Unmanned aerial vehicles (UAVs) have been increasingly utilized in several commercial and civil applications such as package delivery, traffic monitoring, precision agriculture, remote sensing, border patrol, hazard monitoring, disaster relief, and search and rescue operations to collect data/imagery for a ground command station nearby. Current implementations of UAV-based operations heavily rely on control, inference, task allocation, and planning from a human controller that can limit the operation of drones in missions where the operation field is not fully observable to the human controller prior to the mission and reliable and continuous communication is not available between the UAVs and the ground station or among the teammate UAVs during the mission. The UAVs can be particularly useful in such unstructured and unknown environments to provide agile surveying or search-and-rescue operations. Therefore, the future of UAV technology focuses on the development of small, low-cost, and smart drones with a higher level of autonomy. Such drones can facilitate a wide range of sophisticated missions performed by a fleet of cooperative UAVs with minimum human intervention and lower cost. 

The objective of this research is to develop theoretical and practical frameworks for operation, situational awareness, coordination, and communication of a network of fully autonomous multi-agent systems (e.g., UAVs) in dynamic and unknown environments with minimum human interventions. This research can facilitate a new set of applications for autonomous multi-agent systems in remote and dynamic environments. This project involves an integrated set of research, implementation, and experimental validation thrusts to develop novel frameworks for autonomous decision making, coalition formation, coordination, spectrum management, and task allocation in UAV systems. The developed techniques can be utilized in other multi-agent cognitive systems such as robotic systems, and autonomous driving vehicles where quick search, surveillance, and reactions are required with limited human interventions.

This project also offers a number of educational and outreach activities to integrate the results of this research in curriculum enhancement, student mentorship, engaging underrepresented minority and female students, developing hands-on UAV-based sensing experiments for elementary and middle school students and outreach to the community to enhance public awareness about new applications of UAV systems through collaboration with Flagstaff Festival of Science.
 

New vulnerabilities arise in Cyber-Physical Systems (CPS) as new technologies are integrated to interact and control physical systems. In addition to software and network attacks, sensor attacks are a crucial security risk in CPS, where an attacker alters sensing information to negatively interfere with the physical system. Acting on malicious sensor information can cause serious consequences. While many research efforts have been devoted to protecting CPS from sensor attacks, several critical problems remain unresolved. First, existing attack detection works tend to minimize the detection delay and false alarms at the same time; this goal, however, is not always achievable due to the inherent trade-off between the two metrics. Second, there has been much work on attack detection, yet a key question remains concerning what to do after detecting an attack. Importantly, a CPS should detect an attack and recover from the attack before irreparable consequences occur. Third, the interrelation between detection and recovery has met with insufficient attention: Integrating detection and recovery techniques would result in more effective defenses against sensor attacks.

This project aims to address these key problems and develop novel detection and recovery techniques. The project aims to achieve timely and safe defense against sensor attacks by addressing real-time adaptive-attack detection and recovery in CPS. First, this project explores new attack detection techniques that can dynamically balance the trade-off between the detection delay and the false-alarm rate in a data-driven fashion. In this way, the detector will deliver attack detection with predictable delay and maintain the usability of the detection approach. Second, this project pursues new recovery techniques that bring the system back to a safe state before a recovery deadline while minimizing the degradation to the mission being executed by the system. Third, this project investigates efficient techniques that address the attack detection and recovery in a coordinated fashion to significantly improve response to attacks. Specific research tasks include the development of real-time adaptive sensor attack detection techniques, real-time attack recovery techniques, and attack detection and recovery coordination techniques. The developed techniques will be implemented and evaluated on multiple CPS simulators and an autonomous vehicle testbed.

