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The thermal-fluid control of a microchannel evaporator operating in the two-phase regime is considered. The control objective is to stabilize the fluid flow and maintain a minimal evaporator wall temperature by commanding the inlet flow rate using a pump. The first objective is achieved with a feedback controller using only mass flow rate measurement (via a lead filter). The second objective is more challenging as the achievable wall temperature depends on the heat transfer coefficient which is hard to characterize (heavily depends on the flow rate and heat load). An extremum seeking control based on sliding modes is proposed to minimize wall temperature and adjust the flow rate without considering heat transfer coefficient knowledge. Thus, considering a thermal-fluid model, the stability of the flow control loop is establish and the convergence to the extremum seeking control is analyzed via singular perturbation analysis and Tikhonov’s theorem. Simulation results demonstrate the efficacy of the proposed scheme.

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 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.

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.

DORIS is a mobile robot for remote supervision, diagnosis, and data acquisition on offshore facilities. The proposed system is composed of a rail-guided robot capable of carrying different sensors through the inspected area. This paper presents a general overview of the robot, and a description of the developed embedded electronics, power supply system and software architecture. The results with teleoperated navigation validate the concepts considered so far and rise several challenges for future works.

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

This paper addresses the dynamic modeling and the energy storage analysis of a wave energy hyperbaric converter, which consists of a set of oscillating bodies (named as pumping modules) linked to hydropneumatic accumulators and an electric generating unit. A mathematical model of the accumulator is presented and a model for the generating unit is proposed, including a nonlinear model of a Pelton turbine. Then, the hydrodynamic, mechanical, and electrical characteristics of the subsystems that compose the converter are discussed. With the proposed model, it is possible to evaluate the dynamic behavior of the entire system. That is, for a given incident ocean wave, it is possible to evaluate all the system state variables and the generated electric power, including the quality (fluctuation, for example) of the generated voltage and frequency for islanded or power-grid-connected operation. Simulation results considering the proposed wave-to-wire model under the action of regular and irregular incident waves are presented to illustrate the performance of the system.

This paper concerns the reconfiguration control of actively articulated mobile robots navigating through irregular terrains. Active mechanisms are able to accommodate for different operation conditions. The capability to influence mobility depends on the mechanism kinematic structure and the actuators velocity limitations. The robot mobility is evaluated considering ground clearance, orientation and stability. Control strategies are proposed considering the actuators bandwidth to compensate abrupt variations of the navigation trajectory and the driven terrain. The proposed solution consists on anticipating the command action by employing a predictive functional control method to adjust the robot for critical conditions faced during operation. Numerical simulations using field data, recorded while navigating on a natural terrain, are performed to verify the proposed strategies controlling a robot with two actuated DoF.

Electric lighting has not substantially changed in over 100 years. From incandescent bulbs to fluorescent tubes, the efficiency remains low and control mostly involves on/off or dimming. The new wave of solid state lighting offers the possibility of sensor-based intensity regulation, color control, and energy efficiency, under varying needs and environmental conditions. This paper formulates the lighting control problem as an optimization problem balancing color fidelity, human perception and comfort, light field uniformity, and energy efficiency. The optimization problem is solved based on the light propagation model, which is adaptively updated with color sensor feedback to account for changing ambient lighting conditions, such as daylighting. We demonstrate the proposed approach in a smart space testbed under a variety of use conditions. The testbed is instrumented with 12 color tunable lights and 12 light sensors, as well as simulated daylight. The results show substantial improvement in terms of energy usage and delivering good light field quality in the presence of varying lighting conditions. Experimental results corroborate the efficacy of the proposed algorithms.

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 paper the control allocation problem is revisited in the context of quadrotor unmanned aerial vehicles (UAVs). Two control allocation strategies are presented, both aiming to avoid output saturation. The first strategy is based on the optimization of a weighted least squares (WLS) criterion subject to boundary constraints. The second, called direct control allocation (DCA), aims to maintain constant the direction of the desired virtual efforts vector. As a modification of this strategy, we introduce the partial control allocation (PCA) scheme. In the latter, only the direction of a fraction of the desired virtual efforts vector is kept constant. It is also presented a discussion on the performance of the DCA and PCA schemes.

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 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.

