Patrick Heinemann

Foreword agement accounting information (MAI) in organizational settings. In this context, mining managerial performance. VI Foreword Finally, Heinemann looks into the potential moderating influence of supervisors' - wer bases, selected subordinates' characteristics (job locus of control and job se- efficacy), as well as task uncertainty. Here, as well as in the other parts of his study, Heinemann provides highly interesting findings, which may serve as a stimulus for future research. Utz Schäffer Preface VII Preface sons: VIII Preface I further want to express my gratitude to my girlfriend Christina who has had to live through all of my ups and downs and has supported me throughout the entire process. My deepest gratitude, however, belongs to my parents and my grand-mother for their encouragement and unconditional support during every phase of my life. I dedicate this book to them. Patrick Heinemann ab Tl e o f C t en o n IX t s Table of Contents Foreword. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VII Table of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IX List of Tables . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XIII List of Figures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XVII A Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1. Motivation and Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2. Course of Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 B Social Influence and Power. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1. Influence Strategies as a Means to Exercise Power. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1. 1 Social Influence and Influence Strategies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1. 1. 1 The Role of Social Influence in Organizations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1. 1. 2 Means to Exercise Influence in Organizations . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . 8 1. 1. 3 Outcomes of Influence Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1. 1. 4 Factors Affecting the Outcomes of Influence Strategies . . . . . . . . . . . . . . . . . . . . . .

The number of robots applied to different jobs in our society has steadily increased over the past years. While industrial robots welding and varnishing our cars are quite common these days, the future generation of robots will enter our everyday life. In contrast to industrial robots working in special evacuated areas for security reasons, these mobile service robots will have to move among and interact with humans, imposing new challenges on the software of the robots. Only the application of sensors to observe the environment and the subsequent use of intelligent data processing algorithms will enable the robots to avoid collisions with moving obstacles, to get information on their current locations, to perform goal oriented tasks and to cooperate with humans as well as other robots. This thesis addresses these new challenges by describing the development of a team of robots that is able to exhibit intelligent behavior, competitive as well as cooperative, while moving in a highly dynamic environment, as a first step towards a new kind of mobile robots. To evaluate and compare the results of the development, robotic soccer (RoboCup) was chosen as testbed for the robot team. Many of the challenges imposed by the RoboCup environment are similar to those found in real-life applications. Robots in RoboCup have to localize themselves, gather information on their environment, including the position and also the velocity of potential obstacles, deal with varying lighting scenarios, as well as competing and cooperating with other robots. Firstly, the thesis presents the decisions made concerning the co-design of the robot hardware and software. Here, the selection of an omni-directional drive mechanism and a vision sensor with a high frame rate, the software framework designed to be flexible and expandable, as well as the development of a common and simple control interface are the key points in the system design. The main part of the thesis, however, presents the efficient algorithms implemented for image processing, environment modeling, and high-level control. As the only sensor of the robots is an omni-directional vision system that is able to map the complete surrounding of the robot, the performance of the robot system depends on the image processing algorithms and requires a high accuracy and a low processing time. The presented image processing algorithms fulfill both requirements proven by extensive experimental results. Besides landmark and object extraction these algorithms also include new techniques for automatic camera calibration. The environment modeling contains a new combined Monte-Carlo localization and tracking algorithm that is competitive with the best performing algorithms concerning the accuracy and the computation time at the same time, again proven by extensive experimental results. Finally, the high-level robot control component exhibits efficient movement through highly dynamic environments and successful competitive and cooperative behavior. The thesis concludes with an analysis of the team's RoboCup competition results as a final verification of the good performance of the developed system.