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The purpose of this paper is to develop a methodology for detecting tree trunks for autonomous agricultural applications performed using mobile robots.

The system is constructed by following the principles of Voronoi diagram method which is one of the machine learning algorithms used by the robotics, mechatronics and automation researchers.

To analyze the accuracy and performance and to make verification and evaluation, both simulation and experimental studies are conducted. The results indicate that the tree trunk detection system developed using Voronoi diagram method can be able to detect tree trunks with high precision.

A novel solution technique to detect tree trunks is proposed. The adaptation of Voronoi diagram method in an agricultural (orchard) task is presented.

The purpose of this paper is to present work which is a part of the Comprehensive Automation for Specialty Crops project (CASC). Desired trajectory tracking objective has been previously performed by using a non‐model based approach in this project. Long distance autonomous drive has been achieved; however the results haven't met the expectations of the project requirements. In order to provide these requirements, this study is conducted. In this study, long distance autonomous trajectory tracking for an orchard vehicle is studied. Besides longitudinal motion, lateral motion of the vehicle is also considered. The longitudinal and lateral errors are objected to keep into a region of less than 10 cm.

Car‐like robot kinematic modeling approach is used to create desired trajectory. In order to control longitudinal velocity and steering angle of the vehicle, a controller methodology is proposed. Stability of the controller proposed is shown by using Lyapunov stability approach.

The proposed model is adapted into a four‐wheeled autonomous orchard vehicle and tested in an experimental orchard for long distance autonomous drives. More than 15 km autonomous drive is successfully achieved and the details are presented in this paper.

In this study, long distance autonomous trajectory tracking for an orchard vehicle is focused. A model based control strategy, including the information about longitudinal and lateral motion of the vehicle, is constructed. A new approach to create steering angles for turning operations of the orchard vehicle is introduced. It is objected that the longitudinal and lateral errors should be less than 10 cm during the trajectory tracking task.

The purpose is to create an algorithm that optimizes the trajectories that an autonomous vehicle must follow to reduce its energy consumption and reduce the emission of greenhouse gases.

An algorithm is presented that respects the dynamic constraints of the robot, including the characteristics of power delivery by the motor, the behaviour of the tires and the basic inertial parameters. Using quadratic sequential programming with distributed and non-monotonous search direction (Quadratic Programming Algorithm with Distributed and Non-Monotone Line Search), an optimization algorithm proposed and developed by Professor K. Schittkowski is implemented.

Relations between important operating variables have been obtained, such as the evolution of the autonomous vehicle’s velocity, the driving torque supplied by the engine and the forces acting on the tires. In a subsequent analysis, the aim is to analyse the relationship between trajectory made and energy consumed and calculate the reduction of greenhouse gas emissions. Also this method has been checked against another different methodology commented on in the references.

The main limitation comes from the modelling that has been done. As greater is the mechanical systems analysed, more simplifying hypotheses should be introduced to solve the corresponding equations with the current computers. However, the solutions are obtained and they can be used qualitatively to draw conclusions.

One main objective is to obtain guidelines to reduce greenhouse gas emissions by reducing energy consumption in the realization of autonomous vehicles’ trajectories. The first step to achieve that is to obtain a good model of the autonomous vehicle that takes into account not only its kinematics but also its dynamic properties, and to propose an optimization process that allows to minimize the energy consumed. In this paper, important relationships between work variables have been obtained.

The idea is to be friendly with nature and the environment. This algorithm can help by reducing an instance of greenhouse gases.

Originality comes from the fact that we not only look for the autonomous vehicle’s modelling, the simulation of its motion and the analysis of its working parameters, but also try to obtain from its working those guidelines that are useful to reduce the energy consumed and the contamination capability of these autonomous vehicles or car-like robots.

This paper aims to investigate the relationships between organizational citizenship behavior (OCB) and its antecedents – job satisfaction (JS), affective commitment (AC), organizational justice (OJ) and charismatic leadership (CL); the relationships among its antecedents; and mediating roles of JS and AC in the relationships between CL and OCB and between OJ and OCB in the Turkish hospitality industry in Antalya region. Improving OCB in the hospitality industry, as in other industries, plays an important role in increasing service quality and organization performance.

The sample used for this study consisted of 768 employees working in 18 hotels (4- or 5-Star) in the Antalya region of Turkey. The data collection methodology of the study was a questionnaire survey which was administered through drop-and-collect methodology. The analysis methodology of the study was structural equation modeling.

The findings indicated that both AC and JS mediated the relationship between CL and OCB, as well as between OJ and OCB. Guiding employees by charismatic leaders as well as securing OJ at workplaces in hospitality organizations in the Antalya region of Turkey contribute to OCB positively; however, JS and AC mediate this relationship. Thus, it is assumed that managers should take steps to improve the AC and JS attributes of their employees. In addition, unlike empirical findings in hospitality management literature, this research could not find any direct relationship between CL and OCB.

The present study provides practical implications for hotel managers who may be searching for ways in which to increase OCB and firm performance. The sample is confined to the hospitality industry, thus the generalization of findings to other industries might be limited. Hence, the authors suggest future studies which better enable comparison among various industries.

The novelty of the present study arises from two points: incorporating a CL variable into the study’s model regarding the antecedents of OCB and investigating the mediating roles of JS and AC between CL and OCB as well as between OJ and OCB within the hospitality management industry.

The purpose of this paper is to determine which strategic objective factors have significant effects on competitive advantage of private label manufacturers in Turkey.

A study was conducted of 90 Turkish private label manufacturers. A web‐based questionnaire was the chosen method.

Three strategic objective factors were found to have an effect on competitive advantage: production efficiency, market embeddedness and product selling control.

A comparative analysis between retailers and manufacturers of private labels was regarded as necessary to learn about their perspectives regarding competition. The large sample size encouraged confident generalization of the findings. Another limitation was only analyzing data from a country that has a low private label market share.

The findings of this paper offer valuable insights to retailers, national brand manufacturers and private label manufacturers, enabling them to learn the triggers for product manufacturing from the perspective of private label manufacturers.

It is hoped that this paper will reveal some valuable perspectives from an emerging private label market.