Robotic manipulation and automation systems have received a lot of attention in the past few years and have demonstrated promising performance in various applications spanning smart manufacturing, remote surgery, and home automation. These advances have been partly due to advanced perception capabilities (using vision and haptics) and new learning models and algorithms for manipulation and control. However, state-of-the-art cyber-physical systems remain limited in their sensing and perception to a direct line of sight and direct contact with the objects they need to perceive. The goal of this project is to design, build, and evaluate a cyber-physical system that can sense, perceive, learn, and manipulate far beyond what is feasible using existing systems. To do so, the research will explore the terahertz band, which offers a new sensing dimension by inferring the inherent material properties of objects via wireless terahertz signals and without direct contact. This project will also explore radio-frequency signals that can traverse occlusions. Building on these emerging sensing modalities, the core of the project focuses on developing full-spectrum perception, control, learning, and manipulation tasks. The success of this project will result in CPS system architectures with unprecedented capabilities, enabling fundamentally new opportunities to make robotic manipulation more efficient and allowing robots to perform new complex tasks that have not been possible before. 

The project will enable robotic perception via full-spectral wireless sensing in order to unlock unprecedented robotic manipulation capabilities. This research involves learning synergies between sensing and control- whereby sensing is used for control and vice-versa - to optimize the end-to-end cyber-physical tasks. In particular, this research includes three inter-connected thrusts: (i) It will enable a new sensing modality that exploits high-resolution terahertz frequencies for robotic imaging and inference; (ii) It aims to build a new learning platform for full spectrum (mmWave, THz, and vision) perception to enable beyond-vision perception and reasoning in non-line-of-sight and cluttered environments, where optical systems lack in performance; and (iii) It presents a platform to learn the synergies between sensing and control to further co-optimize the end-to-end robotic manipulations tasks. These capabilities can open up entirely new realms of possibility to industrial robotics as well as assistive, warehousing, and smart home robotic. The research will be evaluated through extensive experimentation, prototype design, and system implementation. The results will be disseminated through close collaboration with industry and publications in top research venues.

uring normal operations an aircraft is operated by its autopilot. When the autopilot sense a dangerous condition, near or outside of the flight envelope, the autopilot disengages itself, returning control to the pilot. Well-trained pilots typically can deal with modest out-of-envelope challenges. A pilot who can deal with a significantly compromised flight envelope is very remarkable as happened with Captain Chesley Sullenberger ("Sully") and co-pilot Jeffrey Skiles on US Airways Flight 1549 in 2009 when the aircraft struck a flock of geese just northeast of the George Washington Bridge and suddenly lost all engine power over Manhattan. The pilots glided their plane extraordinarily skillfully to a ditching in the Hudson River off Midtown Manhattan, saving all the passengers and averting a catastrophic crash in New York City. The National Transportation Safety Board official described it as the most successful ditching in aviation history. This capability to operate safely despite the exceptional situation well-outside the norm is the essence of this project, Virtual Sully.

Virtual Sully technology is a development towards full pilotless autonomy, capable of identifying the failure/fault, estimating the remaining control authority, assessing the environment and planning a new feasible mission, doing path planning and executing it safely within the compromised flight envelope. This architecture replaces the traditional top-down one-way adaptation between mission planning, trajectory generation, tracking and stabilizing controller, with a two-way adaptation between mission planning, trajectory generation, and the adaptation of controller parameters to improve the stability and robustness of the control system. The following thrusts are considered: 1) monitoring and capability auditing; 2) high-assurance control with multi-level adaptation; 3) fault-tolerant architecture for unmanned autonomous systems (UAS) with real-time guarantees; 4) development of hardware-in-the loop simulation environment and flight tests using unmanned air vehicle (UAV) prototypes. Fault-tolerant computing infrastructure that can withstand high-stress situations will be integrated within flight control architecture that adapts at multiple levels. The feasibility evaluation of the missions and regenerated trajectories within UAV's remaining capabilities is pursued with real-time guarantees. The testbed is based on hardware-in-the-loop simulation for various failures, as well as extensive tests using real UAVs.

