This paper presents an interaction control strategy for industrial robot manipulators which consists of the combination of a calibration-free, vision-based control method with an impedancecontrol approach. The vision-based, robot control method known as camera-space manipulation is used to generate a given, previously defined trajectory over an arbitrary surface. Then, a kinematic impedance controller is implemented in order to regulate the interaction forces generated by the contact between the robot end-effector and the work surface where the trajectory is traced. The paper presents experimental evidence on how the vision-force sensory fusion is applied to a path-tracking task, using a Fanuc M16-iB industrial robot equipped with a force/torque sensor at the wrist. In this task, several levels of interaction force between the robot end-effector and the surface were defined. As discussed in the paper, such a synergy between the control schemes is seen as a viable alternative for performing industrial maneuvers that require force modulation between the tool held by the robot and the working surface.

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A saturating stiffness control scheme for robot manipulators with bounded torque inputs is proposed. The control law is assumed to be a PD-type controller, and the corresponding Lyapunov stability analysis of the closed-loop equilibrium point is presented. The interaction between the robot manipulator and the environment is modeled as spring-like contact forces. The proper behavior of the closed-loop system is validated using a three degree-of-freedom robotic arm.

The inertial and friction parameters of a robot are used in the development and evaluation of modelbased control schemes and their accuracy is related directly to the performance. These parameters can also be used for a realistic simulation, which may be useful before implementation of new control schemes. In principle, the numerical value of the parameters could be obtained via CAD analysis, but inevitably assembly and manufacturing errors exist. Direct measurement is not a realistic option because the complex nature of the system involves intense, time-consuming effort. Alternatively, we can deduce the values of the parameters by observing the natural response of the system under appropriate experimental conditions, i.e., by using identification schemes, which is an efficient way. This paper presents the experimental evaluation of five identification schemes used to obtain the inertial and friction parameters of a three-degrees-of-freedom direct-drive robot. We assume that the inertial and friction parameters are totally unknown but, by design, the dynamic model is fully known, as in many practical cases. We consider the schemes based on the dynamic regression model, the filtered-dynamic regression model, the supplied-energy regression model, the power regression model and the filtered-power regression model. This paper presents a comparison between experimental and simulated robot response, which enables us to verify the accuracy of each regression model.

This paper presents a control strategy for industrial robot manipulators which consists of the combination of a calibration-free, vision-based control method with an impedance control approach. The vision-based, robot control method known as camera-space manipulation is used to generate a given trajectory over an arbitrary surface. Then, a kinematic posture-based impedance controller is implemented in order to regulate the interaction forces generated by the contact between the robot end-effector and the work surface where the trajectory is traced. The paper presents experimental evidence on how the vision-force sensory fusion improves the precision of a robot-interaction task, by using a Fanuc M16-iB industrial robot equipped with a wrist force/torque sensor. As discussed in the paper, such a synergy between the control schemes is seen as a viable alternative for performing industrial maneuvers that require force modulation between the tool held by the robot and the working surface.