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.

In the United States, there is still a great disparity in medical care and most profoundly for emergency care, where limited facilities and remote location play a central role. Based on the Wessels Living History Farm report, the doctor to patient ratio in the United States is 30 to 10,000 in large metropolitan areas, only 5 to 10,000 in most rural areas; and the highest death rates are often found in the most rural counties. For emergency patient care, time to definitive treatment is critical. However, deciding the most effective care for an acute patient requires knowledge and experience. Though medical best practice guidelines exist and are in hospital handbooks, they are often lengthy and difficult to apply clinically. The challenges are exaggerated for doctors in rural areas and emergency medical technicians (EMT) during patient transport. This project's solution to transform emergency care at rural hospitals is to use innovative CPS technologies to help hospitals to improve their adherence to medical best practice. The key to assist medical staff with different levels of experience and skills to adhere to medical best practice is to transform required processes described in medical texts to an executable, adaptive, and distributed medical best practice guidance (EMBG) system. Compared to the computerized sepsis best practice protocol, the EMBG system faces a much bigger challenge as it has to adapt the best practice across rural hospitals, ambulances and center hospitals with different levels of staff expertise and equipment capabilities. Using a Global Positioning System analogy, a GPS leads drivers with different route familiarity to their destination through an optimal route based on the drivers' preferences, the EMBG system leads medical personnel to follow the best medical guideline path to provide emergency care and minimize the time to definitive treatment for acute patients. The project makes the following contributions: 1) The codification of complex medical knowledge is an important advancement in knowledge capture and representation; 2) Pathophysiological model driven communication in high speed ambulance advances life critical communication technology; and 3) Reduced complexity software architectures designed for formal verification bridges the gap between formal method research and system engineering.

In the United States, there is still a great disparity in medical care and most profoundly for emergency care, where limited facilities and remote location play a central role. Based on the Wessels Living History Farm report, the doctor to patient ratio in the United States is 30 to 10,000 in large metropolitan areas, only 5 to 10,000 in most rural areas; and the highest death rates are often found in the most rural counties. For emergency patient care, time to definitive treatment is critical. However, deciding the most effective care for an acute patient requires knowledge and experience. Though medical best practice guidelines exist and are in hospital handbooks, they are often lengthy and difficult to apply clinically. The challenges are exaggerated for doctors in rural areas and emergency medical technicians (EMT) during patient transport. This project's solution to transform emergency care at rural hospitals is to use innovative CPS technologies to help hospitals to improve their adherence to medical best practice. The key to assist medical staff with different levels of experience and skills to adhere to medical best practice is to transform required processes described in medical texts to an executable, adaptive, and distributed medical best practice guidance (EMBG) system. Compared to the computerized sepsis best practice protocol, the EMBG system faces a much bigger challenge as it has to adapt the best practice across rural hospitals, ambulances and center hospitals with different levels of staff expertise and equipment capabilities. Using a Global Positioning System analogy, a GPS leads drivers with different route familiarity to their destination through an optimal route based on the drivers' preferences, the EMBG system leads medical personnel to follow the best medical guideline path to provide emergency care and minimize the time to definitive treatment for acute patients. The project makes the following contributions: 1) The codification of complex medical knowledge is an important advancement in knowledge capture and representation; 2) Pathophysiological model driven communication in high speed ambulance advances life critical communication technology; and 3) Reduced complexity software architectures designed for formal verification bridges the gap between formal method research and system engineering.

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.

