The objective of this thesis is to develop a strategy that allows robots to safely execute open-loop motion patterns for pre-computed time durations when facing interruptions in communication. By computing the time horizon in which collisions with other robots are impossible, this method allows the robots to move safely despite having no updated information about the environment. As the complexity of multi-robot systems increase, communication failures in the form of packet losses, saturated network channels and hardware failures are inevitable. This thesis is motivated by the need to increase the robustness of operation in the face of such failures. The advantage of this strategy is that it prevents the jerky and unpredictable motion behaviour which often plague robotic systems experiencing communication issues. To compute the safe time horizon, the first step involves constructing reachable sets around the robots to determine the set of all positions that can be reached by the robot in a given amount of time. In order to avoid complications arising from the non-convexity of these reachable sets, analytical expressions for minimum area ellipses enclosing the reachable sets are obtained. By using a fast gradient descent based technique, intersections are computed between a robot’s trajectory and the reachable sets of other robots. This information is then used to compute the safe time horizon for each robot in real time. To this end, provable safety guarantees are formulated to ensure collision avoidance. This strategy has been verified in simulation as well as on a team of two-wheeled differential drive robots on a multi-robot testbed.

The concept of Public Participation comes from democratic theory. The core assumes that people are interested in public affairs and participate constructively. The consensus conference is developed to solve democratic problems. IT invites people without specialized knowledge to discuss controversial issues before they read related data. They set important problems in the domain and ask the experts in public. they debate controversial issues and make a decision. The youth consensus conference is the promise of President Chen in April. It provides youth the opportunity to participate social democracy. And we discuss the comment response adequately, conference satisfy, and conclusion reachable to make sure the difference between experts and participators. 61.1% participators thought comment response adequately is important and better. 55.6% participators thought that the conference satisfy is important and positive. The great conclusion is anticipated. 44.1% participator had different comment in conclusion reachable. Because about half people thought conclusion reachable was not easy to achieve. The experts had almost the same opinion in comment response and they believed the existence of sufficient comment reachable system. It makes everybody would say everything. A part of experts the topics influence consensus. The experts thought the conclusions was not possible adopt by the government. The point is the knowledge gained and the process of discussion.

<div>Reinforcement Learning (RL) is a data-driven technique which is finding increasing application in the development of controllers for sequential decision making problems. Their wide adoption can be attributed to the fact that the development of these controllers is independent of the</div><div>knowledge of the system and thus can be used even when the environment dynamics are unknown. Model-Based RL controllers explicitly model the system dynamics from the observed (training) data using a function approximator, followed by using a path planning algorithm to obtain the optimal control sequence. While these controllers have been proven to be successful in simulations, lack of strong safety guarantees in the presence of noise makes them ill-posed for deployment on hardware, specially in safety critical systems. The proposed work aims at bridging this gap by providing a verification framework to evaluate the safety guarantees for a Model-Based RL controller. Our method builds upon reachability analysis to determine if there is any action which can drive the system into a constrained (unsafe) region. Consequently, our method can provide a binary yes or no answer to whether all the initial set of states are (un)safe to propagate trajectories from in the presence of some bounded noise.</div>

Le traitement AVK concerne plus d’1% de la population française. Il représente un problème de santé publique majeur en raison de son importante iatrogénie. Les causes peuvent être notamment reliées à la complexité du parcours de soins des patients, à leurs erreurs, méconnaissances et incompréhensions. L’éducation thérapeutique (ETP) permettrait de contribuer à rendre ce patient autogestionnaire de ses propres risques. Cette recherche qualitative vise à proposer un modèle d’ETP en en identifiant les compétences d’auto-soins et d’adaptation à la maladie et en en précisant le programme, les méthodes pédagogiques et d’évaluation. Le protocole de recherche comporte une revue de la littérature sur les connaissances des patients, des entretiens de type semi-directifs de patients traités par AVK et de soignants-éducateurs, et enfin des entretiens avec un groupe d’experts. Trente-six entretiens associés à une revue extensive de la littérature ont permis d’élaborer un référentiel de huit compétences. Vingt-et-un objectifs pédagogiques découlent de ces compétences. Les principales difficultés des patients concernaient la mise en lien des concepts constitutifs du paradigme du traitement par AVK. Les huit compétences du référentiel correspondent à la gestion intelligible et sans danger d’un traitement anticoagulant. Les différents types de soignants-éducateurs envisagés pour ce modèle d’ETP se retrouvent dans le parcours de soins habituel des patients. Notre modèle pédagogique se veut applicable à différents contextes de soins. Les propositions tentent de répondre à la problématique de l’intelligibilité du traitement AVK et de rendre accessible aux patients l’éducation thérapeutique. / The VKA treatment concerns more than 1% of the french population. It represents a major public health problem beacause of its consirable drug related iatrogny....

