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

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors

Mobile Robots

The objective of this book is to cover advances of mobile robotics and related technologies applied for multi robot systems' design and development. Design of control system is a complex issue, requiring the application of information technologies to link the robots into a single network. Human robot interface becomes a demanding task, especially when we try to use sophisticated methods for brain signal processing. Generated electrophysiological signals can be used to command different devices, such as cars, wheelchair or even video games. A number of developments in navigation and path planning, including parallel programming, can be observed. Cooperative path planning, formation control of multi robotic agents, communication and distance measurement between agents are shown. Training of the mobile robot operators is very difficult task also because of several factors related to different task execution. The presented improvement is related to environment model generation based on autonomous mobile robot observations

Stochastic Bayesian Games for the Cybersecurity of Nuclear Power Plants

The goal of this research is to reduce the likelihood of successful attacks on nuclear power plants. Cyber-physical systems such as nuclear power plants consist of interconnected physical processes and computational resources. Because the cyber and physical worlds are integrated, vulnerabilities in both the cyber and physical domains can result in physical damage to the system. Nuclear power plants can be targeted by a variety of adversaries — each with a unique motivation and set of resources. To secure nuclear power plants and other cyber-physical systems, we require an approach to security that also accounts for the interactions of human decision-makers. This research uses a game-theoretic approach to nuclear cybersecurity. The cybersecurity of the plant can be viewed as a non-cooperative game between a defender and an attacker. The field of game theory provides a mathematical framework to analyze the interactions of the defender and attacker as both players seek to accomplish their objectives. In this research, a stochastic Bayesian game is used to optimize cybersecurity decision-making. A stochastic Bayesian game is a combination of a stochastic game and a Bayesian game. The stochastic elements of the game enable the consideration of uncertainty in the interactions of the attacker and defender. The Bayesian elements of the game enable the consideration of the uncertainty regarding the attacker's characteristics. This combination is useful for the analysis of nuclear power plant cybersecurity because it enables plant defenders to optimize their security decisions in the presence of uncertainty

ESTIMATION-BASED SOLUTIONS TO INCOMPLETE INFORMATION PURSUIT-EVASION GAMES

Differential games are a useful tool both for modeling conflict between autonomous systems and for synthesizing robust control solutions. The traditional study of games has assumed decision agents possess complete information about one another’s strategies and numerical weights. This dissertation relaxes this assumption. Instead, uncertainty in the opponent’s strategy is treated as a symptom of the inevitable gap between modeling assumptions and applications. By combining nonlinear estimation approaches with problem domain knowledge, procedures are developed for acting under uncertainty using established methods that are suitable for applications on embedded systems. The dissertation begins by using nonlinear estimation to account for parametric uncertainty in an opponent’s strategy. A solution is proposed for engagements in which both players use this approach simultaneously. This method is demonstrated on a numerical example of an orbital pursuit-evasion game, and the findings motivate additional developments. First, the solutions of the governing Riccati differential equations are approximated, using automatic differentiation to obtain high-degree Taylor series approximations. Second, constrained estimation is introduced to prevent estimator failures in near-singular engagements. Numerical conditions for nonsingularity are approximated using Chebyshev polynomial basis functions, and applied as constraints to a state estimate. Third and finally, multiple model estimation is suggested as a practical solution for time-critical engagements in which the form of the opponent’s strategy is uncertain. Deceptive opponent strategies are identified as a candidate approach to use against an adaptive player, and a procedure for designing such strategies is proposed. The new developments are demonstrated in a missile interception pursuit-evasion game in which the evader selects from a set of candidate strategies with unknown weights

Advances in Computer Science and Engineering

The book Advances in Computer Science and Engineering constitutes the revised selection of 23 chapters written by scientists and researchers from all over the world. The chapters cover topics in the scientific fields of Applied Computing Techniques, Innovations in Mechanical Engineering, Electrical Engineering and Applications and Advances in Applied Modeling

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp