An overview of population-based algorithms for multi-objective optimisation

In this work we present an overview of the most prominent population-based algorithms and the methodologies used to extend them to multiple objective problems. Although not exact in the mathematical sense, it has long been recognised that population-based multi-objective optimisation techniques for real-world applications are immensely valuable and versatile. These techniques are usually employed when exact optimisation methods are not easily applicable or simply when, due to sheer complexity, such techniques could potentially be very costly. Another advantage is that since a population of decision vectors is considered in each generation these algorithms are implicitly parallelisable and can generate an approximation of the entire Pareto front at each iteration. A critique of their capabilities is also provided

Liger : a cross-platform open-source integrated optimization and decision-making environment

Real-world optimization problems involving multiple conflicting objectives are commonly best solved using multi-objective optimization as this provides decision-makers with a family of trade-off solutions. However, the complexity of using multi-objective optimization algorithms often impedes the optimization process. Knowing which optimization algorithm is the most suitable for the given problem, or even which setup parameters to pick, requires someone to be an optimization specialist. The lack of supporting software that is readily available, easy to use and transparent can lead to increased design times and increased cost. To address these challenges, Liger is presented. Liger has been designed for ease of use in industry by non-specialists in optimization. The user interacts with Liger via a visual programming language to create an optimization workflow, enabling the user to solve an optimization problem. Liger contains a novel optimization library known as Tigon. The library utilizes the concept of design patterns to enable the composition of optimization algorithms by making use of simple reusable operator nodes. The library offers a varied range of multi-objective evolutionary algorithms which cover different paradigms in evolutionary computation; and supports a wide variety of problem types, including support for using more than one programming language at a time to implement the optimization model. Additionally, Liger functionality can be easily extended by plugins that provide access to state-of-the-art visualization tools and are responsible for managing the graphical user interface. Lastly, new user-driven interactive capabilities are shown to facilitate the decision-making process and are demonstrated on a control engineering optimization problem

Control of Outdoor Robots at Higher Speeds on Challenging Terrain

This thesis studies the motion control of wheeled mobile robots. Its focus is set on high speed control on challenging terrain. Additionally, it deals with the general problem of path following, as well as path planning and obstacle avoidance in difficult conditions. First, it proposes a heuristic longitudinal control for any wheeled mobile robot, and evaluates it on different kinematic configurations and in different conditions, including laboratory experiments and participation in a robotic competition. Being the focus of the thesis, high speed control on uneven terrain is thoroughly studied, and a novel control law is proposed, based on a new model representation of skid-steered vehicles, and comprising of nonlinear lateral and longitudinal control. The lateral control part is based on the Lyapunov theory, and the convergence of the vehicle to the geometric reference path is proven. The longitudinal control is designed for high speeds, taking actuator saturation and the vehicle properties into account. The complete solution is experimentally tested on two different vehicles on several different terrain types, reaching the speeds of ca. 6 m/s, and compared against two state-of-the-art algorithms. Furthermore, a novel path planning and obstacle avoidance system is proposed, together with an extension of the proposed high speed control, which builds up a navigation system capable of autonomous outdoor person following. This system is experimentally compared against two classical obstacle avoidance methods, and evaluated by following a human jogger in outdoor environments, with both static and dynamic obstacles. All the proposed methods, together with various different state-of-the-art control approaches, are unified into one framework. The proposed framework can be used to control any wheeled mobile robot, both indoors and outdoors, at low or high speeds, avoiding all the obstacles on the way. The entire work is released as open-source software