Epilepsy is one of the most common neurological disorders, affecting between 0.4% and 1% of the world's population. While seizures can be controlled in approximately two thirds of newly diagnosed patients through the use of one or more antiepileptic drugs (AEDs), the remainder experience seizures even on multiple medications. The primary impacts of the chronic condition of epilepsy on a patient are a lower quality of life, loss of productivity, comorbidities, and increased risk of death. Epilepsy is an intermittent brain disorder, and in localization-related epilepsy, which is the most common form of epilepsy, one or a few discrete brain areas (the seizure focus or seizure foci) are believed to be responsible for seizure initiation. More recent approaches with implantable electrical stimulation seizure control devices hold value as a therapeutic option for the control of seizures. These devices, directly or indirectly, target the seizure focus and seek to control its expression. In this project we will build a multichannel brain implantable device based on emerging cyber physical system (CPS) principles. This brain implantable CPS device will incorporate key design features to make the device dependable, scalable, composable, certifiable, and interoperable. The device will operate over the life of an animal, or a patient, and continuously record brain activity and stimulate the brain when seizure related activity is detected to abort an impending seizure. Episodic brain disorders such as epilepsy have a considerable impact on a patient's productivity and quality of life and may be life-threatening when seizures cannot be controlled with medications. The goal of this project is to create a second generation brain-implantable sensing and stimulating device (BISSD) based on emerging CPS principles and practice. The development of a BISSD as a exemplifies several defining aspects that inform and illustrate core CPS principles. First, to meet the important challenge of regulatory approval a composable, scalable and certifiable framework that supports testing in multiple species is proposed. Second, a BISSD must be wholly integrated with the patient and fully cognizant at every instant of brain state, including dynamic changes in both the normal and abnormal expression of brain physiology and therapeutic intervention. Thus, this project seeks a tight conjunction of the cyber solution that must monitor itself and monitor and stimulate the brain using implanted, adaptable, distributed, and networked electrodes, and the physical system which in this case is the intermittently failing human brain. Third, a BISSD must function for an extensive period of time, up to the life of the patient, because each surgery to place and retrieve a BISSD carries an attendant risk. This requirement necessitates a dependable solution, which this project seeks to reliably achieve through both an understanding of the brain's foreign body response and a unique hierarchical fault-tolerant design. Fourth, an advanced salient approaches to acquire, compress, and analyze sensor signals to achieve real-time monitoring and control of seizures is employed. This project should yield a powerful, scalable CPS framework for robust fault-tolerant implantable medical devices with real-time processing that can grow with advances in sensors, sensing modalities, time-series analysis, real-time computation, control, materials, power and knowledge of underlying biology. The USA has a competitive advantage in the control of seizures in medically refractory epilepsy. In the modern era, epilepsy surgery evolved in the USA in the 1970s and spread from here to other parts of the world. Similarly, the USA enjoys a competitive advantage in BISSDs, and success in this effort will enable the USA to build on and maintain this advantage. In addition to epilepsy, advances made here can be expected to benefit the treatment of other neurological and psychiatric brain disorders.

Inherent vulnerabilities of information and communication technology systems to cyber-attacks (e.g., malware) impose significant security risks to Cyber-Physical Systems (CPS). This is evidenced by a number of recent accidents. Noticeably, current distributed control of CPS is not really attack-resilient (ensuring task completion despite attacks). Although provable resilience would significantly lift the trustworthiness of CPS, existing defenses are rather ad-hoc and mainly focus on attack detection. In addition, while network attacks have been extensively studied, resilient-to-malware distributed control has been rarely investigated. This project aims to bridge the gap. It aims to investigate provably correct distributed attack-resilient control of CPS. The project will focus on a representative class of CPS, namely unmanned-vehicle-operator networks, and its four main research thrusts are: (1) The development of a distributed attack-resilient control framework to ensure task completion of multiple vehicles despite network attacks and malware attacks, (2) The synthesis of novel distributed attack-resilient control algorithms to deal with network attacks, (3) The design of estimation algorithms to detect malware attacks on vehicles, and computationally efficient algorithms which allow clean vehicles to avoid the collision with the vehicles compromised by malware, and (4) The validation of the cost-effectiveness of the proposed distributed attack-resilient control framework via a principled systematic evaluation plan. The research findings profoundly impact CPS security of a variety of engineering disciplines beyond unmanned-vehicle-operator networks, including smart grid, smart buildings and intelligent transportation systems. The proposed research is interdisciplinary and involves interactions among security, control, distributed algorithms and robotics. This will lead to educational and training opportunities that cross traditional disciplinary boundaries for high-school, undergraduate and graduate students in STEM.