Uncalibrated Visual Servoing With Recursive Least Squares: Adaptive Control for Cable-Driven Parallel Robots

Uncalibrated visual servoing (UVS) aims to adaptively control robots without explicit calibration, promising performance in unstructured or dynamic environments. In principle, integrating recursive least squares to estimate the image Jacobian online can enhance tracking accuracy and resilience to uncertainties. This study implements an RLS-based UVS framework on a three-cable parallel robot with an eye-in-hand camera. Experiments and simulations compare the adaptive approach to a baseline using a static, globally calibrated Jacobian. While the RLS method demonstrated the ability to fine-tune the Jacobian over time, steady-state accuracy and convergence speeds were often similar or inferior to the static approach, particularly under large initial offsets or poor initial conditions. These findings highlight the sensitivity of adaptive UVS to initialization quality and the complexity of achieving practical gains in noisy, nonlinear settings. They underscore the need for improved strategies, such as refined initialization or hybrid methods, to fully realize the theoretical potential of RLS-based uncalibrated servoing in challenging real-world scenarios.