Robustness analysis of evolutionary controller tuning using real systems

A genetic algorithm (GA) presents an excellent method for controller parameter tuning. In our work, we evolved the heading as well as the altitude controller for a small lightweight helicopter. We use the real flying robot to evaluate the GA's individuals rather than an artificially consistent simulator. By doing so we avoid the ldquoreality gaprdquo, taking the controller from the simulator to the real world. In this paper we analyze the evolutionary aspects of this technique and discuss the issues that need to be considered for it to perform well and result in robust controllers

Artificial intelligence tools for path generation and optimisation for mobile robots

The ultimate goal in robotic systems is to develop machines that learn for themselves based on experience. In order to achieve on-line learning some software tools are needed to allow the robots to continually adapt their behaviour in order to constantly optimise their performance. This thesis presents research work focused on path planning for mobile robots with the objective of generating optimal paths for any type of mobile robot in an environment containing any number of static obstacles of any shape. The research specifically recognises that an optimal path can be defined according to several criteria including distance, time, energy consumption and risk. The easiest and most commonly used measure is to minimise distance, but this does not by itself optimise task performance, and the other criteria are generally far more important. Distance is used mainly because there is no direct method to optimise time, energy and risk as they depend on the characteristics of the robot and the environment. This is solved in this research by using a set of Artificial Intelligence tools working together to perform an optimisation process strictly on the criteria selected. The path planning system developed consists of an original and novel two-stage 4 process comprising generation followed by optimisation. Path generation is achieved using cellular automata whose behaviour has been determined by a genetic algorithm. A program called Rutar has been written in which the best behaviour found by the genetic algorithm is encoded, and it has been tested and shown to infallibly generate all the non-redundant paths between any two points around any obstacles. An interesting and valuable feature of Rutar is that the time taken to generate paths depends only on the amount of free space available in which the robot can move and therefore the more obstacles there are present, and hence the more complex the layout, the faster the execution time. The paths generated are sub-optimal solutions, which are then optimised according to the user's selection of a combination of Time, Energy, Distance and Risk criteria. The optimisation process is performed by another genetic algorithm. The original scheme used in this work allows any combination of all the desired criteria in a single optimisation process, allowing it to handle very complex non-linear problems. All of the optimisation criteria can be used in situations where the environment and the robot are considered to be unchanged during the interval in which the robot moves. This optimisation can be performed either off-line or on-line. However, the ability of the developed system to generate and optimise the paths very fast provide an opportunity for dynamic path optimisatiorý which ultimately can lead to on-line learning. This potential of the tools developed for the path planning system is explored and recommendations for further exploitation are made

Real-time evolution of an embedded controller for an autonomous helicopter

In this paper we evolve the parameters of a proportional, integral, and derivative (PID) controller for an unstable, complex and nonlinear system. The individuals of the applied genetic algorithm (GA) are evaluated on the actual system rather than on a simulation of it, thus avoiding the ldquoreality gaprdquo. This makes implicit a formal model identification for the implementation of a simulator. This also calls for the GA to be approached in an unusual way, where we need to consider new aspects not normally present in the usual situations using an unnaturally consistent simulator for fitness evaluation. Although elitism is used in the GAs, no monotonic increase in fitness is exhibited by the algorithm. Instead, we show that the GApsilas individuals converge towards more robust solutions

Web Usage Mining with Evolutionary Extraction of Temporal Fuzzy Association Rules

In Web usage mining, fuzzy association rules that have a temporal property can provide useful knowledge about when associations occur. However, there is a problem with traditional temporal fuzzy association rule mining algorithms. Some rules occur at the intersection of fuzzy sets' boundaries where there is less support (lower membership), so the rules are lost. A genetic algorithm (GA)-based solution is described that uses the flexible nature of the 2-tuple linguistic representation to discover rules that occur at the intersection of fuzzy set boundaries. The GA-based approach is enhanced from previous work by including a graph representation and an improved fitness function. A comparison of the GA-based approach with a traditional approach on real-world Web log data discovered rules that were lost with the traditional approach. The GA-based approach is recommended as complementary to existing algorithms, because it discovers extra rules. (C) 2013 Elsevier B.V. All rights reserved