In this paper, a solution for the formation control of a group of nonholonomic uncertain agents is proposed. The configuration pattern is generated from a given potential function, which may also incorporate a strategy for collision avoidance. The system parametric uncertainties are compensated by a robust adaptive control algorithm named binary adaptive control which combines the good transient properties and robustness of Sliding Mode Control with the desirable steady-state properties of parameter adaptive systems delivering continuous control signals, thus avoiding chattering. First, a static formation (regulation) scenario is considered. Conditions for semi-global stability of the multi-agent system are established using Lyapunov theory for decentralized control schemes. Then, the control scheme is extended for trajectory tracking to be performed by the group of agents while maintaining a desired geometric pattern. In the case of regulation, only the relative position information with respect to neighboring agents and individual velocity are available for the control implementation for each agent. In the case of tracking, also the velocities of the neighbors are needed. Simulations are presented to validate the theoretical results.

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.

The direct adaptive control of planar robot manipulators through visual servoing is considered. A solution is developed for image-based visual systems to allow tracking of a desired trajectory, when both camera calibration and robot dynamics are uncertain. In order to solve the MIMO parameter adaptive problem without using image velocity information, the adaptive camera calibration is formulated as a relative degree two MIMO adaptive control problem. A recently proposed Lyapunov/passivity-based adaptive control for relative degree two MIMO system based on SDU factorization is applied. The resulting adaptive calibration is then combined with an adaptive motion controller for the 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, 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.

In this paper a control theoretical solution is presented for the direct adaptive visual dynamic control of planar manipulators using a fixed camera, when both camera calibration and robot dynamics are uncertain. The proposed scheme is developed for image-based look-and-move visual systems to allow tracking of a moving target. In order to solve the multivariable parameter adaptive problem, the recently proposed Immersion and Invariance (I&I) method is used. The scheme is then combined with an adaptive controller for the manipulator, taking into account its nonlinear dynamics. This leads to an overall stable adaptive visual servoing system. An extension of the I&I approach for 3 D visual servoing is shown to be possible by a convenient control reparametrization associated with a proper image-based depth control scheme.

The paper describes an automatic dynamic positioning system for remotely operated underwater vehicles (ROVs) using a mechanical passive arm for position measurement that is suitable for inspection and intervention tasks requiring precise positioning. Good dynamic performance in tracking was also attained, particularly with the variable structure model reference adaptive control strategy.

Abstract Safety and efficient operation are imperative factors to offshore production sites and a main concern to all Oil & Gas companies. A promising solution to improve both safety and efficiency is to increase the level of automation on the platforms by introducing intelligent robotic systems. Robots can execute a wide variety of tasks in offshore environments, including monitoring and inspection, diagnosis and maintenance, process production intervention, and cargo transport operations. In particular, considering the distance of offshore platforms from the Brazilian coast, such technology has great potential to increase safety by decreasing the number of onboard personnel, simplify logistics, and reduce operating costs of Brazilian facilities. The use of robots can also allow proactive integrity management and increase frequency and efficiency of platform inspection. DORIS is a research project which endeavors to design and implement a mobile robot for remote supervision, diagnosis, and data acquisition on offshore facilities. The proposed system is composed of a rail-guided mobile robot capable of carrying different sensors through the inspected environment. The robot can also analyze sensor data and identify anomalies, such as intruders, abandoned objects, smoke, fire, and liquid leakage. The system is able to read valves and make machinery diagnosis as well. To prove the viability of the proposed system, an initial prototype is developed using a Roomba robot with several onboard sensors and preliminary tests have been performed in a real environment similar to an offshore platform. The tests show that the robot is capable of indicating the presence or absence of objects in a video stream and mapping the local area with laser sensor data during motion. A second prototype has been built to test the DORIS mechanical design. This prototype is used to test concepts related to motion on a rail with straight, curved, horizontal, and vertical sections. Initial results support the proposed mechanical concept and its functionalities. Introduction During the last decade, several Oil & Gas companies, research groups, and academic communities have shown an increased interest in the use of robotic systems for operation of offshore facilities. Recent studies project a substantial decrease in the level of human operation and an increase in automation used on future offshore oil fields (Skourup and Pretlove, 2009). Today, robotic systems are used mainly for subsea tasks, such as mapping the seabed and performing inspection tasks on underwater equipment, risers, or pipelines using Remotely Operated Vehicles (ROVs) or Autonomous Underwater Vehicles (AUVs). Topside operations, on the other hand, have not yet adopted robotized automation as a solution to inspection and operation tasks. From (2010) points out the potential increase in efficiency and productivity with robot operators rather than humans, given that robots work 24 hours per day and 7 days per week, are less prone to errors, and are more reliable. Another highlighted point is the improvement Health, Safety, and Environment (HSE) conditions, as robots can replace humans in tasks performed in unhealthy, hazardous, or confined areas. In the specific Brazilian case, the Oil & Gas industry is growing at a high pace, mainly due to the recent discoveries of big oil fields in the pre-salt layer off the Brazilian coast. These oil reservoirs are located farther than 300 km from the shore and at depths of 5000 to 7000 km. These factors, especially the large distances, motivate the development of an offshore production system with a high degree of automation based on advanced robotics systems.

