Constrained Image-Based Visual Servoing With a Sampling-Based Planning Framework

Visual servoing is established as a theoretically reliable scheme for achieving high-precision robotic control. However, various image and physical constraints inevitably limit the application of visual servoing methods. This manuscript proposes a controller based on quadratic programming with proven local stability, explicitly handling some dominant constraints. Other difficult-to-model constraints, such as collisions, are addressed using an efficient sampling-based bidirectional planning framework designed for the visual servoing problem. Considering constraints, the proposed method explores the permissible image space by progressively expanding two search trees and ultimately generates feasible image trajectories. Subsequently, the quadratic programming-based local controller is extended to the tracking problem, aiming to track the planned trajectory and guide the manipulator to reach target features. The effectiveness of the proposed methods is demonstrated through various simulated and real experiments with a seven-degree-of-freedom manipulator. Comprehensive comparative experiments against several related methods highlight the performance of the proposed method.