Manufacturing and production have been big contributors to improved quality and sustainability of human life. Current market trends, such as consumer demand for variety, short product life cycles, high product quality and low cost, have resulted in the need for efficient, responsive, robust and sustainable manufacturing and production paradigm. 3D printing technologies hold the merit of affordability and customizability, while the key challenge in applying 3D printing for mass customization in real life is how to reduce the lead time per unit. The lead time of 3D printing a product unit comes from two sources, i.e., the pre-fabrication computation and manufacturing process. The pre-fabrication computation is increasingly significant and becomes the bottleneck in the manufacturing flow of mass customization in 3D Printing. This EArly-concept Grant for Exploratory Research (EAGER) project looks to address this problem through new computational methods with potential for two orders of magnitude reduction in time for pre-facbrication computation. This project aims to develop a transformative computational paradigm of 3D printing in mass customization. The project will pursue two novel and complementary objectives: 1) design a suite of quality-guaranteed geometric algorithms for the scalable and time-efficient pre-fabrication computation framework.; and 2) develop a low-complexity and efficient computing system to facilitate and accelerate the use of these methods and algorithms in Objective1. This new computer system focuses on domain-specific computing platforms as the next disruptive technology for power-performance-runtime efficiency improvement. Specifically, the team will develop accelerator-based architectures for computing primitives of geometric algorithms. This new hardware architecture will exploit the parallelism and customization to improve the efficiency of the new computational paradigm in 3D printing with less delay, lower complexity and higher computing power.

Legged robots have captured the imagination of society at large, through entertainment and through the dissemination of research findings. Yet, today's reality of what (bipedal) legged robots can do falls short of society's vision. A big part of the reason is that legged robots are viewed as surrogates for humans, able to go wherever humans can as aids or as assistants where it might also be too dangerous or risky. It is in the expectation of robustness and walking facility that today's research hits its limits, especially when the terrain has granular properties. Impeding progress is the lack of a holistic approach to the cyber-physical modeling and control of legged robots. The vision of this work is to unite experts in granular mechanics, optimal control, and learning theory in order to define a methodology for advancing cyber-physical systems (CPS) involving a tight coupling of the physical with the cyber through dynamic interactions that must be learned online. The proposed work will advance the science of cyber-physical systems by more explicitly tying sensing, perception, and computing to the optimization and control of physical systems whose properties are variable and uncertain. Achieving reliable, adaptive legged locomotion over terrain with arbitrary granular properties would transform several application domain areas of robotics; e.g., disaster response, agricultural and industrial robotics, and planetary robotics. More broadly, the same tools would apply to related CPS with regards to terrain aware exoskeleton and rehabilitation prosthetics for persons with missing, non-functional, or injured legs, as well as to energy networks with time-varying, nonlinear dynamics models. The CPS platform to be studied is that of a bipedal robot locomoting over granular ground material with uncertain physical properties (sand, gravel, dirt, etc.). The proposed work seeks to overcome current impediments to reliable legged locomotion over uncertain terrain type, which fundamentally relies on the controlled interaction of the robot's feet with the physical environment. The research goal is to improve the perception and control of legged locomotion over granular media for the express purpose of achieving robust, adaptive, terrain-aware locomotion. It revolves around the hypothesis that simple models with decent predictive performance and low computational overhead are sufficient for the optimal control formulations as the compute-constrained adaptive subsystem will both learn and classify the peculiarities of the terrain online. The main research objectives will involve: [1] a validated co-simulation platform for legged robot movement over granular media; [2] terrain-dependent, stable gait generation and gait transition strategies via optimal control; [3] online, compute-constrained learning of granular interactions for adaptation and terrain classification; and [4] validated contributions using experimental testbeds involving variable and unknown (to the robot) granular media. Given the high value of the robotic platforms and the research with regards to outreach and participation, they will be used as outreach tools and to create new educational modules for promotion of STEM fields. Further, the multi-disciplinary nature of the work will be highlighted in order to emphasize its importance.

