Proceedings of the 4th field robot event 2006, Stuttgart/Hohenheim, Germany, 23-24th June 2006

Zeer uitgebreid verslag van het 4e Fieldrobotevent, dat gehouden werd op 23 en 24 juni 2006 in Stuttgart/Hohenhei

Landsat Satellite Image Segmentation Using the Fuzzy ARTMAP Neural Network

This application illustrates how the fuzzy ARTMAP neural network can be used to monitor environmental changes. A benchmark problem seeks to classify regions of a Landsat image into six soil and crop classes based on images from four spectral sensors. Simulations show that fuzzy ARTMAP outperforms fourteen other neural network and machine learning algorithms. Only the k-Nearest-Neighbor algorithm shows better performance (91% vs. 89%) but without any code compression, while fuzzy ARTMAP achieves a code compression ratio of 6:1. Even with a code compression ratio of 50:1 fuzzy ARTMAP still maintains good performance (83%). This example shows how fuzzy ARTMAP can combine accuracy and code compression in real-world applications.Office of Naval Research (N00014-92-J-401J, N00014-91-J-4100, N00014-92-J-4015); National Science Foundation (IRI 90-00530

Multi-robot Tethering Using Camera

An autonomous multi-robot or swarm robot able to perform various cooperative mission such as search and rescue, exploration of unknown or partially known area, transportation, surveillance, defence system, and also firefighting. However, multi-robot application often requires synchronised robotic configuration, reliable communication system and various sensors installed on each robot. This approach has resulted system complexity and very high cost of development

Landsat Satellite Image Segmentation Using the Fuzzy ARTMAP Neural Network

This application illustrates how the fuzzy ARTMAP neural network can be used to monitor environmental changes. A benchmark problem seeks to classify regions of a Landsat image into six soil and crop classes based on images from four spectral sensors. Simulations show that fuzzy ARTMAP outperforms fourteen other neural network and machine learning algorithms. Only the k-Nearest-Neighbor algorithm shows better performance (91% vs. 89%) but without any code compression, while fuzzy ARTMAP achieves a code compression ratio of 6:1. Even with a code compression ratio of 50:1 fuzzy ARTMAP still maintains good performance (83%). This example shows how fuzzy ARTMAP can combine accuracy and code compression in real-world applications.Office of Naval Research (N00014-92-J-401J, N00014-91-J-4100, N00014-92-J-4015); National Science Foundation (IRI 90-00530

Concrete brick properties incorporating EPS and POFA as replacement materials

The implementation of sustainable construction and green building becomes the main attention of construction industries in Malaysia as it has been introduced by the government in the Construction Industry Transformation Programme (2016-2020). Therefore, this study focuses on the development of sustainable concrete bricks containing Expanded Polystyrene (EPS) and Palm Oil Fuel Ash (POFA) as sand and cement substitute materials. The percentage of replacement is 20%, 30%, 40% and 50% for EPS and 5%, 10%, 15%, 20% and 25% for POFA. There are 30 different mix designs of brick have been produced and their properties have been identified. Hardened brick density, compressive strength, water absorption and initial rate of absorption are the brick properties identified in this study. Based on the experimental results, it was found that the hardened brick density and compressive strength of the brick decreased as the replacement percentage increased. On the other hand, the water absorption and initial rate of absorption of the brick decreased as the percentage of EPS increased and increased as the percentage of POFA increased. Based on the findings, it shows that EPS and POFA has significantly contributes to the reduction of brick density. Next, for the compressive strength all the bricks have satisfied the minimum strength requirement of non-load bearing brick. Finally, for water absorption and initial rate of absorption, it has been found that majority of the bricks have an acceptable value based on standard requirements for brick. This can be concluded that EPS and POFA could be potential substitute materials for the manufacture of sustainable bricks

Design of an adaptive neural predictive nonlinear controller for nonholonomic mobile robot system based on posture identifier in the presence of disturbance

This paper proposes an adaptive neural predictive nonlinear controller to guide a nonholonomic wheeled mobile robot during continuous and non-continuous gradients trajectory tracking. The structure of the controller consists of two models that describe the kinematics and dynamics of the mobile robot system and a feedforward neural controller. The models are modified Elman neural network and feedforward multi-layer perceptron respectively. The modified Elman neural network model is trained off-line and on-line stages to guarantee the outputs of the model accurately represent the actual outputs of the mobile robot system. The trained neural model acts as the position and orientation identifier. The feedforward neural controller is trained off-line and adaptive weights are adapted on-line to find the reference torques, which controls the steady-state outputs of the mobile robot system. The feedback neural controller is based on the posture neural identifier and quadratic performance index optimization algorithm to find the optimal torque action in the transient state for N-step-ahead prediction. General back propagation algorithm is used to learn the feedforward neural controller and the posture neural identifier. Simulation results show the effectiveness of the proposed adaptive neural predictive control algorithm; this is demonstrated by the minimised tracking error and the smoothness of the torque control signal obtained with bounded external disturbances