Mobile robots and vehicles with active articulated elements are well suited to drive on irregular and rough terrain because they are able to adjust their center of mass and decrease the risk of tip over or other accidents. The articulated mechanism, however, is usually constrained by the actuator's bandwidth, making it difficult for the vehicle to compensate for abrupt changes in terrain profile. In this paper we propose a terrain model-based control method to improve stability when traversing terrains with varying slopes, depressions, and rises. A model predictive control approach takes into account actuator bandwidth to anticipate interactions with the terrain and control the articulated elements to prevent tipping over. The method is applied to an autonomous orchard platform where workers stand while conducting production tasks on the trees, adjusting the platform height and increasing its stability. The feasibility is illustrated via numerical simulations performed with the MD Adams/Car software and Matlab, with both artificial and natural terrain data. Challenging yet realistic terrain profiles are considered to demonstrate the control effectiveness in preventing the platform from tipping over and therefore improving the workers' and the vehicle's safety.

Inertially stabilized platforms are subject of interest to different engineering areas, such as telecommunications, robotics and military systems. The objective is to maintain the attitude of a desired object constant despite the movements of a host vehicle, in order to point towards a chosen direction. This paper deals with the platform stabilization problem in the case where only two gyroscopic sensors are available. In this case, certain restrictions to vehicle dynamics are considered. Sensor positioning and sensitivity analysis to unmodeled motion are also discussed. An extension to the visual tracking problem is presented using a class of algorithms for fast digital image registration.

Urucu, deep in the Amazon forest, is the largest in-land natural gas and oil producing site in Brazil. The oil and gas extracted in Urucu travel some 700 km in a pipeline through the jungle until they reach Manaus, where they are refined and consumed. Exploration of the site is conditioned on the Brazilian oil company Petrobras monitoring a 400 km section of the pipeline for any signs of water contamination. The Environmental Hybrid Robot (EHR) was conceived to automate this humongous monitoring task, currently executed in extremely small scale by scientists and locals on foot and small boats. This paper describes the design, analysis, and testing of the EHR’s second-generation locomotion mechanism, based on a two-DOF parallel mechanism. The new mechanism allows for independent control of wheel position and orientation relative to the terrain, thus significantly increasing the robot’s mobility. Additionally, it allows for control of the vehicle’s velocity-torque curve by adjusting the radius of curvature of the wheel at the point of contact with the soil. We present the forward and differential kinematic models of the new mechanism and, based on them, a differential kinematics-based position and orientation control method. We illustrate the methodology with experimental results.

The problem of rigid robot manipulator tracking control without measurement of joint velocities and in the presence of parametric uncertainty is considered. A control strategy is proposed based on the recently developed I/O VS-MRAC (Input/Output Variable Structure Model-Reference Adaptive Control) scheme. Theoretical and simulation results indicate that the strategy leads to remarkable tracking error transient behavior, stability and performance robustness with respect to parameter uncertainty, nonlinearities and disturbances.

The active control of reconfigurable mobile robots based on multi-objective optimization is considered. Different control strategies to improve the robot mobility are presented. Traction is considered in terms of the contact forces distribution among wheels, while stability is defined by a potential energy margin. Thus, considering the conflicting control objectives associated with these strategies, a multi-objective optimization approach is formulated to determine a set of optimal solutions which allows to establish a tradeoff solution. A planar robot is considered to illustrate the proposed multi-objective optimization approach.