This research is to explore various approaches for a single-chip detector that (1) can record semiconductor-chip-package tampering activity without the need of a battery, (2) can be placed inside semiconductor chip packages through a nozzle-less droplet ejector, and (3) can be wirelessly interrogated without need to open up the semiconductor package. The project?s novelties are (1) the integration of a pyroelectric energy converter, a GHz resonator, an acceleration switch and an on-chip antenna, all on a single chip at a low cost and (2) a submillimeter-sized, battery-less, tamper detector chip that can be placed inside a semiconductor package through a droplet ejector and that can be wirelessly interrogated (for any recorded tampering activity) from the outside of the semiconductor package. The project's broader significance and importance are the foundational technology for individualized detection and recording of tampering activities, without needing an electrical power source such as the battery, and for the recorded event to be wirelessly interrogated, particularly to ensure the authenticity of semiconductor chips. Also, the proposed study of droplet-ejector-based chip packaging will likely open up a new packaging technology for semiconductor chips, particularly for chips whose lateral dimensions are too small for robotic pick-and-placement. Thus, the research will impact the semiconductor industry the foremost, but will also likely help many other industries needing to detect activities involving temperature rise and mechanical banging without battery. The proposed passive resonator will also be broadly applied to battery-less, passive security and identification such as radio frequency identification (RFID).

A single-chip semiconductor-tamper detector will be based on a pyroelectric energy converter (PEC) for generating a voltage and charge to break an RFID tag based on High-overtone Bulk Acoustic Resonator (HBAR) from heat associated with the tamper activity. A MEMS (microelectromechanical systems) acceleration switch will be designed to make an electrical connection between the PEC and the tag when mechanical shocks are applied to semiconductor chips on a printed circuit board (PCB), as a part of a tampering activity to detach semiconductor chips from PCB, so that the voltage and charge of the PEC due to the heat from de-soldering process may electrically break the tag. As the tampering activity involves banging PCBs against hard objects after a de-soldering process, a normally-off MEMS switch will be designed as an acceleration or vibration sensor to detect the banging. Counterfeiters may have options to scavenge IC chips with other methods than the method covered by the proposed tamper detector, but at no avail or at too high costs. The project will show the feasibility of a submillimeter-sized, battery-less and wireless, tamper detecting chip that can be mounted inside a semiconductor package through a nozzle-less droplet ejector. The proposed study will pave foundational technology for a paradigm-shifting concept of individualized detection and recording of tampering activities to ensure authenticity of semiconductor chips. The proposed transducers will likely impact wireless sensor network, energy harvesting, etc., and thus, the research will greatly impact many industries including RFID and wireless sensor industries in addition to semiconductor industry.

Increasing wildfire costs---a reflection of climate variability and development within wildlands---drive calls for new national capabilities to manage wildfires. The great potential of unmanned aerial systems (UAS) has not yet been fully utilized in this domain due to the lack of holistic, resilient, flexible, and cost-effective monitoring protocols. This project will develop UAS-based fire management strategies to use autonomous unmanned aerial vehicles (UAVs) in an optimal, efficient, and safe way to assist the first responders during the fire detection, management, and evacuation stages. The project is a collaborative effort between Northern Arizona University (NAU), Georgia Institute of Technology (GaTech), Desert Research Institute (DRI), and the National Center for Atmospheric Research (NCAR). The team has established ongoing collaborations with the U.S. Forest Service (USFS) in Pacific Northwest Research Station, Kaibab National Forest (NF), and Arizona Department of Forestry and Fire Management to perform multiple field tests during the prescribed and managed fires. This proposal's objective is to develop an integrated framework satisfying unmet wildland fire management needs, with key advances in scientific and engineering methods by using a network of low-cost and small autonomous UAVs along with ground vehicles during different stages of fire management operations including: (i) early detection in remote and forest areas using autonomous UAVs; (ii) fast active geo-mapping of the fire heat map on flying drones; (iii) real-time video streaming of the fire spread; and (iv) finding optimal evacuation paths using autonomous UAVs to guide the ground vehicles and firefighters for fast and safe evacuation. 

