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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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.

This paper presents aspects related to the generation and tracking of closed trajectories over an arbitrary surface of unknown geometry. Such a capacity is required in some of the most important industrial robotic tasks, like the cutting or welding of commercial plates. Previous work has shown how a calibration-free, vision-based method can be combined with an optimal geodesic-mapping approach, in order to generate an optimal trajectory that traces a previously defined path, stored as a CAD model, over an arbitrary, curved surface. The application of this technique in the case of nondevelopable surfaces did not achieve closure when a closedtrajectory was attempted. This paper presents a methodology for successfully achieving closure of a given closed-path, when this is traced over a non-developable surface. The proposed technique was tested using an industrial robot and a visionbased system that included structured lighting for imageanalysis simplification. The results of these experiments are reported herein.

Current industrial robot-programming methods require, depending on the task to be developed, an elevated degree of technical ability and time from a human operator, in order to obtain a precise, nonoptimal result. This correspondence paper presents a methodology used to generate an optimal sequence of robot configurations that enable a precise point-allocating task applicable, for instance, to spot-welding, drilling, or electronic component placement maneuvers. The optimization process starts from a nonoptimal, initial sequence designated intuitively by a human operator using an easy-to-use interface. In this correspondence paper, intuitive programming is considered as the process of defining, in a computer graphics environment and with a limited user knowledge of robotics or the industrial task, the sequence of motions that enable the execution of a complex industrial robotic maneuver. Such an initial sequence is later followed by a robot, very precisely, using a vision-based, calibration-free, robot control method. Further robot path optimization is performed with a genetic algorithm approach. An industrial robot, which is part of the experimental setup, was used in order to validate the proposed procedure.