Fixed-Time Image-Based Visual Servoing With Prescribed Performance

Visual features are often lost during fast servoing due to the limited field of view (FOV) of the camera. In this article, a method for robust fixed-time image-based visual servoing (IBVS) with prescribed performance is proposed to tackle the aforementioned problems. First, a performance function is introduced to preset performance specifications of the image feature errors. This way, the image feature points are kept in the FOV of the camera during the visual servoing process. Next, the implementation of performance constraints on image feature point errors is accomplished using a barrier Lyapunov function (BLF) to improve the steady-state accuracy. Finally, fast stabilization of the system is ensured by the fixed-time Lyapunov stability theorem. The proposed method is implemented for visual servoing of a robotic arm. The experimental results show that the convergence rate is significantly improved compared to the existing methods. The image feature points remain in the FOV of the camera during the fast servoing process, too. Additionally, a bolt visual servoing alignment example study is carried out to demonstrate the applicability of the proposed method in practice.