Evolutionary Networks for Multi-Behavioural Robot Control : A thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Computer Science Massey University, Albany, New Zealand

Artificial Intelligence can be applied to a wide variety of real world problems, with varying levels of complexity; nonetheless, real world problems often demand for capabilities that are difficult, if not impossible to achieve using a single Artificial Intelligence algorithm. This challenge gave rise to the development of hybrid systems that put together a combination of complementary algorithms. Hybrid approaches come at a cost however, as they introduce additional complications for the developer, such as how the algorithms should interact and when the independent algorithms should be executed. This research introduces a new algorithm called Cascading Genetic Network Programming (CGNP), which contains significant changes to the original Genetic Network Programming. This new algorithm has the facility to include any Artificial Intelligence algorithm into its directed graph network, as either a judgement or processing node. CGNP introduces a novel ability for a scalable multiple layer network, of independent instances of the CGNP algorithm itself. This facilitates problem subdivision, independent optimisation of these underlying layers and the ability to develop varying levels of complexity, from individual motor control to high level dynamic role allocation systems. Mechanisms are incorporated to prevent the child networks from executing beyond their requirement, allowing the parent to maintain control. The ability to optimise any data within each node is added, allowing for general purpose node development and therefore allowing node reuse in a wide variety of applications without modification. The abilities of the Cascaded Genetic Network Programming algorithm are demonstrated and proved through the development of a multi-behavioural robot soccer goal keeper, as a testbed where an individual Artificial Intelligence system may not be sufficient. The overall role is subdivided into three components and individually optimised which allow the robot to pursue a target object or location, rotate towards a target and provide basic functionality for defending a goal. These three components are then used in a higher level network as independent nodes, to solve the overall multi- behavioural goal keeper. Experiments show that the resulting controller defends the goal with a success rate of 91%, after 12 hours training using a population of 400 and 60 generations

An investigation of various controller designs for multi-link robotic system (Robogymnast)

An approach to controlling the three-link Robogymnast robotic gymnast and assessing stability is proposed and examined. In the study, a conventionally configured linear quadratic regulator is applied and compared with a fuzzy logic linear quadratic regulator hybrid approach for stabilising the Robogymnast. The Robogymnast is designed to replicate the movement of a human as they hang with both hands holding the high bar and then work to wing up into a handstand, still gripping the bar. The system, therefore has a securely attached link between the hand element and the ‘high bar’, which is mounted on ball bearings and can rotate freely. Moreover, in the study, a mathematical model for the system is linearised, investigating the means of determining the state space in the system by applying Lagrange’s equation. The fuzzy logic linear quadratic regulator controller is used to identify how far the system responses stabilise when it is implemented. This paper investigates factors affecting the control of swing-up in the underactuated three-link Robogymnast. Moreover, a system simulation using MATLAB Simulink is conducted to show the impact of factors including overshoot, rising, and settling time. The principal objective of the study lies in investigating how a linear quadratic regulator or fuzzy logic controller with a linear quadratic regulator (FLQR) can be applied to the Robogymnast, and to assess system behaviour under five scenarios, namely the original value, this value plus or minus ±25%, and plus or minus ±50%. In order to further assess the performance of the controllers used, a comparison is made between the outcomes found here and findings in the recent literature with fuzzy linear quadratic regulator controllers

Visual Feedback Stabilisation of a Cart Inverted Pendulum A

Vision-based object stabilisation is an exciting and challenging area of research, and is one that promises great technical advancements in the field of computer vision. As humans, we are capable of a tremendous array of skilful interactions, particularly when balancing unstable objects that have complex, non-linear dynamics. These complex dynamics impose a difficult control problem, since the object must be stabilised through collaboration between applied forces and vision-based feedback. To coordinate our actions and facilitate delivery of precise amounts of muscle torque, we primarily use our eyes to provide feedback in a closed-loop control scheme. This ability to control an inherently unstable object by vision-only feedback demonstrates an exceptionally high degree of voluntary motor skill. Despite the pervasiveness of vision-based stabilisation in humans and animals, relatively little is known about the neural strategies used to achieve this task. In the last few decades, with advancements in technology, we have tried to impart the skill of vision-based object stabilisation to machines, with varying degrees of success. Within the context of this research, we continue this pursuit by employing the classic Cart Inverted Pendulum; an inherently unstable, non-linear system to investigate dynamic object balancing by vision-only feedback. The Inverted Pendulum is considered to be one of the most fundamental benchmark systems in control theory; as a platform, it provides us with a strong, well established test bed for this research. We seek to discover what strategies are used to stabilise the Cart Inverted Pendulum, and to determine if these strategies can be deployed in Real-Time, using cost-effective solutions. The thesis confronts, and overcomes the problems imposed by low-bandwidth USB cameras; such as poor colour-balance, image noise and low frame rates etc., to successfully achieve vision-based stabilisation. The thesis presents a comprehensive vision-based control system that is capable of balancing an inverted pendulum with a resting oscillation of approximately ±1º. We employ a novel, segment-based location and tracking algorithm, which was found to have excellent noise immunity and enhanced robustness. We successfully demonstrate the resilience of the tracking and pose estimation algorithm against visual disturbances in Real-Time, and with minimal recovery delay. The algorithm was evaluated against peer reviewed research; in terms of processing time, amplitude of oscillation, measurement accuracy and resting oscillation. For each key performance indicator, our system was found to be superior in many cases to that found in the literature. The thesis also delivers a complete test software environment, where vision-based algorithms can be evaluated. This environment includes a flexible tracking model generator to allow customisation of visual markers used by the system. We conclude by successfully performing off-line optimization of our method by means of Artificial Neural Networks, to achieve a significant improvement in angle measurement accuracy.Goodrich Engine Control Systems and Balfour Beatty Rail Technologie

Non-conventional control of the flexible pole-cart balancing problem

Emerging techniques of intelligent or learning control seem attractive for applications in manufacturing and robotics. It is however important to understand the capabilities of such control systems. In the past the inverted pendulum has been used as a test case. The thesis begins with an examination of whether the inverted pendulum or polecart balancing problem is a representative problem for experimentation for learning controllers for complex nonlinear systems. Results of previous research concerning the inverted pendulum problem are presented to show that this problem is not sufficiently testing. This thesis therefore concentrates on the control of the inverted pendulum with an additional degree of freedom as a testing demonstrator problem for learning control system experimentation. A flexible pole is used in place of a rigid one. The transverse displacement of the flexible pole adds a degree of freedom to the system. The dynamics of this new system are more complex as the system needs additional parameters to be defIned due to the pole's elastic deflection. This problem also has many of the signifIcant features associated with flexible robots with lightweight links as applied in manufacturing. Novel neural network and fuzzy control systems are presented that control such a system both in simulation and real time. A fuzzy-genetic approach is also demonstrated that allows the creation of fuzzy control systems without the use of extensive knowledge