Cyber-physical systems interact with the world through software controlling physical effectors. Carefully designed controllers, implemented as safety-critical control software, also interact with other parts of the software suite, and may be difficult to separate, verify, or maintain. Moreover, some software changes, not intended to impact control system performance, do change controller response through a variety of means including interaction with external libraries or unmodeled changes only existing in the cyber system (e.g., exception handling). As a result, identifying safety-critical control software, its boundaries with other embedded software in the system, and the way in which control software evolves could help developers isolate, test, and verify control implementation, and improve control software development. In this work we present an automated technique, based on a novel application of machine learning, to detect commits related to control software, its changes, and how the control software evolves. We leverage messages from developers (e.g., commit comments), and code changes themselves to understand how control software is refined, extended, and adapted over time. We examine three distinct, popular, real-world, safety-critical autopilots—ArduPilot, Paparazzi UAV, and LibrePilot to test our method demonstrating an effective detection rate of 0.95 for control-related code changes.

UAVs have been studied and manufactured to help create wireless communications networks that are more flexible and cost-effective than a typical wireless network. These UAV networks could help bridge the digital divide in rural America by providing wireless communications service to areas where cell companies find it too expensive to build conventional cell towers. To test different aspects of a UAV based millimeter-wave frequency network, we created a MATLAB simulation. The simulation visualizes a digital twin of a farm in eastern Nebraska where UAVs are tested. The simulation allows for link budgeting and interference management calculations by accommodating changes in transmitter and receiver location, frequency of the network, power of the transmitted signal, weather conditions, and antenna specifications. The simulation is able to calculate critical network values such as signal-to-interference-plus-noise ratio (SINR), path loss, atmospheric loss, and antenna gains under dynamically changing conditions.

Summary A cyber‐physical system (CPS) is a collection of computing devices that communicate with each other, operate in the target environment via actuators and interact with the physical world through sensors in a feedback loop. CPSs need to be safe and reliable and function in accordance with their requirements. Testing, focusing on a CPS model and/or its code, is the primary approach used by engineers to achieve this. Generating, selecting and prioritizing test cases that can reveal faults in CPSs, from the wide range of possible input values and stimuli that affect their operation, are of central importance in this process. To date, however, in our search of the literature, we have found no comprehensive survey of research on test case generation, selection and prioritization for CPSs. In this article, therefore, we report the results of a survey of approaches for generating, selecting and prioritizing test cases for CPSs; the results illustrate the progress that has been made on these approaches to date, the properties that characterize the approaches and the challenges that remain open in these areas of research.

Computing can be thought of as a service provided to a system to yield actionable tasks enacted by physical hardware. But rarely is control thought to be in the service of enhancing computation. Consideration of that perspective is what motivates co-regulation, our framework for holistic cyber-physical control of autonomous vehicles. In this paper we elaborate on how co-regulation will enable the next generation of autonomous vehicles precisely because it considers computation as an enabler and consumer of autonomous behavior. We report on the latest advances in this space showing how co-regulation exceeds results in event-triggered, self-triggered, and fixed-rate control strategies yielding more robustness and adaptivity to changing and uncertain conditions - a requirement for next-gen autonomous vehicles. We then describe a co-regulated decision making algorithm based on Markov Decision Processes showing how full consideration of computational resource allocation can increase decision-making capabilities in uncertain environments.

Users play an integral role in the performance of many robotic systems, and robotic systems must account for differences in users to improve collaborative performance. Much of the work in adapting to users has focused on designing teleoperation controllers that adjust to extrinsic user indicators such as force, or intent, but do not adjust to intrinsic user qualities. In contrast, the Human-Robot Interaction community has extensively studied intrinsic user qualities, but results may not rapidly be fed back into autonomy design. Here we provide foundational evidence for a new strategy that augments current shared control, and provide a mechanism to directly feed back results from the HRI community into autonomy design. Our evidence is based on a study examining the impact of the user quality “locus of control” on telepresence robot performance. Our results support our hypothesis that key user qualities can be inferred from human-robot interactions (such as through path deviation or time to completion) and that switching or adaptive autonomies might improve shared control performance.

We propose the application of state-space techniques to develop a novel coupled cyber-physical system (CPS) model and use feedback control to dynamically adjust CPS resource use and performance. We investigate the use of a gain scheduled discrete linear quadratic regulator controller and a forward-propagation Riccati-based controller to handle the discrete-time-varying system. We demonstrate the value of our approach by conducting a disturbance-rejection case study for a small satellite (CubeSat) application in which resources required for attitude control are adjusted in real-time to maximize availability for other computational tasks. We evaluate CPS performance through a set of metrics quantifying physical system error and control effort as well as cyber resource utilization and compare these with traditional fixed-rate optimal control strategies. Results indicate that our proposed coupled CPS model and controller can provide physical system performance similar to fixed-rate optimal control strategies but with less control effort and much less computational utilization.

Cyber-physical system (CPS) research aims to integrate physical and computational models in a manner that outperforms a system in which the two models are kept separate. CPSs can be generated by either folding properties of the physics-based system into a discrete modeling paradigm or vice versa. This paper studies the latter by abstracting execution rate of a real-time feedback control task into a continuous state-space form traditionally employed for physics-based systems. We propose coupling the two models in a linear systems framework and study the impact of this coupling applied to a single degree of freedom second-order oscillator as well as an unstable inverted pendulum, both regulated with an appropriately designed linear quadratic regulator (LQR). Our results illustrate the utility of the proposed abstraction and controller design as a means of coregulating cyber and physical states in real time.