Bionic flapping-wing micro air vehicles (FWMAVs) are promising for a variety of applications because of their flexibility and high mobility. This study reviews the state-of-the-art FWMAVs of various research institutes driven by electrical motor, mechanical transmission structure and “artificial muscle” material and then elaborates on the aerodynamic mechanism of micro-winged birds and insects. Owing to their low mass budget, FWMAVs require actuators with high power density from micrometer to centimeter scales. The selection and design of the mechanical transmission should be considered in parallel with the design of the power electronic interface required to drive it. Finally, power electronic topologies suitable for driving “artificial muscle” materials used in FWMAVs are stated.
Abstract. At present, the manual grading of soybean seeds is both time consuming and laborious, and detecting the full-surface information of soybean seeds using an existing automatic sorting machine is difficult. To solve this problem, an on-line omnidirectional inspection and sorting system for soybean seeds was developed using embedded image processing technology. According to the principles employed by the system, the surface friction properties and full-surface information such as the shape, texture and color of soybean seeds were adopted in the study. Soybean seeds were inspected and sorted using their full surface information in combination with the embedded image processing technology. Split, worm-eaten, gray-spotted, slightly cracked, moldy and normal soybeans were used to test the system. According to the test results, the optimum design parameters of the preliminary sorting device based on the friction properties were a tilting angle of 12° and a linear velocity of 0.4 m/s. Furthermore, the optimum design parameters of the directional integrated device were a tilting angle of 19° and a linear velocity of 0.45 m/s. The sorting speed was 400 soybeans per minute with 8-channel parallel transmission. The average sorting accuracies were 99.4% for split soybeans, 98.5% for worm-eaten soybeans, 98.5% for gray-spotted soybeans, 97.7% for slightly cracked soybeans, 98.6% for moldy soybeans, and 98.9% for normal soybeans. The overall results suggest that the system can potentially meet the needs of the rapid inspection and automatic sorting of soybean seeds and provide references for research on the alternating rotational motion of granules and on-line collection of full-surface information. Keywords: Embedded image processing technology, Full surface, Granules, Inspection, On-line, Sorting, Soybean seeds.
3D scanning is a crucial step to ensure the machining quality of the workpiece and is an essential part of intelligent manufacturing. However, existing scanning systems usually have only one profiler, which must be combined with a dynamic tracking system to achieve a complete scan of a workpiece. This scanning method has low efficiency and complicated path planning for ring-shaped workpieces. Therefore, in this article, an efficient and high-accuracy 3D scanning system is built, which is composed of a linear translation stage and four uniformly distributed laser profilers, and its extrinsic calibration method is studied. At first, based on the working parameters and spatial layout of multiple profilers, a stereoscopic calibrator composed of three non-collinear target balls (TBs) is designed. Then, a multi-profiler data fusion method is proposed, which utilizes a linear encoder to trigger the four profilers synchronously. Finally, by simultaneously using all data from the multiple profilers and the spherical constraint of each TB, all extrinsic parameters are accurately calibrated at the same time. Experimental results show that the average probing size error of the TB with a 38.1mm diameter is stable at about 0.007mm, and its extended uncertainty is about 0.100mm (k=2). In addition, standard cylinders and bend tubes are scanned, the results show that the proposed method can meet the high-accuracy calibration requirements of the tube bending deformation detection system.
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