Emmanuel C. Dean-Leon

Visual servoing for constrained planar robots subject to complex friction

The theoretical framework and the experimental validation of a new image-based position-force control for planar robots are presented in this paper. This scheme produces simultaneous convergence of the constrained visual position and the contact force between the end effector and the constraint surface. Camera, robot, and the visual jacobian parameters are considered unknown. This approach is based on a new formulation of the orthogonalization principle used in the robot force control, termed the visual orthogonalization principle. This allows, under the framework of passivity, to yield a synergetic closed-loop system that fuses accordingly camera, encoder, and the force sensor signals. Furthermore, due to the technological limitations, it can be noticed that the visual servoing contact tasks are characterized by slow motion, typically with frequent velocity reversals along the constraint surface, thus, important friction problems arise at the joints and the contact points. Therefore, visual compensation of the complex dynamic joint friction and the viscous contact friction are also studied. A Linux real-time operating-system-based experimental system is implemented to visually drive a constrained direct-drive planar robot manipulator, equipped with six-axes JR3 force sensor and a digital fixed camera, thus proving the effectiveness of the proposed scheme

Dynamical image-based PID uncalibrated visual servoing with fixed camera for tracking of planar robots with a heuristical predictor

Based on a recent state-of-the-art image-based visual feedback control scheme in fixed uncalibrated camera proposed by the authors, the experimental verification is presented. However, image acquisition and processing for closed-loop control introduces technological challenges through the time delay induced by the CCD camera itself. Thus, to preserve the time domain algorithm and to avoid discretization of our nonlinear controller, a practical predictor is introduced to obtain rough estimates of the image at the bandwidth of the robot. The nonlinear dynamics of the planar robot is considered for the stability analysis. Exponential tracking arises due to a chattering-free visual sliding mode using PID controller based on image error trajectories. The experimental setup describes details of the implementation of our previous theoretical results using a fire wire fixed camera, and a direct drive robot for desired image trajectories. All camera and robot parameters are unknown, and experimental results confirm the theoretical results

Uncalibrated image-based position-force adaptive visual servoing for constrained robots under dynamic friction uncertainties

Visual servoing of constrained dynamical robots has not yet met a formal treatment. Also, notices that due technological constraints, this task is done slowly at velocity reversals, thus dynamic friction arises, which complicates even more the problem. In this paper, a new adaptive scheme for visual servoing of constrained robots subject to dynamic friction is proposed. An image-based control is introduced to produce simultaneous convergence of the constrained visual position and the contact force between the end-effector and the constraint surface. Camera and robot parameters are considered uncertain. This new approach is based on a new formulation of the orthogonalization principle used in force control, coined here visual orthogonalization principle. This allows, under the framework of passivity, to yield a synergetic scheme that fuses camera, encoder and force sensor signals. Simulation results are presented and show that image errors and force errors converge despite uncertainties of friction model.

Global Uncalibrated Visual Servoing for Constrained Robots Working on an Uncalibrated Environments

This paper studies the open problem of uncalibrated visual servoing for constrained robots with parametric uncertainties, interacting with unknown environments. A new theoretical framework and its experimental validation are given. This approach produces global exponential convergence of the constrained visual position and the contact force between the end-effector and the unknown constrain surface. This approach is based on the formulation of the visual orthogonalization principle (VOP), which, under the framework of passivity, yields a closed-loop system that fuses camera, encoder and force sensor signals. An experimental system running on Linux-RTAI OS is implemented to visually drive a constrained direct-drive robot manipulator, equipped with six axis JR3 force sensor and a FireWire CCD digital camera posed in fixed configuration. This experimental system proves the effectiveness of the proposed approach even when the robot, camera and restriction surface parameters are assumed as unknown

Visual servoing for constrained robots: a new complete theoretical framework and its experimental validation