Gausybė sudėtingų realaus laiko sistemų turi būti specifikuotos įvertinant visas galimas situacijas. Specifikacijos teisingumas reiškia, kad sistema užduotomis sąlygomis pasieks norimą rezultatą. Norėdami užtikrinti, kad aprašyta specifikacija yra teisinga, reikia atlikti sistemos verifikavimą ir validavimą. Tradiciniai verifikavimo metodai neužtikrina pilno sistemos patikrinimo. Pagrindinis jų trūkumas yra tai, kad jos negali įvertinti laikinių charakteristikų. Per keletą paskutinių metų, buvo sukurti nauji metodai, kuriuose įvykių įvykimo laikas priklauso intervalams. Šiame darbe šie metodai buvo patobulinti, kad pilnai aprašytų realaus laiko sistemų veiksenas. Šiame darbe pateikiamas pasiekiamų būsenų medžio sudarymo algoritmas, kai sistemos pabaigos laikų momentų aibė priklauso bet kokiam intervalui - griežtam, negriežtam, griežtam iš kairės arba dešinės. / Complexity and variety of systems that are working in real time mode need to be specified regarding all behavior conditions. The correctness of the specification, determines whether implemented system will supply conditions that were set. To ensure that specification of the described real-time system is correct, we have to do verification and validation of the specification. Traditional verification methods do not assure full real time system inspection. The main drawback, talking about them, is impossibility of system evaluation according time. In past few years, new methods were implemented, whereat real time system events befall in time interval. In this paper, these methods were improved to fully specify real time systems behaviour. Reachable states graph and its generating algorithms are described here, wherein real time system events befall in any type of time interval – inclusive, exclusive in left, right or both sides.

Uncertainties and constraints are present in most control systems. For example, robot motion planning and building climate regulation can be modeled as uncertain constrained systems. In this thesis, we develop mathematical and computational tools to analyze and synthesize controllers for such systems. As our first contribution, we characterize when a set is a probabilistic controlled invariant set and we develop tools to compute such sets. A probabilistic controlled invariantset is a set within which the controller is able to keep the system state with a certainprobability. It is a natural complement to the existing notion of robust controlled invariantsets. We provide iterative algorithms to compute a probabilistic controlled invariantset within a given set based on stochastic backward reachability. We prove that thesealgorithms are computationally tractable and converge in a finite number of iterations. The computational tools are demonstrated on examples of motion planning, climate regulation, and model predictive control. As our second contribution, we address the control design problem for uncertain constrained systems with aperiodic sensing and actuation. Firstly, we propose a stochastic self-triggered model predictive control algorithm for linear systems subject to exogenous disturbances and probabilistic constraints. We prove that probabilistic constraint satisfaction, recursive feasibility, and closed-loop stability can be guaranteed. The control algorithm is computationally tractable as we are able to reformulate the problem into a quadratic program. Secondly, we develop a robust self-triggered control algorithm for time-varying and uncertain systems with constraints based on reachability analysis. In the particular case when there is no uncertainty, the design leads to a control system requiring minimum number of samples over finite time horizon. Furthermore, when the plant is linear and the constraints are polyhedral, we prove that the previous algorithms can be reformulated as mixed integer linear programs. The method is applied to a motion planning problem with temporal constraints. / <p>QC 20181016</p>

