KEYWORDS: Sensors, Robotics, Digital signal processing, Motion models, Motion controllers, Motion measurement, Control systems, Motion analysis, Actuators, Data modeling
Recently, there has been an increasing interest in the motion, mechanism, and control of miniature robotic fish using smart actuators due to its efficiency and maneuverability. For a piezoelectric-driven miniature robotic fish, dynamic model of the dual-fin biological system was simulated, and sensors were integrated in the robot fish to measure its dynamic information and realized the motion control with DSP controller. A dynamics model around the structure and working principle of the robot fish was analyzed, and the relational mapping chain from the drive source to the motion of the fuselage was built. Then a lightweight inertial sensor module and a fin-integrated strain sensor were developed, to provide motion and force information of the robotic fish in real time. Especially, the motion state data measured by the inertial sensor is collected by DSP controller, and closed-loop motion control of the robotic fish was realized combining the PI control algorithm with the sensor’s data as feedback information. This study verifies the effectiveness and feasibility of the proposed system with integrated sensors for the robotic fish and provides a theoretical and experimental basis for the development of miniature fish-imitating robots.
This study designs an algorithm to reconstruct the marker point cloud using a monocular camera. The specific processes can be described as: Firstly, a group of images containing only the coded marker and the reference ruler are used for selfcalibration; Secondly, another group of object images are reconstructed by the internal parameters of the camera; The processing algorithms of the two groups of images are similar, and the second group needs additional matching and reconstruction of noncoding points. When a large number of coding marks cannot be placed on the surface of the object to be measured, the observed value of adjustment is reduced. The experimental verification of industrial structural parts shows that the measurement error is 0.05 mm and the relative error is 0.02% in the measurement range of 5 m. Compared with the method using camera internal parameter as observation value adjustment, the accuracy is similar, the number of observations is reduced and it can be applied to most industrial measurement fields.
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