This paper presents the real‐time visual servoing of a manipulator and its tracking strategy of a fish, by employing a genetic algorithm (GA) and the unprocessed gray‐scale image termed here as “raw‐image”. The raw‐image is employed to shorten the control period, since it has more tolerance of contrast variations occurring within an object, and between one input image and the next one. GA is employed in a method called 1‐step‐GA evolution. In this way, for every generational step of the GA process, the found results, which express the deviation of the target in the camera frame, are output for control purposes. These results are then used to determine the control inputs of the PD‐type controller. Our proposed GA‐based visual servoing has been implemented in a real system, and the results have shown its effectiveness by successfully tracking a moving target fish.

No abstract

No abstract

Recognition of a working environment is critical for an autonomous vehicle such as a mobile robot to guide it along corridor and to confirm its possible intelligence. Therefore it is necessary to equip a recognition system with sensor that collect environmental information. As an effective sensor a CCD camera is generally useful for all kinds of mobile robots. However, it is hard to use the CCD camera for visual feedback since it requires to acquire information in real-time, and moreover to be robust against lighting condition varieties. This research presents a corridor recognition method using unprocessed gray-scale image, termed a raw image, and a genetic algorithm (GA), without any image information conversion, to conduct the recognition process in real-time. To achieve robustness concerning lighting condition varieties, we propose a model-matching method using a representative object model designated here as surface-strips model. The robustness of the method against noise in the environment, including lighting conditions variations, and the effectiveness of the method for real-time recognition have been verified using real corridor images.

No abstract