A Multidimensional Scaling Classification of Robotic Sensors
This chapter analyzes the signals captured during impacts and vibrations of a mechanical manipulator. Eighteen signals are captured and several metrics are calculated between them, such as the correlation, the mutual information and the entropy. A sensor
- PDF / 406,793 Bytes
- 10 Pages / 439.37 x 666.142 pts Page_size
- 43 Downloads / 190 Views
A Multidimensional Scaling Classification of Robotic Sensors Miguel F.M. Lima and J.A. Tenreiro Machado
Abstract This chapter analyzes the signals captured during impacts and vibrations of a mechanical manipulator. Eighteen signals are captured and several metrics are calculated between them, such as the correlation, the mutual information and the entropy. A sensor classification scheme based on the multidimensional scaling technique is presented.
35.1
Introduction
The robotic manipulators have several sensors and actuators in order to carry out the desired movements. Due to the multiplicity of sensors, the data obtained can be redundant because the same type of information may be seen by two or more sensors. Due to the price of the sensors, this aspect can be considered in order to reduce the cost of the system. On the other hand, the placement of the sensors is an important issue in order to obtain the suitable signals of the vibration phenomenon. Moreover, the study of these issues can help in the design optimization of the acquisition system. In this line of thought a sensor classification scheme is presented. Several authors have addressed the subject of the sensor classification scheme. White [14] presents a flexible and comprehensive categorizing scheme that is useful for describing and comparing sensors. The author organizes the sensors according to several aspects: measurands, technological aspects, detection means, conversion
M.F.M. Lima (*) CI&DETS and Department of Electrical Engineering, School of Technology, Polytechnic Institute of Viseu, Viseu, Portugal e-mail: [email protected] J.A.T. Machado Department of Electrical Engineering, Institute of Engineering, Polytechnic Institute of Porto, Porto, Portugal e-mail: [email protected] A. Madureira et al. (eds.), Computational Intelligence and Decision Making: Trends and 377 Applications, Intelligent Systems, Control and Automation: Science and Engineering 61, DOI 10.1007/978-94-007-4722-7_35, # Springer Science+Business Media Dordrecht 2013
378
M.F.M. Lima and J.A.T. Machado
phenomena, sensor materials and fields of application. Michahelles and Schiele [10] systematize the use of sensor technology. They identified several dimensions of sensing that represent the sensing goals for physical interaction. A conceptual framework is introduced that allows categorizing existing sensors and evaluates their utility in various applications. Today’s technology offers a wide variety of sensors. In order to use all the data from the diversity of sensors a framework of integration is needed. Sensor fusion, fuzzy logic, and neural networks are often mentioned when dealing with problem of combing information from several sensors to get a more general picture of a given situation. The study of data fusion has been receiving considerable attention [4,8]. A survey of the state of the art in sensor fusion for robotics can be found in [5]. Henderson and Shilcrat [6] introduced the concept of logic sensor that defines an abstract specification of the sensors to inte
Data Loading...
