ISSN:
1573-0409
Keywords:
Verification
;
task-level programming
;
robot vision
;
uncertainty
;
assembly planning
;
fault tolerance
;
classification
;
prediction
;
camera location
Source:
Springer Online Journal Archives 1860-2000
Topics:
Computer Science
,
Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
Notes:
Abstract The purpose of this paper is to give an overview of past and recent work on planning sensing strategies for vision sensors. To achieve an economic use of robots in manufacturing, their programs must provide a high degree of fault-tolerance, security, and robustness to prevent unforeseen errors. Model errors (also termed uncertainties) are one of the most frequent reasons for such undesirable events. Robot systems can be made more reliable and fault-tolerant by providing them with capabilities of error detection and recovery, or error prevention. The latter may be achieved by reducing model errors using tactile and non-tactile sensors. The quality of a robot program synthesized by a task-level programming system depends on the accuracy of the model, since all information that is not explicitly given by the programmer must be derived from it. This means that the following questions have to be answered by the automatic task planner in order to plan non-tactile sensing strategies: (1) When do I have to use sensors to reduce uncertainty about the real world? (2) What do I have to use them for? (3) How do I have to use them to achieve the necessary information within an acceptable period of time? There are very few systems which deal broadly with the problem of robust robot programs, whereas there are numerous works on detail aspects of the field. The main approaches will be introduced and discussed in more detail. Finally, a new concept for generating sensor-integrated robust robot programs will be proposed.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/BF00303225
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