Application of Sampling-Based Motion Planning Algorithms in Autonomous Vehicle Navigation

With the development of the autonomous driving technology, the autonomous vehicle has become one of the key issues for supporting our daily life and economical activities. One of the challenging research areas in autonomous vehicle is the development of an intelligent motion planner, which is able to guide the vehicle in dynamic changing environments. In this chapter, a novel sampling-based navigation architecture is introduced, which employs the optimal properties of RRT* planner and the low running time property of low-dispersion sampling-based algorithms. Furthermore, a novel segmentation method is proposed, which divides the sampling domain into valid and tabu segments. The resulted navigation architecture is able to guide the autonomous vehicle in complex situations such as takeover or crowded environments. The performance of the proposed method is tested through simulation in different scenarios and also by comparing the performances of RRT and RRT* algorithms. The proposed method provides near-optimal solutions with smaller trees and in lower running time

ロボットによる衣類のマニピュレーションに関する研究

取得学位：博士(工学)，学位授与番号：博甲第852号，学位授与年月日：平成18年9月28

Short-term wind speed forecasting by an adaptive network-based fuzzy inference system (ANFIS): an attempt towards an ensemble forecasting method

Accurate Wind speed forecasting has a vital role in efficient utilization of wind farms. Wind forecasting could be performed for long or short time horizons. Given the volatile nature of wind and its dependent on many geographical parameters, it is difficult for traditional methods to provide a reliable forecast of wind speed time series. In this study, an attempt is made to establish an efficient adaptive network-based fuzzy interference (ANFIS) for short-term wind speed forecasting. Using the available data sets in the literature, the ANFIS network is constructed, tested and the results are compared with that of a regular neural network, which has been forecasted the same set of dataset in previous studies. To avoid trial-and-error process for selection of the ANFIS input data, the results of autocorrelation factor (ACF) and partial auto correlation factor (PACF) on the historical wind speed data are employed. The available data set is divided into two parts. 50% for training and 50% for testing and validation. The testing part of data set will be merely used for assessing the performance of the neural network which guarantees that only unseen data is used to evaluate the forecasting performance of the network. On the other hand, validation data could be used for parameter-setting of the network if required. The results indicate that ANFIS could not outperform ANN in short-term wind speed forecasting though its results are competitive. The two methods are hybridized, though simply by weightage, and the hybrid methods shows slight improvement comparing to both ANN and ANFIS results. Therefore, the goal of future studies could be implementing ANFIS and ANNs in a more comprehensive ensemble method which could be ultimately more robust and accurat

The Dispositional Attribution of Customer Satisfaction through the Juxtaposition of QFD Aand Servqual in Service Industry Design

This study has been carried out to analyze the dispositional source of satisfaction through the juxtaposition of Quality Function Deployment (QFD) and Service Quality (ServQual) in the service industry. ServQual is one of the famous tools to measure the customer satisfaction. The customer satisfaction is measure through five dimensions, which are reliability, assurance, tangible, empathy and responsiveness. QFD is one of the mathematical approach to transform the customer needs into technical requirements. The difference between these two models is, ServQual evaluates the priorities of the basic customer needs, which within these five dimensions, which one is the most important and which one is the least important, according to responds obtained from the customer. Meanwhile, for QFD evaluate the priorities of technical requirement of service that will be able to satisfy the customer. The study has been carried out at one customer service centre in Bangi. The study applies ServQual methodology to measure the customer satisfaction after the service has been delivered through its five dimensions. The QFD methodology is used as one of the mathematical approach to transform the customer needs into technical requirement and evaluate the priorities of technical requirement of service that will be able to satisfy the customer. The results show ServQual and QFD ought to be consolidated to get alternate points of view regarding the behavior of the customers. Acted as variables to quantify in terms of the contentment felt by the customers in the service quality. Besides, it has a distinctive methodology to help service industry being able to gauge the satisfaction of customers. Together they give an intense instrument that is not exclusively will reveal whether the customer satisfaction is fulfilled or not, but rather additionally how great the distinction of the service is as well as the level of competence the organization functions. In addition to that, it will let the companies in the service industries know that they should take action in order to enhance the service quality and along these lines to make the customers satisfied. Through the theoretical base questionnaires, as both of the juxtaposition approaches' analysis, the main priorities need to be taken into consideration is ‘facilities’ in service centre. The service centre needs to ease the customer in whatever their purpose to come to the service centre and make them comfortable enough. While, the factor ‘Ease of contact’ is become the vital consideration in ServQual before integrating towards QFD. These elements are the improvement needed by the service centre to look into details in order to increase and grab more attention towards customer satisfaction

The Dispositional Attribution of Customer Satisfaction through the Juxtaposition of QFD Aand Servqual in Service Industry Design

This study has been carried out to analyze the dispositional source of satisfaction through the juxtaposition of Quality Function Deployment (QFD) and Service Quality (ServQual) in the service industry. ServQual is one of the famous tools to measure the customer satisfaction. The customer satisfaction is measure through five dimensions, which are reliability, assurance, tangible, empathy and responsiveness. QFD is one of the mathematical approach to transform the customer needs into technical requirements. The difference between these two models is, ServQual evaluates the priorities of the basic customer needs, which within these five dimensions, which one is the most important and which one is the least important, according to responds obtained from the customer. Meanwhile, for QFD evaluate the priorities of technical requirement of service that will be able to satisfy the customer. The study has been carried out at one customer service centre in Bangi. The study applies ServQual methodology to measure the customer satisfaction after the service has been delivered through its five dimensions. The QFD methodology is used as one of the mathematical approach to transform the customer needs into technical requirement and evaluate the priorities of technical requirement of service that will be able to satisfy the customer. The results show ServQual and QFD ought to be consolidated to get alternate points of view regarding the behavior of the customers. Acted as variables to quantify in terms of the contentment felt by the customers in the service quality. Besides, it has a distinctive methodology to help service industry being able to gauge the satisfaction of customers. Together they give an intense instrument that is not exclusively will reveal whether the customer satisfaction is fulfilled or not, but rather additionally how great the distinction of the service is as well as the level of competence the organization functions. In addition to that, it will let the companies in the service industries know that they should take action in order to enhance the service quality and along these lines to make the customers satisfied. Through the theoretical base questionnaires, as both of the juxtaposition approaches' analysis, the main priorities need to be taken into consideration is ‘facilities’ in service centre. The service centre needs to ease the customer in whatever their purpose to come to the service centre and make them comfortable enough. While, the factor ‘Ease of contact’ is become the vital consideration in ServQual before integrating towards QFD. These elements are the improvement needed by the service centre to look into details in order to increase and grab more attention towards customer satisfaction

Sampling-based online motion planning for mobile robots: utilization of Tabu search and adaptive neuro-fuzzy inference system

Despite the proven advantages of sampling-based motion planning algorithms, their inability to handle online navigation tasks and providing low-cost solutions make them less efficient in practice. In this paper, a novel sampling-based algorithm is proposed which is able to plan in an unknown environment and provides solutions with lower cost in terms of path length, runtime and stability of the results. First, a fuzzy controller is designed which incorporates the heuristic rules of Tabu search to enable the planner for solving online navigation tasks. Then, an adaptive neuro-fuzzy inference system (ANFIS) is proposed such that it constructs and optimizes the fuzzy controller based on a set of given input/output data. Furthermore, a heuristic dataset generator is implemented to provide enough data for the ANFIS using a randomized procedure. The performance of the proposed algorithm is evaluated through simulation in different motion planning queries. Finally, the proposed planner is compared to some of the similar motion planning algorithms to support the claim of superiority of its performance