With the increasingly extensive application of robots in industrial production, visual of the robotic arm has become an important research direction in the field of robots, which is also recognized as believed to widely considered to the most vital directions of modern high-tech development. In the robot vision system, the binocular visual system is most widely applied. When the robotic arm based on binocular vision captures the object, SURF algorithm is adopted to realize feature point extraction and matching of images. Because of the influence of the objective factors, there is false match in the process of SURF matching of feature points. In this paper, SURF algorithm is improved and reverse feature vectors matching strategy is used to initialize the matching points set. Then the RANSAC algorithm is adopted to select the initial matching points set in accordance of the polar geometric model to get the final good match set. The experiment results show that the improved SURF algorithm has higher registration accuracy and faster speed than SURF.

The nonlinear characteristics in the bicycle robot dynamic system make the bicycle robot control much more difficult. Aiming at this problem, the cascade sliding-mode control method was applied to the bicycle robot nonlinear system. A kind of bicycle robot nonlinear dynamic model presented based on Lagrange method was analyzed and the cascade sliding-mode controller of multi-sliding surface was designed with the method of variable structure control theory. This method divided system states into two sub-systems. Firstly, two state variables of subsystem are chosen to construct the first-layer sliding surface, on the basis of the first-layer sliding function, the first layer sliding surface and one of the residual state variables are used to construct the second-layer sliding surface, at last, all of the system states are included in final sliding surface. The ultimate control is obtained by using Lyapunov stability theorem. since each sub-sliding surface contains the equivalent components of sliding surface, which makes the system states move along the each sub-sliding surface. The cascade sliding-mode controller has the bicycle robot controlled effectively, which has been verified in the simulation experiment.

Peg-in-hole assembly, a crucial component of robotic automation in manufacturing, continues to pose challenges due to its strict tolerance requirements. To date, most conventional peg-in-hole assembly algorithms have been validated only within simulated environments or under limited observational scenarios. In this paper, an environment-agnostic coarse-to-fine visual servoing (EA-CTFVS) assembly algorithm is proposed. Firstly, to solve the frequent issue of visual blindness during visual servoing, a bottleneck pose is proposed to be used as the desired pose for the visual servoing. Secondly, to achieve accurate assembly, a coarse-to-fine framework is constructed, in which a rough pose is given by the coarse controller to remove large initial alignment errors. For the fine controller, a twin network-based fine controller is provided to improve assembly accuracy. Furthermore, EA-CTFVS utilizes the Oriented Bounding Box (OBB) of objects as the input for visual servoing, which guarantees the system’s ability to operate effectively in diverse and complex scenes. The proposed EA-CTFVS achieves a successful assembly rate of 0.92/0.89 for initial alignment errors of 15/30 cm and 0.6 mm tolerance in real-world D-sub plug assembly tasks under complex scenarios.