As a typical visual positioning system, monocular ranging is widely used in various fields. However, when the distance increases, there is a greater error. YOLOv8 network has the advantages of fast recognition speed and high accuracy. This paper proposes a method by combining YOLOv8 network recognition with a monocular ranging method to achieve target localization and grasping for the NAO robots. By establishing a visual distance error compensation model and applying it to correct the estimation results of the monocular distance measurement model, the accuracy of the NAO robot’s long-distance monocular visual positioning is improved. Additionally, a grasping control strategy based on pose interpolation is proposed. Throughout, the proposed method’s advantage in measurement accuracy was confirmed via experiments, and the grasping strategy has been implemented to accurately grasp the target object.

The NAO robot integrates sensors, vision systems, and control systems. Its monocular vision system is adopted to locate the target object in the three-dimensional space of robots. Firstly, a positioning model based on monocular vision is established according to the principle of small hole perspective. Then, the position coordinates of the target center are obtained in the image coordinate system. In the model mentioned above, the relationship between position coordinates and image coordinates is established at a certain space height. According to this relationship, the two-dimensional coordinates in the image are converted into the three-dimensional coordinates in the robot coordinate system. After getting the target location, we establish the navigation map and find the optimal path under the unknown environment. Based on the simultaneous localization and the mapping (SLAM) theory, the sonar sensor of the NAO robot is used to detect the distance between the robot and the obstacles or between the robot and the end landmark. Moreover, the sonar sensor and the camera are used to distinguish the obstacle and the landmark. After the navigation map is built, the bi-directional parallel search strategy and the simulated annealing algorithm are introduced to improve the traditional artificial bee colony algorithm, and the improved artificial bee colony algorithm is proposed to find an optimal path in the navigation map. Finally, the experimental results show that based on the built environment map, the robot can find an optimal path from the origin to the landmark on the premise of avoiding obstacles.

As a typical visual positioning system, monocular ranging is widely used in various fields. However, when the distance increases, there is a greater error. YOLOv8 network has the advantages of fast recognition speed and high accuracy. This paper proposes a method by combining YOLOv8 network recognition with a monocular ranging method to achieve target localization and grasping for the NAO robots. By establishing a visual distance error compensation model and applying it to correct the estimation results of the monocular distance measurement model, the accuracy of the NAO robot's long-distance monocular visual positioning is improved. Additionally, a grasping control strategy based on pose interpolation is proposed. Throughout, the proposed method's advantage in measurement accuracy was confirmed via experiments, and the grasping strategy has been implemented to accurately grasp the target object.

The inherent nonlinear hysteresis characteristics of giant magnetostrictive actuators are significantly influenced by the frequency of the input signal, and this rate-dependent effect poses severe challenges to high-precision modeling and control. To address this issue, this paper proposes an improved rate-dependent Prandtl–Ishlinskii (PI) model, which incorporates a rate-dependent envelope function with asymmetric left–right thresholds, and additional rate-dependent nonlinear terms to more accurately characterize the frequency-dependent hysteresis behavior of giant magnetostrictive actuators (GMAs). Based on this model, its inverse model is analytically constructed to achieve feedforward compensation, thereby substantially mitigating the influence of hysteresis nonlinearity. On this compensation basis, a sliding mode control strategy with prescribed performance is designed to guarantee global closed-loop stability, and to satisfy the predefined transient and steady-state performance on the tracking error. Finally, verification tests are conducted on a GMA experimental platform, and the results demonstrate that the proposed modeling and composite control scheme can effectively suppress rate-dependent hysteresis disturbances, achieving fast system response and desirable steady-state accuracy, thus validating the feasibility and engineering practicality of the method.