SUMMARYThis paper presents a novel line of sight control system for a robot vision tracking system, which uses a position feedforward controller to preposition a camera, and a vision feedback controller to compensate for the positioning error. Continuous target tracking is an important function for service robots, surveillance robots, and cooperating robot systems. However, it is difficult to track a specific target using only vision information, while a robot is in motion. This is especially true when a robot is moving fast or rotating fast. The proposed system controls the camera line of sight, using a feedforward controller based on estimated robot position and motion information. Specifically, the camera is rotated in the direction opposite to the motion of the robot. To implement the system, a disturbance compensator is developed to determine the current position of the robot, even when the robot wheels slip. The disturbance compensator is comprised of two extended Kalman filters (EKFs) and a slip detector. The inputs of the disturbance compensator are data from an accelerometer, a gyroscope, and two wheel-encoders. The vision feedback information, which is the targeting error, is used as the measurement update for the two EKFs. Using output of the disturbance compensator, an actuation module pans the camera to locate a target at the center of an image plane. This line of sight control methodology improves the recognition performance of the vision tracking system, by keeping a target image at the center of an image frame. The proposed system is implemented on a two-wheeled robot. Experiments are performed for various robot motion scenarios in dynamic situations to evaluate the tracking and recognition performance. Experimental results showed the proposed system achieves high tracking and recognition performances with a small targeting error.

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Total internal reflection fluorescence (TIRF) microscopy, which has about 100-nm axial excitation depth, is the method of choice for nanometer-sectioning imaging for decades. Lately, several new imaging techniques, such as variable angle TIRF microscopy, supercritical-angle fluorescence microscopy, and metal-induced energy transfer imaging, have been proposed to enhance the axial resolution of TIRF. However, all of these methods use high numerical aperture (NA) objectives, and measured images inevitably have small field-of-views (FOVs). Small-FOV can be a serious limitation when multiple cells need to be observed. We propose large-FOV nanometer-sectioning microscopy, which breaks the complementary relations between the depth of focus and axial sectioning by using MIET. Large-FOV imaging is achieved with a low-magnification objective, while nanometer-sectioning is realized utilizing metal-induced energy transfer and biexponential fluorescence lifetime analysis. The feasibility of our proposed method was demonstrated by imaging nanometer-scale distances between the basal membrane of human aortic endothelial cells and a substrate.

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Nanometer‐sectioning optical microscopy has become an indispensable tool in membrane‐related biomedical studies. Finally, many nanometer‐sectioning imaging schemes, such as variable‐angle total internal reflection fluorescence microscopy, metal‐induced energy transfer (MIET) imaging, and supercritical‐angle fluorescence microscopy have been introduced. However, these methods can measure a single layer of molecules, and the measurement ranges are below 100 nm, which is not large enough to cover the thickness of lamellipodium. This paper proposes an optical imaging scheme that can identify the axial locations of two layers of molecules with an extended measurement range and a nanometer‐scale precision by using MIET, axial focal plane scanning, and biexponential analysis in fluorescence lifetime imaging microscopy. The feasibility of the proposed method is demonstrated by measuring an artificial sample of a known structure and the lamellipodium of a human aortic endothelial cell whose thickness ranges from 100 to 450 nm with 18.3 nm precision.

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