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In this work, we present a new robust vision-based controller for wheeled mobile robots, equipped with a fixed monocular camera, to perform autonomous navigation in agricultural fields accurately. Here, we consider the existence of uncertainties in the parameters of the robot-camera system and external disturbances caused by high driving velocities, sparse plants, and terrain unevenness. Then, we design a robust image-based visual servoing (rIBVS) approach based on the sliding mode control (SMC) method for robot motion stabilization, even under the presence of such inaccuracies and perturbations. The vision-based controller, based on column and row primitives, is slightly modified to include a robustness term into the original feedback control laws to ensure successful row crop reaching and following tasks. We employ the Lyapunov stability theory to verify the stability and robustness properties of the overall closed-loop system. 3D computer simulations are carried out in the ROS-Gazebo platform, an open-source robotics simulator, using a differential-drive mobile robot (DDMR) in an ad-hoc developed row crop environment to illustrate the effectiveness and feasibility of the proposed control methodology.

Nowadays, legged mobile robots have increased the interest of the robotics community because such mechanisms have higher versatility and autonomy compared to wheeled mobile robots. Although single-leg or multi-leg mechanisms can cross any terrain, some disadvantages are related to their increased complexity in mechanical design, modeling and control, and higher power consumption. A first case study for the balance and motion planning problem is the hopping robot, which is a nonholonomic system whose motion dynamics of each hopping cycle can be split into flight and stance phases. In this work, we consider the modeling and control design of a one-legged hopping robot in the flight phase by using the sliding mode approach, due to its well-known ability to deal with parametric uncertainties and nonlinear disturbances. Then, two nonlinear controllers are designed and implemented to automatically stabilize the robot joints during the flight in the presence of perturbations caused by the neglected high-order nonlinear terms in the modeling process, unmodeled dynamics and measurement noise. The Lyapunov stability theory is used to demonstrate the stability and robustness properties of the overall closed-loop control systems. Numerical simulations and a comparative analysis are provided to illustrate the performance and feasibility of the proposed control methodology.

This work presents a new robust image-based visual servoing (rIBVS) approach for wheeled mobile robots (WMRs) endowed with a single monocular camera to carry out autonomous navigation in row crop fields. Then, we design a robust vision-based controller by using the super-twisting algorithm (STA) approach to stabilize the robot motion in the presence of model inaccuracies caused by imperfect camera calibration, and trajectory perturbations due to different plant distributions and high robot driving velocities. The rIBVS approach switches between column and row visual primitives extracted from the images, allowing WMRs to execute the navigation task in two phases: the crop row reaching and the crop row following. To illustrate the effectiveness and feasibility of the proposed control methodology, 3D computer simulations are executed in the ROS-Gazebo simulator using a differential-drive mobile robot (DDMR) navigating autonomously in an ad-hoc developed row crop agricultural environment.

This paper presents a simulation study of the balancing problem for a monopod robot in which the lower body (the leg) has been modified to include a passively spring-loaded prismatic joint. Such a mechanism can move by hopping but can also stand and balance on a single point. We aim to investigate the extent to which a balance controller can deal with the large values and rapid changes in the spring-damper forces, while controlling the absolute positions and orientations of its parts and balancing on one leg. It can be shown that a good performance is achieved if the spring-loaded joint is instrumented and calibrated so that its position and velocity, as well as the stiffness and damping coefficients, are considered when calculating the controller state variables. We also demonstrate the effectiveness of the balance controller by adding a high-order sliding mode (HOSM) observer based on the finite-time algorithm for robust parameter estimation of the stiffness and damping coefficients. The stability analysis and convergence proofs are presented based on the Lyapunov stability theory. Numerical simulations are included to illustrate the performance and feasibility of the proposed methodology.

