Antonio C. Leite

Adaptive visual servoing scheme free of image velocity measurement for uncertain robot manipulators

This work addresses the visual tracking problem of robot manipulators with non-negligible dynamics using a fixed camera, when the camera-robot system parameters are uncertain. An adaptive strategy is developed for visual servoing systems based on the image-based look-and-move structure to allow the tracking of a 2D reference trajectory, without using image velocity measurements. The adaptive visual servoing problem free of image velocity information is formulated as a relative degree two MIMO adaptive control problem. As a solution, we employ a recently proposed Lyapunov/passivity-based adaptive control scheme based on the SDU factorization method. From a cascade control strategy, the resulting online camera calibration scheme is combined with a direct adaptive motion controller for the robot manipulator, which takes into account its uncertain nonlinear dynamics. The overall stability of the adaptive visual servoing system can be proved thanks to the explicit Lyapunov-like functions of both adaptation schemes.

Hybrid Adaptive Vision-Force Control for Robot Manipulators Interacting with Unknown Surfaces

A hybrid control scheme based on adaptive visual servoing and direct force control is proposed for robot manipulators to perform interaction tasks on smooth surfaces. The camera parameters, as well as the constraint surface, are considered to be uncertain. A fixed camera with optical axis non-perpendicular to the robot workspace is used for position control, while a force sensor mounted on the robot wrist is used for force regulation. In order to solve the interaction problem on unknown surfaces, a method is developed to estimate the constraint geometry and keep the end-effector orthogonal to the surface at the contact point, during the task execution. Experimental results are presented to illustrate the performance and feasibility of the proposed scheme.

Sliding mode control of uncertain multivariable nonlinear systems applied to uncalibrated robotics visual servoing

An output-feedback sliding mode controller using monitoring functions was recently introduced for linear uncertain single-input-single-output (SISO) systems with unknown control direction. Here, a generalization is developed for multivariable systems with strong nonlinearities. The monitoring scheme is extended to handle the uncertainty of the plant high frequency gain matrix Kp. Our strategy provides global stability properties and exact output tracking. Experimental results with a robotics visual servoing system, using a fixed but uncalibrated camera, illustrate the robustness and practical viability of the proposed scheme.

Hybrid vision-force robot control for tasks on unknown smooth surfaces

This work considers a hybrid force and vision control system for robotic manipulators using a force sensor and a fixed uncalibrated camera. A method is proposed to combine direct force control and adaptive visual servoing to perform tasks on unknown smooth surfaces, in the presence of uncertainties in the camera-robot system parameters. The considered task involves the visual tracking of a moving target, while the end-effector tip exerts a controlled contact force on the surface. Simulation results are presented to illustrate the performance of the proposed scheme

Adaptive 3D Visual Servoing without Image Velocity Measurement for Uncertain Manipulators

Abstract This work addresses the 3D visual servoing problem for robot manipulators in the presence of uncertainties in the camera-robot system parameters. From a fixed and uncalibrated camera, the cartesian motion is decomposed into a 2D motion on a plane orthogonal to the optical axis and a 1D motion parallel to this axis. An adaptive visual servoing scheme based on a kinematic approach is developed for image-based look-and-move systems to allow both depth and planar tracking of a reference trajectory, without using image velocity and depth measurements. A cascade control strategy based on direct/indirect adaptive method is applied to consider the uncertain robot kinematics and dynamics in the presented solution. The stability properties and the convergence analysis are discussed from the Lyapunov-passivity formalism. Simulation results are shown to illustrate the performance of the proposed control scheme.

Analysis of a moving remote center of motion for robotics-assisted minimally invasive surgery

This paper presents a novel control architecture for controlling a moving remote center of motion in addition to the end-effector motion during robotic surgery. In minimally invasive surgery, it is common to require that the point at which the robot enters the body, called the incision point or the trocar, does not allow for any lateral motion. It is generally considered that no motion should be applied to this point in order to avoid inflicting damage to the patients skin. However, in surgery, the patients body may be moving, for example due to breathing or the beating of the heart. In order to compensate for this motion-or if we for some other reason want to leverage the possible motion of the incision point to improve performance in any other way-we derive a new framework which allows us to actively control the motion both at the incision point and the end effector. The novelty of the approach lies in the possibility of controlling both the incision point and the end effector to follow a trajectory, and that we find a Jacobian matrix that satisfies the velocity constraints in both the end-effector and the incision point frames. This allows us to formulate a framework that is not only suited for control, but also for analyzing the condition number of the Jacobian and avoid any singular configurations that may arise either as a result of the constrained motion or the manipulator geometry. The approach is verified experimentally on a redundant industrial manipulator.

Overcoming Kinematic Singularities with the Filtered Inverse Approach

Abstract In this work, we propose a solution to the inverse kinematics problem based on the differential kinematics and on a recently proposed algorithm which estimates the inverse of the Jacobian matrix dynamically. The output of the algorithm can be interpreted as a filtered inverse (FI) of the Jacobian matrix. An interesting property of the FI algorithm is its ability to cope with kinematic singularities. The update law of the estimator is driven by error signals that consider both the left and the right inverse matrices, thus enabling trajectory tracking and minimization of the control effort simultaneously. This paper shows that the FI algorithm can be applied to a Jacobian matrix augmented with additional constraints, which allows for setting the priority or weight to different control objectives, such as obstacle avoidance. Simulation results are presented to illustrate the performance and feasibility of the proposed solution.

Kinematic control of constrained robotic systems

Este artigo considera o problema de controle de postura para sistemas roboticos com restricoes cinematicas. A ideia principal e considerar as restricoes cinematicas dos mecanismos a partir de suas equacoes estruturais, ao inves de usar explicitamente a equacao de restricao. Um estudo de caso para robos paralelos e robos cooperativos e discutido baseado nos conceitos de cinematica direta, cinematica diferencial, singularidades e controle cinematico. Resultados de simulacao, obtidos a partir de um mecanismo Four-Bar linkage, uma plataforma de Gough-Stewart planar e dois robos cooperativos, ilustram a aplicabilidade e versatilidade da metodologia proposta.

Kinematic control of robot manipulators using filtered inverse

This paper presents a kinematic control scheme for robot manipulators based on an algorithm that dynamically estimates an inverse of the Jacobian matrix. An interesting property of this algorithm is its ability to deal with the problem of kinematic singularities. The output of the algorithm can be interpreted as the filtered inverse of the Jacobian matrix. A case study of a 3-DoF non-redundant manipulator is presented. Some simulation results illustrate the performance of the proposed methodology.

Adaptive control of nonlinear visual servoing systems for 3D cartesian tracking

This paper presents a control strategy for robot manipulators to perform 3D cartesian tracking using visual servoing. Considering a fixed camera, the 3D cartesian motion is decomposed in a 2D motion on a plane orthogonal to the optical axis and a 1D motion parallel to this axis. An image-based visual servoing approach is used to deal with the nonlinear control problem generated by the depth variation without requiring direct depth estimation. Due to the lack of camera calibration, an adaptive control method is used to ensure both depth and planar tracking in the image frame. The depth feedback loop is closed by measuring the image area of a target object attached to the robot end-effector. Simulation and experimental results obtained with a real robot manipulator illustrate the viability of the proposed scheme.