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

A particle swarm optimization algorithm for optimal car-call allocation in elevator group control systems

High-rise buildings require the installation of complex elevator group control systems (EGCS). In vertical transportation, when a passenger makes a hall call by pressing a landing call button installed at the floor and located near the cars of the elevator group, the EGCS must allocate one of the cars of the group to the hall call. We develop a Particle Swarm Optimization (PSO) algorithm to deal with this car-call allocation problem. The PSO algorithm is compared to other soft computing techniques such as genetic algorithm and tabu search approaches that have been proved as efficient algorithms for this problem. The proposed PSO algorithm was tested in high-rise buildings from 10 to 24 floors, and several car configurations from 2 to 6 cars. Results from trials show that the proposed PSO algorithm results in better average journey times and computational times compared to genetic and tabu search approaches

Optimal car dispatching for elevator groups using genetic algorithms

The car dispatching problem in an elevator group consists of assigning cars to the hall calls at the same time that car call are served. The problem needs to coordinate the movements of individual cars with the objective of operating efficiently the whole group. In this paper, we propose an elevator group control system based on a genetic algorithm which makes use of a novel fitness function to evaluate the individuals. The fitness function allows a quick execution of the algorithm. Tests are provided for various types of high-rise buildings to assess the elevator service performance. Comparative simulations show that our genetic algorithm outperforms traditional conventional algorithms developed in the industry. It is important to note that the algorithm is quickly evaluated allowing a real-life implementatio

Improving the Efficiencies of Elevator Systems Using Fuzzy Logic

This research presents the application of fuzzy logic in elevators. This analyzes the features of elevators and how fuzzy logiccould be used to minimize the waiting time, detect when the temperature is high for the car, and determine which floor hashighest number of people waiting for the car. High rising building is a common sight in most of the cities today. Fast andefficient elevator transportation is a key feature when creating these kinds of buildings. As the complexity of a systemincreases, it becomes more difficult and eventually impossible to make a precise statement about its behaviour. Many of thesystems build before fuzzy logic use trial and error and effort had to be done over and over to arrive at effective control.Fuzzy logic concepts are used to enable the elevator control system to make decisions. The design criteria include ofoptimizing movement of elevators with regard to several factors such as waiting time, riding time, energy, load, etc.Software simulation is done in order to capture the performance of the proposed system which is compared to conventionalapproaches.Keywords: Fuzzy logic (FL), Elevator. Car, Software simulation

Elevator controller based on implementing a random access memory in FPGA

Previous techniques of elevator controllers suffer from two main challenges: processing time, and software size. In this work these challenges have been overcame by implementing a controller random access memory (RAM) in a fast FPGA for a proto-type of two-floors elevator, as known the RAM and FPGA are fast devices. A look-up-table LUT (which is fast technique) has been proposed for this work, this LUT has represented a proposed relation between 10 and 7 lines, the states of the sensors and switches have been represented by the 10 input lines, and the commands for the motors of slide door and traction machine have been represented by the 7 output lines. The proposed LUT has been schematically realize by a (10×7) bits RAM which has been implemented in field programmable gate arrays (FPGA). The proposed system has been performed using 'ISE Design Suit' software package and FPGA Spartan6 SP-605 evaluation kit, the clock frequency of this FPGA is 200 MHz which is respectively high. The processing time and software size of the proposed controller had reached to 20ns and 3.75 MB, which they are less than that obtained from the results of previous techniques

A viral system algorithm to optimize the car dispatching in elevator group control systems of tall buildings

Nowadays is very common the presence of tall buildings in the business centres of the main cities of the world. Such buildings require the installation of numerous lifts that are coordinated and managed under a unique control system. Population working in the buildings follows a similar traffic pattern generating situations of traffic congestion. The problem arises when a passenger makes a hall call wishing to travel to another floor of the building. The dispatching of the most suitable car is the optimization problem we are tackling in this paper. We develop a viral system algorithm which is based on a bio-inspired virus infection analogy to deal with it. The viral system algorithm is compared to genetic algorithms, and tabu search approaches that have proven efficiency in the vertical transportation literature. The experiments undertaken in tall buildings from 10 to 24 floors, and several car configurations from 2 to 6 cars, provide valuable results and show how viral system outperforms such soft computing algorithms.Plan Estatal de Investigación Científica y Técnica y de Innovación (España