Robust robotic visual servoing for uncertain systems

The control of robotic manipulators in unstructured environments is a challenging task. Exploiting the camera images for that purpose, known as visual servoing, offers an interesting solution to the problem. Classic visual servoing techniques were the first attempts towards this goal. However, these methods suffered from system’s shortcomings such as ones imposed by the limited camera’s field of view and the robot’s reachability. Numerous approaches were proposed to overcome these limitations. Nevertheless, most of these techniques assumed full knowledge about the system and did not account for uncertainties. Uncertainties in visual servoing systems are introduced by multiple sources such as camera image noise and robot parameters. The lack of knowledge about system’s parameters may lead to reduced accuracy or even total failure. Adaptive techniques were introduced previously to cope with this matter. However, those techniques were usually useful for deterministic uncertainties (e.g., camera calibration errors). Alternately, robust methods were employed to improve the performance of the system under uncertainties. Yet, those methods were usually conservative and more concerned with the stability of the system, rather than its accuracy. This work proposes three steps towards robust and accurate visual servoing. First, the pose estimation algorithm, used in many visual servoing systems, is revised by introducing novel sensor fusion techniques. Multiple fusion algorithms at different levels of estimation are introduced to enhance the accuracy and robustness of the estimation against system uncertainties. Second, a novel uncertainty estimation technique is presented to approximate the level of uncertainties induced by image noise at different levels of the system. A general approach is used for that matter which has applicability over a wide range of controllers. Finally, multiple constraint-aware and robust controllers with improved stability and numerical feasibility are proposed to enhance the performance of the visual servoing systems in presence of uncertainties. The developed uncertainty model is exploited in robust control design. The effectiveness of each proposed technique is verified through numerous simulations and experiments. As it is expected, the proposed methods are capable of handling the uncertainties and enhancing the accuracy, while accounting for system constraints.