The theoretical framework and experimental validation of a new image-based position-force control is presented in this paper. This scheme produces simultaneous convergence of the constrained visual position and the contact force between the end-effector and the constraint surface. Camera, robot and Jacobian parameters are considered uncertain. This approach is based on a new formulation of the orthogonalization principle used in force control, coined here visual orthogonalization principle. This allows, under the framework of passivity, to yield a synergetic scheme that fuses accordingly camera, encoder and force sensor signals. Furthermore, notice that visual servoing contact tasks are characterized by slow motion, and typically with velocity reversals along the constrained surface due actual technological limitations of the camera, thus, important problems of friction at the joint and contact point arise. Therefore, in this paper, compensation of dynamic joint friction and viscous contact friction are also studied. In order to prove the effectiveness of the theoretical scheme, a Linux-RTAI real-time OS experimental system is used to obtain a direct-drive robot manipulator equipped with six axis JR3 force sensor and a CCD commercial digital fixed camera. Results show an excellent performance

Experimental study on image-based position-force adaptive visual servoing for constrained robots under Jacobian and dynamic friction uncertainties

In this paper an image-based control is introduced to produce simultaneous convergence of the constrained visual position and contact force between the end-effector and the constraint surface. Camera, robot and Jacobian parameters are considered uncertain. This new approach is based on a new formulation of the orthogonalization principle used in force control, coined here visual orthogonalization principle. This allows, under the framework of passivity, to yield a synergetic scheme that fuses camera, encoder and force sensor signals. However, when complex friction arises, visual servoing suffers to drive the robot to the desired trajectories, in particular in slow motion and velocity reversals, which are typical motions in this sort of control schemes. Moreover, dynamic friction is usually neglected in motion control and it is not the exception in visual servoing literature. In our case, we explore an interesting result to compensate for uncertain dynamic friction. In order to prove the theory described in this article, the real-time OS, Linux-RTAI, is used to obtain experimental results of this controller on a direct-drive robot manipulator equipped with six axis JR3 force sensor. Results suggest its excellent performance.

Image-based adaptive visual servoing of 2D robots under Jacobian and dynamic friction uncertainties: theory and experiments

A globally convergent visual feedback control scheme is proposed for dynamical planar robot arms subject to uncertain camera, robot, analytical Jacobian and dynamic friction parameters. When complex friction arises, visual servoing suffers to drive the robot to the desired trajectories, in particular in slow motion and velocity reversals, which are typical motion regime in visual servoing due to the vision system properties. Moreover, dynamic friction is usually neglected in motion control and it is not the exception in visual servoing literature. In order to prove the theory described in this paper, the real-time OS, Linux-RTAI, is used to obtain experimental results of this controller on a direct-drive robot manipulator. Results suggest its excellent performance.

ADAPTIVE VISUAL SERVOING FOR CONSTRAINED ROBOTS UNDER JACOBIAN, JOINT DYNAMIC AND CONTACT VISCOUS FRICTION UNCERTAINTIES

Abstract Visual servoing of constrained robots has not yet met a formal treatment nor its friction compensation. This kind of robots moves slowly along the constrained surface due to technological limitations of the camera system, therefore important problems of friction at the joint and contact point arise. The problem turns very complicated when parametric uncertainty on robot, camera and friction is considered. In this paper, a new visual servoing scheme that satisfy this problem is presented. It induces sliding modes without chattering to guarantee locally exponential convergence of tracking errors. Simulations results are presented and discussed.

Orthogonalization Principle for Dynamic Visual Servoing of Constrained Robot Manipulators

A monocular visual servoing scheme for constrained robots is considered in this chapter. Inspired by the Orthogonalization Principle (OP) introduced by Suguru Arimoto in the context of robot force control, a Visual Orthogonalization Principle (VOP) is proposed and a novel control scheme for adaptive image-based visual servoing is presented. The scheme guarantees a global exponential convergence for the image-based position-velocity and contact forces even when the robot parameters are considered unknown. The stability of the new control scheme is tested under experiments. The experimental results comply to the theoretical considerations.