This project will advance the frontier of disaster management by developing: (i) an innovative drone-based forest fire detection and monitoring technology for rapid intervention in hard-to-access areas with minimal human intervention to protect firefighter lives; (ii) multi-level fire modeling to offer strategic, event-scale, and new on-board, low-computation tactics using fast fire mapping from UAVs; and (iii) a bounded reasoning-based planning mechanism where the UAVs identify the fastest and safest evacuation roads for firefighters and fire-trucks in highly dynamic and uncertain dangerous zones. The developed technologies will be translational to a broad range of applications such as disaster (flooding, fire, mud slides, terrorism) management, where quick search, surveillance, and responses are required with limited human interventions. This project will also contribute to future engineering curricula and pursue a substantial integration of research and education while also engaging female and underrepresented minority students, developing hands-on research experiments for K-12 students. 

This project is in response to the NSF Cyber-Physical Systems 20-563 solicitation.

In societal-scale cyber-physical systems (SCPS), machine learning algorithms are increasingly becoming the interface between stakeholders---from matching drivers and riders on ride-sharing platforms to the real-time scheduling of energy resources in electric vehicle (EV) charging stations. The fact that the different stakeholders in these systems have different objectives gives rise to strategic interactions which can result in inefficiencies and negative externalities across the SCPS. This NSF CAREER project seeks to develop a foundational understanding of the strategic interactions that arise in SCPS, the impacts they have on social welfare, and how they affect algorithmic decision-making. The goal is to shift how engineers design algorithms for SCPS. Currently, learning algorithms are trained and developed in isolation, and uncertainty and strategic interactions are treated---if at all--- as adversarial or worst-case. In contrast, the proposed research aims to develop algorithms that can consider economic interactions, human behavior, and uncertainty when making decisions. The theory and algorithms developed through this project will be validated on two physical testbeds: 1. an EV charging testbed where drivers routinely mis-report preferences for faster charging, and 2. the Caltech Social Science Experimental Laboratory where controlled experiments will be conducted to understand how people respond to algorithms. The proposal also includes an integrated education and outreach plan, which includes outreach to K-12 students and new undergraduate and graduate courses on the complexities of learning in SCPS.

Key goals of this project include developing a unified design methodology for learning in the presence of strategic behaviors in SCPS and the systematic study of the control actions and control authority that individual users and policymakers can wield to achieve societal goals. The fact that strategic manipulations in SCPS are played out through the (mis)-reporting of data or through algorithmic decision-making distinguishes these problems from those classically studied in game theory and economics. Furthermore, in contrast with existing work in computer science and economics that study strategic interactions, this project aims to take a dynamic view of SCPS, which leverages tools and ideas from dynamical systems theory and stochastic processes to complement ideas in machine learning, game theory, and behavioral economics. This perspective will allow for new insights into how repeated interactions affect strategic decision-making in SCPS and which design decisions impact learning in game theoretic settings. This opens the door to new insights and the analysis of previously overlooked control knobs for achieving societal goals in SCPS.

This NSF CPS project aims to redesign the information structure utilized by system operators in today's electricity markets to accommodate technological advances in energy generation and consumption. The project will bring transformative change to power systems by incentivizing and facilitating the integration of non-conventional energy resources via a principled design of bidding, aggregation, and market mechanisms. Such integration will provide operators with the necessary flexibility to operate a network with high levels of renewable penetration. This will be achieved by a comprehensive bottom-down approach that will first identify the intrinsic cost of utilizing novel renewable resources and accommodate the operational ecosystem accordingly. The intellectual merits of the project include novel theories and algorithms for operating a vast number of distributed resources and testbed implementations of markets and controls. The project's broader impacts include K-12 and undergraduate programs, including in-class and extra-curricular STEM activities through, e.g., Hopkins in-class and extra-curricular STEM activities, and the Caltech WAVE summer research program.

