Middleware and Architecture for Advanced Applications of Cyber-physical Systems

In this thesis, we address issues related to middleware, architecture and applications of cyber-physical systems. The first problem we address is the cross-layer design of cyber-physical systems to cope with interactions between the cyber layer and the physical layer in a dynamic environment. We propose a bi-directional middleware that allows the optimal utilization of the common resources for the benefit of either or both the layers in order to obtain overall system performance. The case study of network connectivity preservation in a vehicular formation illustrates how this approach can be applied to a particular situation where the network connectivity drives the application layer. Next we address another aspect of cross-layer impact: the problem that arises when network performance, in this case delay performance, affects control system performance. We propose a two-pronged approach involving a flexible adaptive model identification algorithm with outlier rejection, which in turn uses an adaptive system model to detect and reject outliers, thus shielding the estimation algorithm and thereby improving reliability. We experimentally demonstrate that the outlier rejection approach which intercepts and filters the data, combined with simultaneous model adaptation, can result in improved performance of Model Predictive Control in the vehicular testbed. Then we turn to two advanced applications of cyber-physical systems. First, we address the problem of security of cyber-physical systems. We consider the context of an intelligent transportation system in which a malicious sensor node manipulates the position data of one of the autonomous cars to deviate from a safe trajectory and collide with other cars. In order to secure the safety of such systems where sensor measurements are compromised, we employ the procedure of “dynamic watermarking”. This procedure enables an honest node in the control loop to detect the existence of a malicious node within the feedback loop. We demonstrate in the testbed that dynamic watermarking can indeed protect cars against collisions even in the presence of sensor attacks. The second application of cyber-physical systems that we consider is cyber-manufacturing which is an origami-type laser-based custom manufacturing machine employing folding and cutting of sheet material to manufacture 3D objects. We have developed such a system for use in a laser-based autonomous custom manufacturing machine equipped with real-time sensing and control. The basic elements in the architecture are a laser processing machine, a sensing system to estimate the state of the workpiece, a control system determining control inputs for a laser system based on the estimated data, a robotic arm manipulating the workpiece in the work space, and middleware supporting the communication among the systems. We demonstrate automated 3D laser cutting and bending to fabricate a 3D product as an experimental result. Lastly, we address the problem of traffic management of an unmanned aerial system. In an effort to improve the performance of the traffic management for unmanned aircrafts, we propose a probability-based collision resolution algorithm. The proposed algorithm analyzes the planned trajectories to calculate their collision probabilities, and modifies individual drone starting times to reduce the probability of collision, while attempting to preserve high performance. Our simulation results demonstrate that the proposed algorithm improves the performance of the drone traffic management by guaranteeing high safety with low modification of the starting times

Middleware and Architecture for Advanced Applications of Cyber-physical Systems

In this thesis, we address issues related to middleware, architecture and applications of cyber-physical systems. The first problem we address is the cross-layer design of cyber-physical systems to cope with interactions between the cyber layer and the physical layer in a dynamic environment. We propose a bi-directional middleware that allows the optimal utilization of the common resources for the benefit of either or both the layers in order to obtain overall system performance. The case study of network connectivity preservation in a vehicular formation illustrates how this approach can be applied to a particular situation where the network connectivity drives the application layer. Next we address another aspect of cross-layer impact: the problem that arises when network performance, in this case delay performance, affects control system performance. We propose a two-pronged approach involving a flexible adaptive model identification algorithm with outlier rejection, which in turn uses an adaptive system model to detect and reject outliers, thus shielding the estimation algorithm and thereby improving reliability. We experimentally demonstrate that the outlier rejection approach which intercepts and filters the data, combined with simultaneous model adaptation, can result in improved performance of Model Predictive Control in the vehicular testbed. Then we turn to two advanced applications of cyber-physical systems. First, we address the problem of security of cyber-physical systems. We consider the context of an intelligent transportation system in which a malicious sensor node manipulates the position data of one of the autonomous cars to deviate from a safe trajectory and collide with other cars. In order to secure the safety of such systems where sensor measurements are compromised, we employ the procedure of “dynamic watermarking”. This procedure enables an honest node in the control loop to detect the existence of a malicious node within the feedback loop. We demonstrate in the testbed that dynamic watermarking can indeed protect cars against collisions even in the presence of sensor attacks. The second application of cyber-physical systems that we consider is cyber-manufacturing which is an origami-type laser-based custom manufacturing machine employing folding and cutting of sheet material to manufacture 3D objects. We have developed such a system for use in a laser-based autonomous custom manufacturing machine equipped with real-time sensing and control. The basic elements in the architecture are a laser processing machine, a sensing system to estimate the state of the workpiece, a control system determining control inputs for a laser system based on the estimated data, a robotic arm manipulating the workpiece in the work space, and middleware supporting the communication among the systems. We demonstrate automated 3D laser cutting and bending to fabricate a 3D product as an experimental result. Lastly, we address the problem of traffic management of an unmanned aerial system. In an effort to improve the performance of the traffic management for unmanned aircrafts, we propose a probability-based collision resolution algorithm. The proposed algorithm analyzes the planned trajectories to calculate their collision probabilities, and modifies individual drone starting times to reduce the probability of collision, while attempting to preserve high performance. Our simulation results demonstrate that the proposed algorithm improves the performance of the drone traffic management by guaranteeing high safety with low modification of the starting times