In farming systems, harvesting operations are tedious, time- and resource-consuming tasks. Deploying a fleet of autonomous robots to work alongside farmworkers may provide vast productivity and logistics benefits. In this context, an intelligent robotic system should monitor human behavior, identify the ongoing activities and anticipate the worker's needs. Unlike other application areas, such as warehouses and factories, research on human behavior recognition in agriculture is still in its infancy and has few case studies. Thus, there is a need for developing a fully integrated human activity recognition (HAR) methodology applied for agricultural operations in production fields. In this work, the main contribution consists of creating a benchmark framework of video-based human pickers detection, classifying their activities and corresponding motion direction, to serve in harvesting operations in different agricultural scenarios. Our solution uses the combination of a Mask Region-based Convolutional Neural Network (Mask R-CNN) for object detection and optical flow for motion estimation with a newly added statistical attribute of flow motion descriptors, named as Correlation Sensitivity (CS). A classification criterion is defined based on the analysis of the Kernel Density Estimation (KDE) technique and the K-Means clustering algorithm. Both methods are evaluated upon in-house collected datasets from different environments like strawberry polytunnels and apple tree orchards. The proposed benchmark framework is quantitatively analyzed using measures of sensitivity, specificity, and accuracy and shows satisfactory results amidst various dataset challenges such as multi-foreground objects, lighting variation, blur, and occlusions.

The development and integration of Wire Arc Additive Manufacturing (WAAM) systems is nowadays a topic of growing interest. Industries are starting to focus on deploying this technology due to its vast capability of producing different types of parts and creating new possibilities for engineering design. Measuring the process characteristics is crucial in a WAAM system, because it helps to ensure that the build up was done according as planned. In this work, it was developed a passive vision-based monitoring method to measure the metal bead width in a WAAM process. The deposition was carried out by using a carbon steel wire with GMAW process, a Motoman HP20 robot arm and a welding torch device. A Xiris XVC-1000camera was used for visual inspection and mounted in a suitable configuration to minimize arc's noise. The experimental results show that it is possible to measure the bead deposition width in real time with a satisfactory accuracy using monocular cameras. Therefore, it is a feasible solution to be used in WAAM systems with the advantage of being relatively low-cost, as compared to other active vision equipment.

This paper presents a novel 3-DoF parallel robot arm designed for use in applications that demand a high number of densely mounted manipulators with overlapping workspaces. The presented arm, which is mounted on a single rail, has such a workspace that allows the mechanism to reach outside the transverse footprint of its rail. Neighboring arms may therefore have overlapping workspaces on both sides and can be placed very close together, a property that is usually not found in parallel robots. The workspace of the arm in the direction of the rail is only limited by the length of the rail itself, and can therefore be made arbitrarily long. This makes the arm a light-weight alternative to traditional gantry systems. The main motivation is to increase the number of arms that can be mounted on a restricted area compared to conventional robots. The arm is applied to the agricultural domain where we show that it is well suited for tasks like robotic weeding and harvesting, i.e., cases where a high number of arms working in parallel is beneficial and can increase efficiency of the operations substantially. A kinematic analysis of the arm in terms of workspace and singularities is presented.

In this work, it is considered an image based visual servoing control problem, for uncertain robot manipulators. Visual feedback is provided by a fixed monocular camera with uncertain parameters, for the purpose of tracking translational trajectories of a spherical target, both the trajectory on image plane and depth. Based on a cascade structure, the proposed adaptive visual servoing is combined with an adaptive motion control strategy. Stability and passivity properties are analyzed with the Lyapunov method. Simulation results illustrate and highlight performance of the proposed controller.

In this work, we consider a novel adaptive hybrid pose/force control strategy for uncertain robot manipulators capable of performing interaction tasks on poorly structured environments. A unique hybrid control law, based on an orientation-dependent term, is proposed to overcome the performance degradation of the feedback system due to the presence of uncertainties in the geometric parameters of the contact surfaces. A gradient-based adaptive law, which depends on the tracking error, is designed to deal with parametric uncertainties in the robot kinematics and the stiffness of the environment. In our solution, the effect of the uncertain robot dynamics is addressed by using an adaptive dynamic control based on a cascade control strategy. The Lyapunov stability theory and the passivity paradigm are employed to carry out the stability analysis of the overall closed-loop control system. Numerical simulations are included to illustrate the performance and feasibility of the proposed methodology.

