No description available.

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.

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.

La génération de stratégies pour les solveurs en Satisfiabilité Modulo des Théories (SMT) nécessite des outils théoriques et pratiques qui permettent aux utilisateurs d’exercer un contrôle stratégique sur les aspects heuristiques fondamentaux des solveurs de SMT, tout en garantissant leur performance. Nous nous intéressons dans cette thèse au solveur Z3 , l’un des plus efficaces lors des compétitions SMT (SMT-COMP). Dans les solveurs SMT, la définition d’une stratégie repose sur un ensemble de composants et paramètres pouvant être agencés et configurés afin de guider la recherche d’une preuve de (in)satisfiabilité d’une instance donnée. Dans cette thèse, nous abordons ce défi en définissant un cadre pour la génération autonome de stratégies pour Z3, c’est-à-dire un algorithme qui permet de construire automatiquement des stratégies sans faire appel à des connaissances d’expertes. Ce cadre général utilise une approche évolutionnaire (programmation génétique), incluant un système à base de règles. Ces règles formalisent la modification de stratégies par des principes de réécriture, les algorithmes évolutionnaires servant de moteur pour les appliquer. Cette couche intermédiaire permettra d’appliquer n’importe quel algorithme ou opérateur sans qu’il soit nécessaire de modifier sa structure, afin d’introduire de nouvelles informations sur les stratégies. Des expérimentations sont menées sur les jeux classiques de la compétition SMT-COMP. / The Strategy Challenge in Satisfiability Modulo Theories (SMT) claims to build theoretical and practical tools allowing users to exert strategic control over core heuristic aspects of high-performance SMT solvers. In this work, we focus in Z3 Theorem Prover: one of the most efficient SMT solver according to the SMT Competition, SMT-COMP. In SMT solvers, the definition of a strategy relies on a set of tools that can be scheduled and configured in order to guide the search for a (un)satisfiability proof of a given instance. In this thesis, we address the Strategy Challenge in SMT defining a framework for the autonomous generation of strategies in Z3, i.e. a practical system to automatically generate SMT strategies without the use of expert knowledge. This framework is applied through an incremental evolutionary approach starting from basic algorithms to more complex genetic constructions. This framework formalise strategies modification as rewriting rules, where algorithms acts as enginess to apply them. This intermediate layer, will allow apply any algorithm or operator with no need to being structurally modified, in order to introduce new information in strategies. Validation is done through experiments on classic benchmarks of the SMT-COMP.