Modeling Robotic Systems with Activity Flow Graphs

Autonomous robotic systems are becoming increasingly common in our society, with research efforts towards automated goods transportation, service robots and autonomous cars. These complex systems have to solve many different problems in order to function robustly. Two especially important areas of interest are perception and high level control. Intelligent systems have to perceive their surroundings in order to facilitate autonomy. With an understanding of the environment, they then can make their own decisions based on high level control policies defined by the developers. Robotic systems differ drastically in their sensory capabilities, their computational power, and their designated tasks. When developing algorithms, however, we need to have a common modeling framework that enables us to generalize and re-use existing solutions. A modular approach, which is coherent across different platforms, also allows faster prototyping of new systems. In this dissertation we develop a modeling framework based on data flow that achieves this goal. We first extend the existing Synchronous Data Flow (SDF) model and combine it with reactive programming ideas and finite-state machines. Together, these existing frameworks enable us to model many aspects of complex robotic systems. We apply this model to a robot in a warehouse scenario to demonstrate the viability of the approach. Using three disjoint formalisms to model a robotic system has many downsides. In a first unification step we merge SDF and reactive programming into Hybrid Flow Graphs (HFGs), where we explicitly model synchronous and asynchronous data flow. We then apply the HFG model to the perception system of an autonomous transportation robot. In a last step, we eliminate the need for separate finite-state machines by introducing the concept of activity into the data flow. We therefore unify the different aspects into a single and coherent framework which we call Activity Flow Graphs (AFGs). The flow of activity enables us to model high level state directly in the data flow graph. The result is a single computation graph that can express both perception and high level control aspects of any robotic system. We then demonstrate this with multiple high level robotic system models. Finally, we make use of the uniform AFG model to provide a single graphical user interface that allows a developer to rapidly prototype complete robotic systems. Since all aspects of a robot can be implemented using the same theoretical framework, there is no need to switch between different paradigms. The user interface is designed to give immediate feedback, which speeds up prototyping, testing and evaluation, as well as debugging when working with real robots.Autonome Roboter werden zunehmend zu einem wichtigen Bestandteil unserer Gesellschaft, in Bereichen wie dem automatisierten Gütertransport, in der Servicerobotik und bei autonomen Automobilen. Diese komplexen Systeme müssen viele Problem lösen, um robust zu funktionieren. Zwei sehr wichtige Anwendungsfelder sind die Umgebungswahrnehmung und die Ablaufplanung. Intelligente Systeme müssen ihre Umgebung wahrnehmen, um autonom agieren zu können. Mit einem Verständnis der Umwelt können sie Entscheidungen treffen, welche auf abstrakten Richtlinien der Entwickler basieren. Verschiedene Roboter weichen stark in ihren sensorischen Fähigkeiten, in ihrer Rechenleistung und in ihren zu lösenden Aufgaben voneinander ab. Bei der Entwicklung von Algorithmen wird jedoch ein einheitliches Modellierungssystem benötigt, welches die Wiederverwendung von existierenden Lösungen erlaubt. Ein modulares System, welches über mehrere Plattformen hinweg genutzt werden kann, ermöglicht eine schnellere Entwicklung von neuen Systemen. In dieser Dissertation wird ein auf Datenfluss basierendes Modell entwickelt, welches diese Anforderungen erfüllt. Zuerst wird das existierende Synchronous Data Flow (SDF) Modell erweitert und mit Elementen von reaktiver Programmierung und endlichen Zustandsautomaten kombiniert. Zusammen können so viele Aspekte von Robotern modelliert werden. Das Modell wird auf einen Roboter in einem Warenhausszenario angewandt, um den Ansatz zu validieren. Drei verschiedene Formalismen zur Modellierung von Robotern zu verwenden hat viele Nachteile. In einem ersten Vereinigungsschritt werden SDF und reaktive Programmierung zu hybriden Flussgraphen (HFG) kombiniert, bei denen synchroner und asynchroner Datenfluss explizit modelliert werden. Dann wird das HFG-Modell auf die Wahrnehmungsmodule eines autonomen Transportsystems angewandt. Anschließend wird der Bedarf eines Zustandsautomaten beseitigt, indem das Konzept der Aktivität in den Datenfluss eingeführt wird. Dadurch werden alle Aspekte in einem einzigen, schlüssigen System vereinigt, welches Aktivitätsflussgraph (AFG) genannt wird. Der Aktivitätsfluss ermöglicht es, den höheren Systemzustand direkt im Datenflussgraphen zu modellieren. Als Ergebnis erhalten wir einen einzigen Berechnungsgraphen, der sowohl zur Beschreibung der Umgebungswahrnehmung als auch zur Kontrolle der höheren Abläufe benutzt werden kann. Dies wird anhand mehrerer Robotersysteme demonstriert. Eine graphische Benutzerschnittstelle wird bereitgestellt, welche von dem einheitlichen Modell Gebrauch macht, um ein schnelles Prototyping von Robotern zu ermöglichen. Da alle Aspekte mit demselben System modelliert werden, muss nicht zwischen verschiedenen Paradigmen gewechselt werden. Die Nutzerschnittstelle erleichtert Entwicklung, Test und Validierung von Algorithmen sowie das Auffinden von Fehlern bei echten Robotern

OptPlatform: metaheuristic optimisation framework for solving complex real-world problems

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonWe optimise daily, whether that is planning a round trip that visits the most attractions within a given holiday budget or just taking a train instead of driving a car in a rush hour. Many problems, just like these, are solved by individuals as part of our daily schedule, and they are effortless and straightforward. If we now scale that to many individuals with many different schedules, like a school timetable, we get to a point where it is just not feasible or practical to solve by hand. In such instances, optimisation methods are used to obtain an optimal solution. In this thesis, a practical approach to optimisation has been taken by developing an optimisation platform with all the necessary tools to be used by practitioners who are not necessarily familiar with the subject of optimisation. First, a high-performance metaheuristic optimisation framework (MOF) called OptPlatform is implemented, and the versatility and performance are evaluated across multiple benchmarks and real-world optimisation problems. Results show that, compared to competing MOFs, the OptPlatform outperforms in both the solution quality and computation time. Second, the most suitable hardware platform for OptPlatform is determined by an in-depth analysis of Ant Colony Optimisation scaling across CPU, GPU and enterprise Xeon Phi. Contrary to the common benchmark problems used in the literature, the supply chain problem solved could not scale on GPUs. Third, a variety of metaheuristics are implemented into OptPlatform. Including, a new metaheuristic based on Imperialist Competitive Algorithm (ICA), called ICA with Independence and Constrained Assimilation (ICAwICA) is proposed. The ICAwICA was compared against two different types of benchmark problems, and results show the versatile application of the algorithm, matching and in some cases outperforming the custom-tuned approaches. Finally, essential MOF features like automatic algorithm selection and tuning, lacking on existing frameworks, are implemented in OptPlatform. Two novel approaches are proposed and compared to existing methods. Results indicate the superiority of the implemented tuning algorithms within constrained tuning budget environment