par Keymeulen, Didier 
;Iwata, Masaya;Konaka, Kenji;Kuniyoshi, Yasuo;Higuchi, Tetsuya
Référence New generation computing, 16, 2, page (97-122)
Publication Publié, 1998
;Iwata, Masaya;Konaka, Kenji;Kuniyoshi, Yasuo;Higuchi, TetsuyaRéférence New generation computing, 16, 2, page (97-122)
Publication Publié, 1998
Article révisé par les pairs
| Résumé : | Recently there has been great interest in the design and study of evolvable systems based on Artificial Life principles in order to monitor and control the behavior of physically embedded systems such as mobile robots, plants and intelligent home devices. At the same time new integrated circuits called software-reconfigurable devices have been introduced which are able to adapt their hardware almost continuously to changes in the input data or processing. When the configuration phase and the execution phase are concurrent, the software-reconfigurable device is called evolvable hardware (EHW). This paper examines an evolutionary navigation system for a mobile robot using a Boolean function approach implemented on gate-level evolvable hardware (EHW). The task of the mobile robot is to reach a goal represented by a colored ball while avoiding obstacles during its motion. We show that the Boolean function approach using dedicated evolution rules is sufficient to build the desired behavior and its hardware implementation using EHW allows to decrease the learning time for on-line training. We demonstrate the effectiveness of the generalization ability of the Boolean function approach using EHW due to its representation and evolution mechanism. The results show that the evolvable hardware configuration learned off-line in a simple environment creates a robust robot behavior which is able to perform the desired behaviors in more complex environments and which is insensitive to the gap between the real and simulated world. |