Threat assessment and reliable motion-prediction of surrounding vehicles are some of the major challenges encountered in autonomous vehicles' safe decision-making. Predicting a threat in advance can give an autonomous vehicle enough time to avoid crashes or near crash situations. Most vehicles on roads are human-driven, making it challenging to predict their intentions and movements due to inherent uncertainty in their behaviors. Moreover, different driver behaviors pose different kinds of threats. Various driver behavior predictive models have been proposed in the literature for motion prediction. However, these models cannot be trusted entirely due to the human drivers' highly uncertain nature. This thesis proposes a novel trust-based driver behavior prediction and stochastic reachable set threat assessment methodology for various dangerous situations on the road. This trust-based methodology allows autonomous vehicles to quantify the degree of trust in their predictions to generate the probabilistically safest trajectory. This approach can be instrumental in the near-crash scenarios where no collision-free trajectory exists. Three different driving behaviors are considered: Normal, Aggressive, and Drowsy. Hidden Markov Models are used for driver behavior prediction. A "trust" in the detected driver is established by combining four driving features: Longitudinal acceleration, lateral acceleration, lane deviation, and velocity. A stochastic reachable set-based approach is used to model these three different driving behaviors. Two measures of threat are proposed: Current Threat and Short Term Prediction Threat which quantify present and the future probability of a crash. The proposed threat assessment methodology resulted in a lower rate of false positives and negatives. This probabilistic threat assessment methodology is used to address the second challenge in autonomous vehicle safety: crash avoidance decision-making. This thesis presents a fast, proactive decision-making methodology based on Stochastic Model Predictive Control (SMPC). A proactive decision-making approach exploits the surrounding human-driven vehicles' intent to assess the future threat, which helps generate a safe trajectory in advance, unlike reactive decision-making approaches that do not account for the surrounding vehicles' future intent. The crash avoidance problem is formulated as a chance-constrained optimization problem to account for uncertainty in the surrounding vehicle's motion. These chance-constraints always ensure a minimum probabilistic safety of the autonomous vehicle by keeping the probability of crash below a predefined risk parameter. This thesis proposes a tractable and deterministic reformulation of these chance-constraints using convex hull formulation for a fast real-time implementation. The controller's performance is studied for different risk parameters used in the chance-constraint formulation. Simulation results show that the proposed control methodology can avoid crashes in most hazardous situations on the road. / Master of Science / Unexpected road situations frequently arise on the roads which leads to crashes. In an NHTSA study, it was reported that around 94% of car crashes could be attributed to driver errors and misjudgments. This could be attributed to drinking and driving, fatigue, or reckless driving on the roads. Full self-driving cars can significantly reduce the frequency of such accidents. Testing of self-driving cars has recently begun on certain roads, and it is estimated that one in ten cars will be self-driving by the year 2030. This means that these self-driving cars will need to operate in human-driven environments and interact with human-driven vehicles. Therefore, it is crucial for autonomous vehicles to understand the way humans drive on the road to avoid collisions and interact safely with human-driven vehicles on the road. Detecting a threat in advance and generating a safe trajectory for crash avoidance are some of the major challenges faced by autonomous vehicles. We have proposed a reliable decision-making algorithm for crash avoidance in autonomous vehicles. Our framework addresses two core challenges encountered in crash avoidance decision-making in autonomous vehicles: 1. The outside challenge: Reliable motion prediction of surrounding vehicles to continuously assess the threat to the autonomous vehicle. 2. The inside challenge: Generating a safe trajectory for the autonomous vehicle in case of future predicted threat. The outside challenge is to predict the motion of surrounding vehicles. This requires building a reliable model through which future evolution of their position states can be predicted. Building these models is not trivial, as the surrounding vehicles' motion depends on human driver intentions and behaviors, which are highly uncertain. Various driver behavior predictive models have been proposed in the literature. However, most do not quantify trust in their predictions. We have proposed a trust-based driver behavior prediction method which combines all sensor measurements to output the probability (trust value) of a certain driver being "drowsy", "aggressive", or "normal". This method allows the autonomous vehicle to choose how much to trust a particular prediction. Once a picture is painted of surrounding vehicles, we can generate safe trajectories in advance – the inside challenge. Most existing approaches use stochastic optimal control methods, which are computationally expensive and impractical for fast real-time decision-making in crash scenarios. We have proposed a fast, proactive decision-making algorithm to generate crash avoidance trajectories based on Stochastic Model Predictive Control (SMPC). We reformulate the SMPC probabilistic constraints as deterministic constraints using convex hull formulation, allowing for faster real-time implementation. This deterministic SMPC implementation ensures in real-time that the vehicle maintains a minimum probabilistic safety.

