Most of the current visual Simultaneous Localization and Mapping (SLAM) algorithms are designed based on the assumption of a static environment, and their robustness and accuracy in the dynamic environment do not behave well. The reason is that moving objects in the scene will cause the mismatch of features in the pose estimation process, which further affects its positioning and mapping accuracy. In the meantime, the three-dimensional semantic map plays a key role in mobile robot navigation, path planning, and other tasks. In this paper, we present OFM-SLAM: Optical Flow combining MASK-RCNN SLAM, a novel visual SLAM for semantic mapping in dynamic indoor environments. Firstly, we use the Mask-RCNN network to detect potential moving objects which can generate masks of dynamic objects. Secondly, an optical flow method is adopted to detect dynamic feature points. Then, we combine the optical flow method and the MASK-RCNN for full dynamic points’ culling, and the SLAM system is able to track without these dynamic points. Finally, the semantic labels obtained from MASK-RCNN are mapped to the point cloud for generating a three-dimensional semantic map that only contains the static parts of the scenes and their semantic information. We evaluate our system in public TUM datasets. The results of our experiments demonstrate that our system is more effective in dynamic scenarios, and the OFM-SLAM can estimate the camera pose more accurately and acquire a more precise localization in the high dynamic environment.
Aiming at decreasing the component complexity and cost of flower transplanting machine, an integrated transplanting method for picking and planting flower seedlings was proposed, and a hybrid-driven five-bar parallel mechanism was designed. A "beak-shaped" trajectory was designed for integrated transplanting requirements, and meantime, either the posture requirements of transplanting claw were determined. Based on the transplanting trajectory of the mechanism, a corresponding mathematical model for solving the link parameters was established, and then the five-bar mechanism was divided into two bar groups, optimization was conducted in two steps based on genetic algorithm and NSGA-II algorithm. Consequently, the optimal solution of the hybrid-driven five-bar parallel mechanism for flower seedling transplanting was obtained. Compared with similar designs, the trajectory displacement of the proposed mechanism is larger in the condition of smaller link size, which indicates that the mechanism can effectively decrease the machine size. The real-time controllable motor angular acceleration fluctuation is smaller and the commutation times are less, which has the advantage of reducing the difficulty of the mechanism control system. Subsequently, the correctness of the design method is verified by kinematics simulation. Finally, the synchronous linkage motion control methods of the two motors were designed, a transplanting experiment of the prototype was carried out, the picking success rate had reached 90%-93.4% and transplanting success rate was 80.5%-86.9% during experiment, which showed that the integrated operation of picking and planting flower seedlings can be realized by the proposed mechanism.
Mature broccoli has large flower balls and thick stems. Therefore, manual broccoli picking is laborious and energy-consuming. However, the big spheroid vegetable-picking manipulator has a complex structure and poor enveloping effect and easily causes mechanical damage. Therefore, a broccoli flower ball-picking manipulator with a compact structure and simple control system was designed. The manipulator was smart in structure and stable in configuration when enveloped in flower balls. First, a physical damage test was carried out on broccoli according to the underactuated manipulator’s design scheme. The maximum surface pressure of the flower ball was 30 N, and the maximum cutting force of the stem was 35 N. Then, kinematic analysis was completed, and the statical model of the underactuated mechanism was established. The dimension of the underactuated mechanism for each connecting rod was determined based on the damage test results and design requirements. The sizes of each connecting rod were 50 cm, 90 cm, 50 cm, 90 cm, 50 cm, 60 cm, and 65 cm. The statical model calculated the required thrust of the underactuated mechanism as 598.66–702.88 N. Then, the manipulator was simulated to verify its reliability of the manipulator. Finally, the manipulator’s motion track, speed, and motor speed were determined in advance in the laboratory environment. One-hundred picking tests were carried out on mature broccoli with a 135–185 mm diameter. Results showed that the manipulator had an 84% success rate in picking and a 100% lossless rate. The fastest single harvest time in the test stand was 11.37 s when the speed of the robot arm was 3.4 m/s, and the speed of the stepper motor was 60 r/min.
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