| 书目名称 | Contributions to Autonomous Mobile Systems | | 编辑 | Andreas Graffunder,Rüdiger Hantsche,Lutz Vietze | | 视频video | http://file.papertrans.cn/238/237178/237178.mp4 | | 丛书名称 | Advances in Control Systems and Signal Processing | | 图书封面 |  | | 描述 | Autonomous mobile systems (AMS) are systems capable of some mobility and equipped with advanced sensor devices in order to flexibly respond to changing environmental situations, thus achieving some degree of autonomy. The purpose of this book is to contribute to some essential topics in this broad research area related to sensing and control, but not to present a complete design of an AMS. Subjects conceming knowledge based control and decision, such as moving around obstacles, task planning and diagnosis are left for future publications in this series. Research in the area of AMS has grown rapidly during the last decade, see e.g. [WAXMAN et al. 87], [DICKMANNS , ZAPP 87]. The requirements of an AMS strongly depends on the desired tasks the system should execute, its operational environment and the expected speed of the AMS. For instance, road vehicles obtain velocities of 10 m/s and more, therefore the processing of sensor data such as video image sequences has to be very fast and simple, while indoor mobile robots deal with shorter distances and lower speeds, thus more sophistcated techniques are applicable and -as is done in our approach- additional sensors can be integrated to | | 出版日期 | Book 1992 | | 关键词 | algorithms; detection; image processing | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-663-06842-6 | | isbn_softcover | 978-3-528-06383-2 | | isbn_ebook | 978-3-663-06842-6 | | copyright | Springer Fachmedien Wiesbaden 1992 |
| 1 |
Front Matter |
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Abstract
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| 2 |
,Introduction, |
Andreas Graffunder,Rüdiger Hantsche,Irmfried Hartmann,Joerg Moebius,Zhiyun Ren,Matthias Boldin,Lutz |
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Abstract
Autonomous mobile systems (AMS) are systems capable of some mobility and equipped with advanced sensor devices in order to flexibly respond to changing environmental situations, thus achieving some degree of autonomy.
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| 3 |
,Modeling and Control of Robotic Manipulators, |
M. Boldin,A. Graffunder,Z. Ren |
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Abstract
The application of robotic manipulators in industrial manufacturing has grown rapidly during the last decades. In some fields such as spot welding and spray painting the use of robots is very common since reliable and rather simple modeling and control techniques are available. Utilisation of heavy rigid robots with high gear ratios allows to use simple linear models and linear control techniques. As robot application became more popular in industry it proved to be desirable to let the robot do more sophisticated tasks such as assembly, arc welding, grinding a.s.o. and to increase the robots working speed. The solution of these problems as well as the increasing appearance of direct drive robots required to consider the nonlinear coupled structure of robot mechanics.
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| 4 |
,Estimation of Structure and Relative Motion from Stereo Image Sequences, |
A. Graffunder |
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Abstract
One of the key-problems in realizing Autonomous Mobile Systems (AMS) is deriving descriptions of the structure and motions of objects in the environment of an AMS.
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| 5 |
,Dynamic 3D Vision : The Visually Controlled Robot, |
A. Graffunder,Z. Ren |
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Abstract
In this chapter we are interested in how the concepts of chapter 3 may be utilized for the purpose of robot control in closed visual loop. Specifically, we are interested in formulating a control strategy that is suitable for controlling the movements of a robot relative to relevant objects in its environment. This is obvious, since achieving some degree of “autonomy” for a mobile agent (robot) includes achieving some capabilities for situation-dependent movement-behaviour. In many cases, this can be done best by exploiting information from visual sensors.
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| 6 |
,Endeffector Force Approximation, |
M. Boldin |
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Abstract
Endeffector forces play an important role in certain manufactoring tasks, such as screwing or inserting, in path tracking tasks, such as contour following or grinding, in tasks where e.g. two robots handle the same object and in mobile robots where the robot arm and consequently the end effector is influenced by platform movements. Before any force control scheme can be implemented there has of course to be a certain force measurement or sensing. In the literature there are mainly three approaches to force sensing. The first is the use of wrist force sensors, i.e. sensors consisting of mechanical elements equipped with strain gauges which convert displacement to force signals (see e.g. [SHIMANO, ROTH 79]). There are different designs available, but the principle remains the same. The advantage of these force sensors is the accuracy of the measurement and the fact that exact knowledge of the endeffector forces is obtained. Their disadvantage is the relatively high price, that makes them impossible to use when low cost solutions are requested. Another method is to measure or monitor the joint torques, which is either done by torque sensors mounted on each joint or by analyzing the ac
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,Ultrasonic Modeling, |
L. Vietze |
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Abstract
The first generation of mobile platforms moved along some given trajectories following magnetic fields produced by cables under the floor. This type of system would stop automatically by mechanical switches if a collision were to occur. In the next generation of mobile systems the sunounding environment around the mobile system is in addition watched with ultrasonic distance measurements. If an obstacle is detected within some security area of the mobile system, it can stop in order to avoid collision.
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| 8 |
,Lane Recognition and Following, |
J. Moebius |
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Abstract
One of the most important parts of autonomous vehicle-control are the determination of environment information. For different mission tasks there is special information needed, but in any case information about a possible path or — in structured environments — the admissable lane is necessary.
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| 9 |
,Concept of a Multi-Transputer-System and its Application to Parallel Processing, |
R. Hantsche |
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Abstract
The determination of an appropriate underlying computer architecture is an important aspect of building up an autonomous mobil system (AMS). This includes not only the selection of a suitable structure of system-components but also the choice of the components themselves, especially that of the processing element(s). The necessary computing power for such a vehicle — navigating vision-based in a changing environment — lies beyond the performance capabilities of single microprocessors, even if future technology may improve the processing rate further. This is caused mainly by the real-time constraint of an AMS and the complexity of the tasks which have to be solved. Parallel processing, in which computational work is done to a certain extend simultanously, seems to be more convenient to real-time vision and control. This may lead to the desired increase of data-throughput and the opportunity to decompose the AMS-software into more or less independent sub-tasks. For designing a suitable computer architecture besides performance facilities, both other general requirements of a computer architecture and specific conditions of our AMS application has to be considered. Subsequently, ther
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Back Matter |
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Abstract
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