Supervised Control of a Flying Performing Robot using its Intrinsic Sound

We present the current results of our ongoing research in achieving efficient control of a flying robot for a wide variety of possible applications. A lightweight small indoor helicopter has been equipped with an embedded system and relatively simple sensors to achieve autonomous stable flight. The controllers have been tuned using genetic algorithms to further enhance flight stability. A number of additional sensors would need to be attached to the helicopter to enable it to sense more of its environment such as its current location or the location of obstacles like the walls of the room it is flying in. The lightweight nature of the helicopter very much restricts the amount of sensors that can be attached to it. We propose utilising the intrinsic sound signatures of the helicopter to locate it and to extract features about its current state, using another supervising robot. The analysis of this information is then sent back to the helicopter using an uplink to enable the helicopter to further stabilise its flight and correct its position and flight path without the need for additional sensors

Managing uncertainty in sound based control for an autonomous helicopter

In this paper we present our ongoing research using a multi-purpose, small and low cost autonomous helicopter platform (Flyper ). We are building on previously achieved stable control using evolutionary tuning. We propose a sound based supervised method to localise the indoor helicopter and extract meaningful information to enable the helicopter to further stabilise its flight and correct its flightpath. Due to the high amount of uncertainty in the data, we propose the use of fuzzy logic in the signal processing of the sound signature. We discuss the benefits and difficulties using type-1 and type-2 fuzzy logic in this real-time systems and give an overview of our proposed system

Temporal fuzzy association rule mining with 2-tuple linguistic representation

This paper reports on an approach that contributes towards the problem of discovering fuzzy association rules that exhibit a temporal pattern. The novel application of the 2-tuple linguistic representation identifies fuzzy association rules in a temporal context, whilst maintaining the interpretability of linguistic terms. Iterative Rule Learning (IRL) with a Genetic Algorithm (GA) simultaneously induces rules and tunes the membership functions. The discovered rules were compared with those from a traditional method of discovering fuzzy association rules and results demonstrate how the traditional method can loose information because rules occur at the intersection of membership function boundaries. New information can be mined from the proposed approach by improving upon rules discovered with the traditional method and by discovering new rules

Fuzzy Helicopter Rotor Speed Estimation based on Sound

This work focuses on the use of a supervising computer to extract detailed information from an autonomous helicopter’s intrinsic sound signature. This can be used at a later stage to enhance the helicopter’s control without the need to add additional sensors. We propose a system to extract the overall rotor speed from the sound of the helicopter. A fuzzy temporal filter based system is trained on flight data using an Adaptive Network- Based Fuzzy Inference System and tested in three test flights. Test flights confirm the system to be working, capable of closely following the measured rotational speed from a sensor on-board the helicopter

Finding multi-density clusters in non-stationary data streams using an ant colony with adaptive parameters

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Density based methods have been shown to be an effective approach for clustering non-stationary data streams. The number of clusters does not need to be known a priori and density methods are robust to noise and changes in the statistical properties of the data. However, most density approaches require sensitive, data dependent parameters. These parameters greatly affect the clustering performance and in a dynamic stream a good set of parameters at time t are not necessarily the best at time t+1. Furthermore, these parameters are global and so restrict the algorithm to finding clusters of the same density. In this paper, we propose a density based algorithm with adaptive parameters which are local to each discovered cluster. The algorithm, denoted Ant Colony Multi-Density Clustering (ACMDC), uses artificial ants to form nests in dense areas of the data. As the ants move between nests, their collective memory is stored in the form of pheromone trails. Clusters are identified as groups of similar nests. The proposed algorithm is evaluated across a number of synthetic data streams containing overlapping and embedded multi-density clusters. The performance of the algorithm is shown to be favourable to a leading density based stream-clustering algorithm despite requiring no tunable parameters

A Multi-Agent System for Modelling the Spread of Lethal Wilt in Oil-Palm Plantations

Lethal Wilt (Marchitez Letal) is a disease which affects Elaeis Guineensis, a plant used in the production of palm oil. The disease is increasingly common but the spatial dynamics of the infection spread remain poorly understood. It is particularly dangerous due to the speed at which it spreads and the speed at which infected plants show symptoms and die. Early identification, or even better, accurate prediction of areas at high risk of infection can slow the spread of the disease and limit crop waste. This study is based on data collected over a five-year period from an affected plantation in Colombia. The aim of the study is to analyse the collected data to better understand how the disease spreads and then to model the behaviour. Based on insights from the initial analysis a multi-agent-based system is proposed to model the pattern of infection. The model is comprised of two steps; first Kernel Density Estimation is used to create an estimation of the distribution from which newly infected plants are drawn and this density estimation is then used to direct agents on a biased-walk of the surrounding areas. Results show that the model can approximate the behaviour of the disease and can predict areas which are at high risk of future infection