This thesis is concerned with the Pursuit-and-Evasion (PE) problem where the pursuer aims to minimize the time to capture the evader while the evader tries to prevent capture. In the problem, the evader has two advantages: a higher manoeuvrability and that the pursuer is uncertain about the evader??s state. Cooperation among multiple pursuer vehicles can thus be used to overcome the evader??s advantages. The focus here is on the formulation and development of frameworks and algorithms for cooperation amongst pursuers, aiming at feasible implementation on real and autonomous vehicles. The thesis is split into Parts I and II. Part I considers the problem of capturing an evader of higher manoeuvrability in a deterministic PE game. The approach is the employment of Forward Reachable Set (FRS) analysis in the pursuers?? control. The analysis considers the coverage of the evader??s FRS, which is the set of reachable states at a future time, with the pursuer??s FRS and assumes that the chance of capturing the evader is dependent on the degree of the coverage. Using the union of multiple pursuers?? FRSs intuitively leads to more evader FRS coverage and this forms the mechanism of cooperation. A framework for cooperative control based on the FRS coverage, or FRS-based control, is proposed. Two control algorithms were developed within this framework. Part II additionally introduces the problem of evader state uncertainty due to noise and limited field-of-view of the pursuers?? sensors. A search-and-capture (SAC) problem is the result and a hybrid architecture, which includes multi-sensor estimation using the Particle Filter as well as FRS-based control, is proposed to accomplish the SAC task. The two control algorithms in Part I were tested in simulations against an optimal guidance algorithm. The results show that both algorithms yield a better performance in terms of time and miss distance. The results in Part II demonstrate the effectiveness of the hybrid architecture for the SAC task. The proposed frameworks and algorithms provide insights for the development of effective and more efficient control of pursuer vehicles and can be useful in the practical applications such as defence systems and civil law enforcement.

Darbe nagrinėjamas realiojo laiko sistemų, specifikuotų agregatiniu metodu, verifikavimo uždavinys. Sprendžiant šį uždavinį, naudojama pasiekiamų būsenų grafo sudarymo metodika, leidžianti įvertinti laiko intervalus, kuriais įvyksta sistemoje apibrėžti įvykiai. Darbe nagrinėjami pasiekiamų būsenų grafo sudarymo algoritmai ir pateikta prototipinėse programose naudojama duomenų struktūra. Suformuluoti ir įrodyti teiginiai apie pasiekiamų būsenų grafo sudarymo algoritmo sudėtingumą (maksimalaus vienos būsenos galimų perėjimų skaičiaus; grafo viršūnių skaičiaus augimo priklausomybės nuo įvykių skaičiaus; maksimalaus įvykių skaičiaus elgsenoje laiko intervale; maksimalaus viršūnių skaičiaus laiko intervale.) Pateikta trijų testinių sistemų grafų automatizuoto sudarymo analizė. Parodyta, kad Simplekso optimizavimo procedūra laiko momentų palyginimui gali būti naudojama tik atskirais atvejais. / The work deals with a verification task of real time system specified by aggregate method. While solving the task, a technique for creation a reachable state graph is used. The technique permits to evaluate intervals of time when the defined system events occur. Reachable state graph creation algorithms are analysed in the work. A data structure used in prototype software is presented in the work too. Assertions about a complexity of algorithm for reachable state graph creation are formulated and proved. These assertions concern maximum number of transitions from single state, dependency of number of graph verteles growth on a number of events, maximum number of events in a behaviour during time interval, and maximum number of vertexes during time interval. Analysis of automated creation of graphs for three test systems is presented. It is shown that Simplex optimisation procedure for comparison of time intervals can be used only in certain cases.

The problem of analyzing concurrent systems has been investigated by many researchers, and several solutions have been proposed. Among the proposed techniques, reachability analysis—systematic enumeration of reachable states in a finite-state model—is attractive because it is conceptually simple and relatively straightforward to automate and can be used in conjunction with model-checking procedures to check for application-specific as well as general properties. The system validation problem considered here is the problem of verifying that the original specification is itself logically consistent. If, for instance, the specification has a design error, an implementation is expected to pass a conformance test if it contains the same error. A validation for the logical consistency of the system, however, must reveal the design error. An automated analysis of all reachable states in a distributed system can be used to trace obscure logical errors that would be very hard to find manually. This type of validation is traditionally performed by the symbolic execution of a finite state machine model of the system studied. The author presents an overview of the existing validation techniques and methods. Specified and analyzed systems are presented as reachable state graph. The implementation of the aggregate specifications validation system is also presented.