Agriculture is considered the economic basis of countries around the globe, and the development of new technologies contributes to the harvesting efficiency. Autonomous vehicles are used in farms for seeding, harvesting and tasks like pesticide application. However, one of the main issues of any plantation is insect pest and disease identification, essential for pest control and maintenance of healthy plants. This work presents and compares three methods for insect pest identification using computer vision: Deep Convolutional Neural Network (DCNN), as a baseline; Hierarchical Deep Convolutional Neural Network (HD-CNN), in order to improve prediction of similar classes; and Pixel-wise Semantic Segmentation Network (SegNet). They were tested for two kinds of culture, soybean and cotton. SegNet outperformed both approaches by a wide margin: the methods had respective accuracies of 70.14% DCNN, 74.70% HD-CNN and 93.30% SegNet.

In recent years, the use of mobile robots in agriculture has increased significantly because of their capability to carry out agricultural tasks in a safe and efficient manner, with limited or without human intervention. Crop monitoring and inspection have become an important part of precision agriculture, supporting farmers in the management of insect pests, weeds and diseases in order to reduce costs and losses. In this work, we present the design and development of an autonomous mobile robot, conceived to perform routine monitoring and inspection tasks on soy and cotton crops. The robot design is similar to a differential-drive vehicle due to its simplicity of construction, modeling and control. The autonomous navigation is successfully carried out via the use of odometry and cameras properly attached to the robot structure. Preliminary field tests with the prototype operating on a cotton farm are presented to show the performance and feasibility of the electro-mechanical design for navigation in row crops.

In this work, we address the modelling and control design of a wheeled mobile robot capable of performing autonomous navigation tasks in agricultural fields. The methodology is based on the kinematics approach due to its wellknown ability to ensure satisfactory performance when the robot motions are carried out with low velocities and slow accelerations. Two control strategies are used for stabilization and trajectory tracking purposes: the first scheme is based on a fuzzy logic algorithm and considers the regulation problem of the robot position in Cartesian space; the second scheme is based on a visual servoing algorithm and considers the tracking problem of a straight line by using a constant linear velocity in Cartesian space and the position error in image space. Simulation results are presented to illustrate the effectiveness and feasibility of the fuzzy logic approach. Experimental tests with an agricultural mobile robot are carried out to verify and validate the visual servoing approach for cotton crop monitoring.

This work proposes a new strategy for autonomous navigation of mobile robots in sugarcane plantations based on thermal imaging. Unlike ordinary agricultural fields, sugarcane farms are generally vast and accommodates numerous arrangements of row crop tunnels, which are very tall, dense and hard-to-access. Moreover, sugarcane crops lie in harsh regions, which hinder the logistics for employing staff and heavy machinery for mapping, monitoring, and sampling. One solution for this problem is TIBA (Tankette for Intelligent BioEnergy Agriculture), a low-cost skid-steering mobile robot capable of infiltrating the crop tunnels with several sensing/sampling systems. The project concept is to reduce the product cost for making the deployment of a robot swarm feasible over a larger area. A prototype was built and tested in a bioenergy farm in order to improve the understanding of the environment and bring about the challenges for the next development steps. The major problem is the navigation through the crop tunnels, since most of the developed systems are suitable for open field operations and employ laser scanners and/or GPS/IMU, which in general are expensive technologies. In this context, we propose a low-cost solution based on infrared (IR) thermal imaging. IR cameras are simple and inexpensive devices, which do not pose risks to the user health, unlike laser-based sensors. This idea was highly motivated by the data collected in the field, which have shown a significant temperature difference between the ground and the crop. From the image analysis, it is possible to clearly visualize a distinguishable corridor and, consequently, generate a straight path for the robot to follow by using computationally efficient approaches. A rigorous analysis of the collected thermal data, numerical simulations and preliminary experiments in the real environment were included to illustrate the efficiency and feasibility of the proposed navigation methodology.