Cyberphysical Constructs and Concepts for Fully Automated Networked Vehicles

Human lives are at stake in networked systems of automated vehicles. Drawing from mature domains where life/safety critical cyberphysical systems have already been deployed as well as from various scientific disciplines, we introduce the SPEC (Safety, Privacy, Efficiency, Cybersecurity) problem which arises in self-organizing and self-healing networks of fully automated terrestrial vehicles, and CMX functionalities intended for vehicular onboard systems. CM stands for Coordinated Mobility, X stands for S, P, E and C. The CMX framework encompasses cyberphysical constructs (cells, cohorts) endowed with proven properties, onboard proactive security modules, unfalsifiable cyberphysical levels, protocols and distributed algorithms for timed-bounded inter-vehicular communications, reliable message dissemination, trusted explicit agreements/coordination, and privacy preserving options that insulate passengers from illegitimate internal cyber-surveillance and external eavesdropping and tracking. We establish inter alia that safety and privacy can be obtained jointly, by design. The focus of this report is on SE properties. Notably, we show how to achieve theoretical absolute safety (0 fatalities and 0 severe injuries in rear-end collisions and pileups) and highest efficiency (smallest safe inter-vehicular gaps) jointly, by design, in spontaneous cohorts of vehicles. Results conveyed in this report shall open new opportunities for innovative research and development of high societal impact.Les vies humaines sont en jeu dans les réseaux de véhicules automatisés, à l’instar de domaines matures où des systèmes critiques en matière de sécurité-innocuité ont déjà été déployés. Les connaissances acquises dans ces domaines ainsi que dans diverses disciplines scientifiques permettent de définir le problème SPEC (Safety, Privacy, Efficiency, Cybersecurity) qui se pose dans les réseaux auto-organisés et auto-réparateurs de véhicules terrestres à conduite entièrement automatisée. On introduit CMX, un ensemble de fonctionnalités destinées aux systèmes bord. CM est l’abréviation de Coordinated Mobility, et X signifie S, P, E et C. L’ensemble CMX repose sur des constructions cyberphysiques (cellules, cohortes) dotées de propriétés prouvées, les concepts de module de sécurité proactif et de niveaux cyberphysiques infalsifiables, des protocoles et des algorithmes distribués pour communications inter-véhiculaires en temps borné, dissémination fiable de messages, coordination et accords explicites dignes de confiance, ainsi que sur des options de protection de la vie privée qui permettent aux passagers d’interdire la cyber-surveillance illégitime interne et externe (écoutes radio et pistage des trajets). On établit qu’il est possible de garantir conjointement sécurité-innocuité (safety) et respect de la vie privée (privacy), par conception. Ce rapport est consacré aux propriétés SE. En particulier, on montre comment obtenir la sécurité-innocuité absolue théorique (taux nul de mortalité et de graves blessures en cas de collisions longitudinales) et maximiser l’efficacité (espaces inter-véhiculaires minimaux) conjointement, par conception, dans les cohortes spontanées de véhicules. Les résultats contenus dans ce rapport devraient ouvrir de nouvelles perspectives de recherche et développement à fort impact sociétal

Game-Theoretic Safety Assurance for Human-Centered Robotic Systems

In order for autonomous systems like robots, drones, and self-driving cars to be reliably introduced into our society, they must have the ability to actively account for safety during their operation. While safety analysis has traditionally been conducted offline for controlled environments like cages on factory floors, the much higher complexity of open, human-populated spaces like our homes, cities, and roads makes it unviable to rely on common design-time assumptions, since these may be violated once the system is deployed. Instead, the next generation of robotic technologies will need to reason about safety online, constructing high-confidence assurances informed by ongoing observations of the environment and other agents, in spite of models of them being necessarily fallible.This dissertation aims to lay down the necessary foundations to enable autonomous systems to ensure their own safety in complex, changing, and uncertain environments, by explicitly reasoning about the gap between their models and the real world. It first introduces a suite of novel robust optimal control formulations and algorithmic tools that permit tractable safety analysis in time-varying, multi-agent systems, as well as safe real-time robotic navigation in partially unknown environments; these approaches are demonstrated on large-scale unmanned air traffic simulation and physical quadrotor platforms. After this, it draws on Bayesian machine learning methods to translate model-based guarantees into high-confidence assurances, monitoring the reliability of predictive models in light of changing evidence about the physical system and surrounding agents. This principle is first applied to a general safety framework allowing the use of learning-based control (e.g. reinforcement learning) for safety-critical robotic systems such as drones, and then combined with insights from cognitive science and dynamic game theory to enable safe human-centered navigation and interaction; these techniques are showcased on physical quadrotors—flying in unmodeled wind and among human pedestrians—and simulated highway driving. The dissertation ends with a discussion of challenges and opportunities ahead, including the bridging of safety analysis and reinforcement learning and the need to ``close the loop'' around learning and adaptation in order to deploy increasingly advanced autonomous systems with confidence