Proactive ride control systems have the potential to improve seakeeping effects for high-speed vessels. By enabling the vessel to adjust for waves it has not yet encountered, the vessel's seakeeping characteristics may be significantly improved, allowing it to reach higher speeds. This paper investigates the feasibility of a vision-based wave sensing approach that will be able to determine pertinent wave characteristics, such as slope, height, and frequency. A stereo vision-based system has been successfully applied to the in-situ measurement of laboratory water waves. Surface plots of the initial results from imaging regular sinusoidal waves at the Davidson Lab at Stevens Institute of Technology indicate excellent horizontal and vertical correlation with the generated wave characteristics. Validation of this data has been obtained by conventional wave probes mounted at two different locations in the tank

A dynamic quasi-Newton method for uncalibrated, vision-guided robotic tracking control with fixed imaging is developed and demonstrated. This method does not require calibrated kinematic and camera models. Robotic control is achieved at each step through minimizing a nonlinear objective function, by taking quasi-Newton steps and estimating the composite Jacobian at each step. The Jacobian is estimated using a dynamic recursive least-squares algorithm. Experimental results demonstrate the validity of this approach.

This paper presents uncalibrated control schemes for vision-guided robotic tracking of a moving target using a moving camera. These control methods are applied to an uncalibrated robotic system with eye-in-hand visual feedback. Without a priori knowledge of the robot's kinematic model or camera calibration, the system is able to track a moving object and maintain the desired features. These control schemes estimate the system Jacobian as well as changes in target features due to target motion. Four novel strategies are simulated, and a variety of parameters are investigated with respect to performance.

Target tracking and obstacle avoidance are demonstrated for uncalibrated visual servoing. An objective function is designed that encourages target following by a robotic end-effector while discouraging movements near an obstacle. The objective function incorporates the error between the target and the end-effector and a potential function related to the obstacle. This objective function is minimized using a dynamic nonlinear least squares optimization method in conjunction with a recursive least squares Jacobian estimation algorithm. The approach is generic and can be applied to a variety of systems. Calibration is unnecessary after a reconfiguration or disturbance to the robotic workcell. This type of control has the potential to provide a low-cost, low-maintenance automation solution for unstructured industries and environments. Experimental results demonstrate both target tracking and obstacle avoidance for an uncalibrated robotic system.

Tracking of a moving target by uncalibrated model independent visual servo control is achieved by developing a new "dynamic" quasi-Newton approach. Model independent visual servo control is defined as using visual feedback to control a robot without precisely calibrated kinematic and camera models. The control problem is formulated as a nonlinear least squares optimization. For the moving target case, this results in a time-varying objective function which is minimized using a new dynamic Newton's method. A second-order convergence rate is established, and it is shown that the standard method is not guaranteed convergence for a moving target. The algorithm is extended to develop a dynamic Broyden update and subsequently a dynamic quasi-Newton method. Results for both one- and six-degree-of-freedom systems demonstrate the success of the algorithm and shows dramatic improvement over previous methods.