In recent years, the use of robots in agriculture has been increasing mainly due to the high demand of productivity, precision and efficiency, which follow the climate change effects and world population growth. Unlike conventional agriculture, sugarcane farms are usually regions with dense vegetation, gigantic areas, and subjected to extreme weather conditions, such as intense heat, moisture and rain. TIBA - Tankette for Intelligent BioEnergy Agriculture - is the first result of an R&D project which strives to develop an autonomous mobile robotic system for carrying out a number of agricultural tasks in sugarcane fields. The proposed concept consists of a semi-autonomous, low-cost, dust and waterproof tankette-type vehicle, capable of infiltrating dense vegetation in plantation tunnels and carry several sensing systems, in order to perform mapping of hard-to-access areas and collecting samples. This paper presents an overview of the robot mechanical design, the embedded electronics and software architecture, and the construction of a first prototype. Preliminary results obtained in field tests validate the proposed conceptual design and bring about several challenges and potential applications for robot autonomous navigation, as well as to build a new prototype with additional functionality., 9 pages, 15 figures

The majority of works in line of sight (LOS) stabilization and tracking using inertially stabilized platforms (ISPs) apply simple linear controllers to achieve the required performance. However, these techniques do not provide ideal disturbance rejection, which is a desired characteristic for these types of systems in the context of high-accuracy applications. In this work, we propose a Sliding Mode Control (SMC) strategy for both stabilization and target tracking for a 3- DoF ISP. Both state and output feedback cases are considered. In the latter case, a High-Order Sliding Mode Observer (HOSMO) is proposed for the estimation of the ISP joint velocities. In each case, two Super-Twisting Controllers (STC) are employed in a cascade topology, providing finite-time convergence to the sliding variables. The inner controller ideally rejects the dynamic disturbances acting on the ISP joints, reducing the system to an ideal double integrator. The outer controller ensures target tracking in quaternion space, ideally rejecting all remaining kinematic disturbances. Numerical simulations show the efficiency and performance of the proposed methodology.

In this work, we consider the 3D visual tracking problem for a robot manipulator with uncertainties in the kinematic and dynamic models. The visual feedback is provided by a fixed and uncalibrated camera located above the robot workspace. The Cartesian motion of the robot end effector can be separated into a 1D motion parallel to the optical axis of the camera and a 2D motion constrained on a plane orthogonal to this axis. Thus, the control design can be simplified, and the overall visual servoing system can be partitioned in two almost-independent subsystems. Adaptive visual servoing schemes, based on a kinematic approach, are developed for image-based look-and-move systems allowing for both depth and planar tracking of a reference trajectory, without using image velocity and depth measurements. In order to include the uncertain robot kinematics and dynamics in the presented solution, we develop a cascade control strategy based on an indirect/direct adaptive method. The stability and convergence properties are analyzed in terms of Lyapunov-like functions and the passivity-based formalism. Numerical simulations including hardware-in-the-loop results, obtained with a robot manipulator and a web camera, are presented to illustrate the performance and feasibility of the proposed control scheme. Copyright © 2016 John Wiley & Sons, Ltd.

Neste trabalho, apresenta-se uma metodologia para a adaptacao tecnologica de um automodelo comercial para pesquisas basicas e avancadas na area de robotica agricola. A modelagem matematica do veiculo e baseada em um modelo cinematico simples do tipo biciclo, enquanto que o algoritmo de controle e projetado a partir de uma abordagem cinematica. Uma ideia basica consiste em adaptar a lei de controle desenvolvida para o modelo do uniciclo e, de acordo com algumas restricoes cinematicas, aplica-la para o biciclo. Simulacoes computacionais em Rviz, o ambiente de visualizacao 3D da plataforma ROS, sao realizadas para verificar e validar as abordagens de modelagem e controle apresentadas. Resultados experimentais sao incluidos para ilustrar a eficiencia e a viabilidade da metodologia proposta.

This paper deals with the problem of controlling the 3D Cartesian position of an object grasped by a multifingered robot hand using the visual servoing approach. We consider that the geometry of the object is known and the contact constraints are always satisfied during the grasping motion. An RGB camera and a depth sensor are simultaneously used to provide visual positioning and depth information of the grasped object. The control design is based on the kinematic control approach and two visual servo controllers are developed in the image space, ensuring the zero-convergence of the output tracking errors and high disturbance-rejection capability. Practical tests for visual tracking tasks of a grasped object are performed with a three-fingered robot hand and a natural interface device. Experimental results are shown to illustrate the performance and feasibility of the proposed methodology.

