We propose a novel implementation of visual servoing whereby a human operator can guide a robot relative to the coordinate frame of an eye-in-hand camera. Among other applications, this can allow the operator to work in the image space of the eye-in-hand camera. This is achieved using a gamepad, a time-of-flight camera (an active sensor that creates depth data), and recursive least-squares update with Gauss-Newton control. Contributions of this paper include the use of a person to cause the control action in a visual-servoing system, and the introduction of uncalibrated position-based visual servoing. The system's efficacy is evaluated via trials involving human operators in different scenarios.

The applications of robotics are becoming more and more common in non-traditional industries such as the medical industry including robotic surgery and sample microtoming as well as food industry that include the processing of meats, fruits and vegetables. In this paper, the influence of the blade edge-shape and its slicing angle on the cutting of biomaterials are formulated and discussed based on the stress analysis that has been presented in part I. Through modeling the cutting force, an optimal slicing angle can be formulated to maximize the feed rate while minimizing the cutting forces. Moreover, the method offers a means to predict cutting forces between the blade and the biomaterials, and a basis for design of robust force control algorithms for automating the cutting of biomaterials

Bio-material cutting, such as meat deboning, is one the most common operations in food processing. Automating this process using robotic devices with closed-loop force control has shown some promise. The control of the force trajectory directly relates to the internal stress in the material being cut, and must provide enough force to initiate the cut. The ability to model the stress distribution in the bio-materials being cut would provide a better understanding of the influencing factors and help predict the required cutting force for the design of the cutting mechanism and for automating the cutting operations. This research is presented in two parts: part I models the stress distribution when a blade acts on the bio-material and part II discusses the principles of biomaterial cutting. Starting with modeling a point force in the normal and tangential direction on the boundary of a semi-infinite body, an analytical expression for the stress tensor has been obtained and simulated using direct integral method. This paper provides the theoretical basis for explaining the cutting phenomena and predicting the cutting forces, a topic to be presented in part II

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.

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.