Part 1: Upper-limb motor impairments arise from a wide range of clinical conditions including amputations, spinal cord injury, or stroke. Addressing lost hand function, therefore, is a major focus of rehabilitation interventions; and research in robotic hands and hand exoskeletons aimed at restoring fine motor control functions gained significant speed recently. Integration of these robots with neural control mechanisms is also an ongoing research direction. We will develop prosthetic and wearable hands controlled via nested control that seamlessly blends neural control based on human brain activity and dynamic control based on sensors on robots. These Hand Augmentation using Nested Decision (HAND) systems will also provide rudimentary tactile feedback to the user. The HAND design framework will contribute to the assistive and augmentative robotics field. The resulting technology will improve the quality of life for individuals with lost limb function. The project will help train engineers skilled in addressing multidisciplinary challenges. Through outreach activities, STEM careers will be promoted at the K-12 level, individuals from underrepresented groups in engineering will be recruited to engage in this research project, which will contribute to the diversity of the STEM workforce. Part 2: The team previously introduced the concept of human-in-the-loop cyber-physical systems (HILCPS). Using the HILCPS hardware-software co-design and automatic synthesis infrastructure, we will develop prosthetic and wearable HAND systems that are robust to uncertainty in human intent inference from physiological signals. One challenge arises from the fact that the human and the cyber system jointly operate on the same physical element. Synthesis of networked real-time applications from algorithm design environments poses a framework challenge. These will be addressed by a tightly coupled optimal nested control strategy that relies on EEG-EMG-context fusion for human intent inference. Custom distributed embedded computational and robotic platforms will be built and iteratively refined. This work will enhance the HILCPS design framework, while simultaneously making novel contributions to body/brain interface technology and assistive/augmentative robot technology. Specifically we will (1) develop a theoretical EEG-EMG-context fusion framework for agile HILCPS application domains; (2) develop theory for and design novel control theoretic solutions to handle uncertainty, blend motion/force planning with high-level human intent and ambient intelligence to robustly execute daily manipulation activities; (3) further develop and refine the HILCPS domain-specific design framework to enable rapid deployment of HILCPS algorithms onto distributed embedded systems, empowering a new class of real-time algorithms that achieve distributed embedded sensing, analysis, and decision making; (4) develop new paradigms to replace, retrain or augment hand function via the prosthetic/wearable HAND by optimizing performance on a subject-by-subject basis.

The objective of this research is to design a semi-automated, efficient, and secure emergency response system to reduce the time it takes emergency vehicles to reach their destinations, while increasing the safety of non-emergency vehicles and emergency vehicles alike. Providing route and maneuver guidance to emergency vehicles and non-emergency vehicles will make emergency travel safer and enable police and other first responders to reach and transport those in need, in less time. This should reduce the number of crashes involving emergency vehicles and associated litigation costs while improving medical outcomes, reducing property damage, and instilling greater public confidence in emergency services. At the same time, non-emergency vehicles will also be offered increased safety and, with the reduction of long delays attributed to emergency vehicles, experience reduced incident-related travel time, which will increase productivity and quality of life for drivers. Incorporating connected vehicles into the emergency response system will also provide synergistic opportunities for non-emergency vehicles, including live updates on accident sites, areas to avoid, and information on emergency routes that can be incorporated into navigation software so drivers can avoid potential delays. While the proposed system will naturally advance the quality of transportation in smart cities, it will also provide a platform for future techniques to build upon. For example, the proposed system could be connected with emergency care facilities to balance the load of emergency patients at hospitals, and act as a catalyst toward the realization of a fully-automated emergency response system. New courses and course modules will be developed to recruit and better prepare a future workforce that is well versed in multi-disciplinary collaborations. Video demos and a testbed will be used to showcase the research to the public. The key research component will be the design of an emergency response system that (1) dynamically determines EV routes, (2) coordinates actions by non-emergency vehicles using connected vehicle technology to efficiently and effectively clear paths for emergency vehicles, (3) is able to adapt to uncertain traffic and network conditions, and (4) is difficult to abuse or compromise. The project will result in (1) algorithms that dynamically select EV routes based on uncertain or limited traffic data, (2) emergency protocols that exploit connected vehicle technology to facilitate emergency vehicles maneuvers, (3) an automation module to assist with decision making and maneuvers, and (4) an infrastructure and vehicle hardening framework that prevents cyber abuse. Experiments will be performed on a testbed and a real test track to validate the proposed research.