This work presents a methodology for autonomous manipulation of valves using a dual-arm robot and the visual sensing provided by cameras mounted on the head and each robot arm. A novel image-based identification and pose estimation method is devised to determine the valve to be manipulated as well as to estimate its position and orientation with respect to the robot base. The valve pose is used in the positioning and alignment of the robot end effector enabling the manipulation task to be carried out autonomously. Experimental results, obtained with a BaxterTM robot performing valve turning tasks, illustrate the feasibility of the proposed methodology. The main objective of this work is to provide capabilities to develop new solutions and innovative technologies to deal with the challenge of manipulating valves autonomously in poorly structured and harsh environments

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.

El control servovisual de robots manipuladores ha sido un tema en evolución en los últimos años, especialmente en aplicaciones donde el ambiente es no estructurado o el acceso para un operador es difícil.Para diseñar este tipo de controladores se hace importante realizar simulaciones previas para ajustar los parámetros o tener una aproximación del comportamiento. En este trabajo se presentan dos diferentes modelos de sistemas de visión,enfocados a aplicaciones en el control de robots manipuladores. El modelado de las cámaras de video se obtendrá con la ya conocida proyección de perspectiva. Para validar los modelos se presentaran simulaciones de dos controladores servovisuales en 2D y 3D.

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.

In this work, the bilateral teleoperation of a Multi-Master Multi-Slave (MM/MS) robotic system able to perform object manipulation tasks is presented. A multi-agent formation control strategy is considered to control the object's velocity. The velocity and interaction forces are provided by the master robots while the object is manipulated with a bounded force by the slave robots. Stability and transparency are guaranteed by two-layer passivity strategy using the energy tanks approach. The stability and convergence analysis is carried out by using the Lyapunov stability theory and the passivity formalism. Numerical simulations are included to illustrate the performance and effectiveness of the proposed control scheme.

In this work, we address the inverse kinematics problem for parallel mechanisms using a differential kinematics approach based on the recently proposed Filtered Inverse method. The key idea behind the method is to employ an inversion algorithm which dynamically estimates the inverse of the Jacobian matrix, rather than using the instantaneous calculation of the true inverse. An interesting property of this novel solution is its ability to cope with kinematic singularities as well as the mechanical joint limits. The stability and convergence analysis of the inversion algorithm is carried out by using the Lyapunov stability theory. Simulation results, obtained with a slider-crank mechanism, are shown to illustrate the performance and feasibility of the proposed scheme.

In this work the problem of remote calibration and trajectory replanning for a subsea robotic manipulator is considered. Because the trajectory planning is normally done in a structured environment, several uncertainties arise when the robot is placed on the seabed and these need to be compensated for to guarantee that the task specifications are fulfilled. We address the particular problem of configuration errors in the robot base with respect to the configuration used during the off-line trajectory planning. A calibration method based on both internal and external sensors is presented in order to estimate the uncertainty in the location of the robot. Moreover, a trajectory replanning strategy in the Cartesian velocity space is proposed to guarantee that the originally planned trajectory is followed. Simulation and experiments performed with a 6-DoF robot manipulator and a real calibration grid show the viability of the proposed planning and control schemes.

This paper addresses the posture control problem for robotic systems subject to kinematic constraints. The key idea is to consider the kinematic constraints of the mechanisms from their structure equations, instead of explicitly using the constraint equations. A case study for parallel robots and cooperating redundant robots is discussed based on the following concepts: forward kinematics, differential kinematics, singularities and kinematic control. Simulations results, obtained with a Four-Bar linkage mechanism, a planar Gough-Stewart platform and two cooperating robots, illustrate the applicability and versatility of the proposed methodology. Este artigo considera o problema de controle de postura para sistemas robóticos com restrições cinemáticas. A ideia principal é considerar as restrições cinemáticas dos mecanismos a partir de suas equações estruturais, ao invés de usar explicitamente a equação de restrição. Um estudo de caso para robôs paralelos e robôs cooperativos é discutido baseado nos conceitos de cinemática direta, cinemática diferencial, singularidades e controle cinemático. Resultados de simulação, obtidos a partir de um mecanismo Four-Bar linkage, uma plataforma de Gough-Stewart planar e dois robôs cooperativos, ilustram a aplicabilidade e versatilidade da metodologia proposta.

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.

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.

