Evolutionary calibration of sensors using genetic programming on evolvable hardware

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