- author = "Ho-Sik Seok and Byoung-Tak Zhang",
- title = "Evolutionary Calibration of Sensors using Genetic Programming on Evolvable Hardware",
- booktitle = "Proceedings of the 2001 Congress on Evolutionary Computation CEC2001",
- year = "2001",
- pages = "630--634",
- address = "COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea",
- publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA",
- month = "27-30 " # may,
- organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)",
- publisher = "IEEE Press",
- keywords = "genetic algorithms, genetic programming, sensor calibration, evolvable hardware, mobile robot, Khepera, autonomous mobile robots, complex dynamic environment, decision-making ability, evolutionary calibration, interpretation correctness, learning, noise, reconfigurable sensor interpretation circuit, robot perceptual ability, sensitivity, sensor calibration logic, sensor reliability, calibration, control system analysis computing, mobile robots, reconfigurable architectures, reliability, sensitivity, sensors",
- ISBN = "0-7803-6658-1",
-
URL = "
http://bi.snu.ac.kr/Publications/Conferences/International/CEC2001_Seok.pdf",
-
DOI = "
doi:10.1109/CEC.2001.934450",
- size = "5 pages",
- abstract = "In order to retain some degree of decision-making ability in a complex and dynamic environment, there have been many attempts to build autonomous mobile robots. However, conventional methods pay little attention to the unreliability of sensors. Because of corruption by noise and differences in sensitivity, even the same kinds of sensors show different observations under the same conditions. This causes a problem in that a minor change to the environment of the sensor system has a great influence on the perceptual ability of the robot. To improve the reliability of the sensors, we present a method for the evolutionary calibration of sensors using genetic programming as the calibration mechanism. In our approach, the sensor calibration logic is implemented on evolvable hardware. Therefore, as the learning goes on, the sensor interpretation circuit reconfigures itself to a more suitable form at run-time. Through two experiments on different tasks, we confirmed that our method significantly improved the correctness of interpretation",
-
notes = "CEC-2001 - A joint meeting of the IEEE, Evolutionary
Programming Society, Galesia, and the IEE.
IEEE Catalog Number = 01TH8546C,
Library of Congress Number = .
GP tree converted to bit string down loaded onto XC6216 FPGA",