In this work, we address the problem of kinematic modeling and control design of a multifingered robot hand. Each robot finger is modeled as a parallel manipulator and its kinematic constraints are computed from empirical analysis due to the inherent mechanical complexity of the mechanism. The motion control problem for a grasped object is solved by using the kinematic control approach, which is able to ensure the asymptotic stability of the output tracking error and the prehension of the object. The kinematics-based control scheme is designed to include the contact model in the hand Jacobian matrix, allowing for the simultaneous control of the object position as well as the relative position between the fingers. Experiments are carried out with a three-fingered robot hand executing grasping and manipulation tasks of soft objects. Practical results are shown to illustrate the performance and effectiveness of the proposed methodology.

In this work, we present a control and obstacle collision avoidance method for redundant robot manipulators operating in partially structured environments in the presence of humans. The control algorithm is based on the concept of artificial potential fields and it uses the pseudo-inverse of the Jacobian matrix with a weighting factor for the mechanical joint limits, taking advantage of the robot redundancy for the purpose of obstacle avoidance and control goal achievement. The detection algorithm uses a depth sensor based on the structured light to obtain a 2-1/2-D description of the surroundings from a point cloud. Repulsive fields are created around the detected obstacles, allowing for the robot to perform the task of interest without collisions. A filtering methodology based on geometric elements is presented to filter the RGB-D scene captured by the depth sensor, eliminating the robot body and the obstacles located outside its workspace. Experimental results, obtained with a Motoman DIA10 robot and a Microsoft KinectTM, illustrate the feasibility of the proposed scheme.

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.

This work addresses the hybrid position-force control problem for robot manipulators performing inter-action tasks on constraint surfaces with regular curvature. A novel hybrid control law, based on an orientation-dependent term, is proposed to circumvent the performance degradation owing to modeling uncertainty, particularly with respect to the geometry of the constraint surface. As in our previous work, the effect of the robot dynamics can be included by using a cascade control strategy. In contrast, instead of only position, the presented approach provides complete robot posture control. Simulation results illustrate the performance and feasibility of the proposed control scheme.

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 K p . 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.

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. Este trabalho apresenta uma estratégia de controle para robôs manipuladores realizarem rastreamento cartesiano 3D utilizando servovisão. Considerando uma câmera fixa, o movimento cartesiano 3D é decomposto em um movimento 2D sobre um plano ortogonal ao eixo óptico e em outro movimento 1D paralelo ao mesmo eixo. Uma abordagem de servovisão baseada em imagem é utilizada para tratar o problema de controle não-linear, gerado pela variação de profundidade, sem a necessidade de estimar esta medida. Devido à ausência de calibração da câmera, um método de controle adaptativo é utilizado para assegurar rastreamento planar e de profundidade nas coordenadas da imagem. A malha de controle de profundidade é fechada através da medição da área da imagem de um objeto fixado no efetuador do robô. Simulação e resultados experimentais, obtidos com um robô manipulador real, ilustram a viabilidade do esquema proposto.

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

Neste trabalho, considera-se um sistema robótico servovisual usando câmera fixa e não-calibrada. Dois controladores adaptativos são propostos para rastrear visualmente um alvo em movimento na presença de incertezas nos parâmetros da câmera. A primeira estratégia adaptativa utiliza o método de Hierarquia de Controle para solucionar o problema de controle adaptativo multivariável. A segunda é baseada na simetrização via fatoração da matriz de controle. Os algoritmos adaptativos são robustos na medida em que apresentam reduzida sensibilidade à incertezas cinemáticas. Resultados experimentais com um manipulador em movimento planar ilustram a robustez e a viabilidade dos esquemas propostos. In this work, a robotic visual servoing system using a fixed and uncalibrated camera is considered. Two adaptive controllers are proposed for visually tracking a moving target in the presence of camera parameters uncertainties. The first adaptive strategy uses the Hierarchy of Control method to solve the multivariable adaptive control problem. The second one is based on the symmetrization via factorization of the control matrix. The adaptive algorithms are robust in the sense that they present reduced sensitivity to kinematic uncertainties. Experimental results with a manipulator in a planar motion illustrate the robustness and viability of the proposed schemes.

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 patient's skin. However, in surgery, the patient's 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.