA fiber density, rather than the shrinkage of dendrites and the stripping of dendritic spines of stellate neurons post‑lesion. Together, these results indicate that sustained epidural compression of primary somatosensory cortex affects the TCA fibers and the dendrites of stellate neurons for a prolonged period. In addition, oxidative stress is responsible for the reduction of TCA fiber density in barrels rather than the shrinkage of dendrites and the stripping of dendritic spines of stellate neurons