Introducing distributed energy resources (DERs) at a large scale requires rethinking power grid operations to account for increased uncertainty and new operational constraints. The proposed research undertakes this task by overhauling the information structure that markets and grid controls utilize. We seek to characterize and shape how information is exchanged and used to manage the grid to improve efficiency, stability, and incentive alignment. The research is organized into three thrusts. Thrust 1 emphasizes the role of information in coordination. It seeks to characterize DER costs and constraints, designing bidding strategies tailored to convey information about the atypical characteristics of DER costs. Thrust 2 aims to develop aggregation strategies that efficiently manage resources by accounting for their cost and constraints, integrating DERs via an aggregate bid that protects sensitive user information and is robust to market manipulation. Finally, Thrust 3 characterizes the overall impact of DERs on operations. We will examine how user incentives that span across markets implicitly couple market outcomes and develop design mechanisms to mitigate inter-market price manipulation. We will also design pricing schemes that provide efficient DER allocation while preserving real-time operational constraints such as frequency regulation.

Distributed cyber-physical systems (CPS), where multiple computer programs distributed across a network interact with each other and with physical processes, are challenging to design and verify. Such systems are found in industrial automation, transportation systems, energy distribution systems, and many other applications. This project is developing a ?systems theory? for such applications that provides a good analytical toolkit for understanding how a system will behave when networks misbehave. It is building tools that make it possible to reason about the design of safe and reliable distributed CPS applications in an accessible user-friendly environment. In distributed applications, Brewer's CAP theorem tells us that when networks become partitioned, there is a tradeoff between consistency and availability in distributed software systems. Consistency is agreement on the values of shared variables; availability is the ability to respond to reads and writes of those shared variables. This project builds on an extension that has shown that consistency, availability, and network latency can be quantified, and that the CAP theorem can be generalized to give an algebraic relation between these quantities. This generalization is called the CAL theorem because it replaces ?network Partitioning? with ?Latency,? where partitioning is just a limiting case of latency. The CAL theorem can be used to help design distributed systems that fail gracefully when network performance degrades. With increasing latency, either consistency or availability (or some measure of both) must be sacrificed, and the CAL theorem quantifies these sacrifices. 

This project is applying the CAL theorem to distributed CPS. The project is deriving the fundamental limits implied by the CAL theorem and developing a methodology for systematically trading off availability and consistency in application-specific ways. The application of the CAL theorem to CPS generalizes consistency to include agreement on the state of the physical world and availability to include the latency of software responses to stimulus from the physical world. Instead of focusing solely on network latency, the project adopts a measure called ?apparent latency? that includes network latency plus all other sources of latency (e.g., computation time). This measure is practically measurable. The project builds upon the recently developed Lingua Franca coordination language to provide system designers with concrete analysis and design tools to make the required tradeoffs in deployable software. The tools automatically produce graphical renditions of systems and user-friendly feedback on the concurrent, distributed, and real-time aspects.

The future of cyber-physical systems are smart technologies that can work collaboratively, cooperatively, and safely with humans. Smart technologies and humans will share autonomy, i.e., the right, obligation and ability to share control in order to meet their mutual objectives in the environment of operations. For example, surgical robots must interact with surgeons to increase their capabilities in performing high-precision surgeries, drones need to deliver packages to humans and places, and autonomous cars need to share roads with human-driven cars. In all such interactions, these systems must act safely despite the risks and uncertainties that are intrinsic with humans, technologies, and the environments in which they interact. The key insight of this project is that control strategies can be developed that increase safety in situations where a human needs to closely interact with a cyber-physical system (CPS) that is capable of autonomy or semi-autonomous action.

The goal of this project is to develop risk-aware interactive control and planning for achieving safe cyber-physical-human (CPS-h) systems. This project will advance the state-of-the-art of CPS-h planning and control in three main ways: (i) developing computationally tractable risk-aware trajectory planning algorithms that are suited to general autonomous CPS-h, (ii) developing a computationally efficient and empirically supported framework to account for risk-awareness in human?s decision-making, and (iii) deriving interaction-aware planning algorithms for achieving safe and efficient interactions between multiple risk-aware agents. The proposed algorithms will be extensively evaluated with human subjects in interaction with autonomous CPS-h such as autonomous cars and quadcopters. This work will have direct impact on many CPS-h domains including but not limited to multi-agent interactions, autonomous driving, collaboration and coordination between humans and autonomous agents in safety-critical scenarios.