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.

In this work, a hydraulic turbine speed governor is proposed in view of its application in an isolated electric generation system based on an ocean wave energy converter (WEC). The proposed strategy is based on cascade closed-loop control combined with feedforward of load disturbances. The main characteristics of a WEC are presented and a dynamic model of the generating unit is developed. Experimental results illustrate the performance of the proposed control 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

The problem of rigid robot manipulator tracking control without measurement of joint velocities and in the presence of parametric uncertainty is considered. A control strategy is proposed based on the VS-MRAC (variable structure model-reference adaptive control) scheme. Theoretical and experimental results on a PUMA 560 indicate that the strategy leads to remarkable tracking error transient behavior, stability and performance robustness with respect to parameter uncertainty, nonlinearities and disturbances.

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 considers the localization and navigation of a mobile robot. The control strategy is based on a nonlinear model predictive control technique that utilizes the Newton method. The robot localization is obtained using information from odometric and ultrasonic sensors through a Kalman filter. Simulation and experimental results illustrate the efficacy of the proposed method.

The problem of direct adaptive visual servoing of robot manipulators is considered. A solution is developed for image-based look-and-move visual systems to allow tracking of a desired trajectory, when both camera calibration and robot dynamics are uncertain. In order to solve the multivariable parameter adaptive problem, the recently proposed immersion and invariance (I&I) method is used. The scheme is then combined with a robust motion controller for the manipulator, which takes into account its uncertain nonlinear dynamics and leads to an overall stable adaptive visual servoing system. The effectiveness of the proposed strategy is illustrated through simulations and experimental results.

A control-theoretical solution is presented for the direct adaptive visual control of planar manipulators using a fixed camera, when both camera calibration and robot dynamics are uncertain. The proposed scheme is developed for image-based look-and-move visual systems to allow tracking of a moving target. In order to solve the multivariable parameter adaptive problem, the proposed immersion and invariance (I&I) method is used. The scheme is then combined with the adaptive controller for the manipulator, taking into account its nonlinear dynamics and leading to an overall stable adaptive visual system. Simulations and experimental results are also presented for the proposed strategy.

In this work, a new 2D adaptive visual positioning/tracking controller is considered. The proposed scheme is developed for image-based eye-to-hand systems to perform position and tracking tasks on the 3D environment, when both camera calibration and robot parameters are uncertain. The symmetric-diagonal-upper (SDU) factorization method is adopted to solve the adaptive multivariable control problem. The positioning/tracking controller is first developed for the Cartesian robot case, and then extended to the general robotic system. Simulation results are also presented for the proposed strategy.

Addresses the design, implementation and experimental evaluation of a sliding control scheme for uncertain systems which is free of chattering and results in smooth control signals. The proposed approach relies on an appropriate prediction error. It is shown that, in practice, successful performance of the basic sliding controller requires adequate filtering of the measured signal. A particular Kalman-filter, independent of the plant itself, is shown to yield very satisfactory experimental results obtained on a 1 d.o.f. helicopter-like mechanism.

A robot visual servoing system using a fixed camera is considered. Two adaptive controllers are proposed for visually tracking a moving target in the presence of camera uncertainties. The first adaptive strategy is based on an original method proposed by Hsu and Aquino (1999). The key idea is to use a hierarchy of control methods to solve the adaptive control problem. The adaptive algorithm is robust in the sense that it has reduced sensitivity to kinematic uncertainties. The second adaptive law is based on the symmetrization of the control matrix. Experimental results with a robot manipulator in a planar motion illustrate the robustness and viability of the proposed scheme.

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.

A robot visual servoing system using an uncalibrated fixed camera is considered. An adaptive controller is proposed for visually tracking a predefined trajectory on some unknown smooth surface. Adaptation is designed to compensate the camera uncertainties and the lack of knowledge about the surface. The key idea is to extend the use of a recently proposed hierarchy of control method to solve the adaptive control problem which now consists of a non-linear visual servoing system. Experimental results illustrate the robustness and viability of the proposed scheme.

Abstract The Oil & Gas industry has seen increasing costs of finding and extracting hydrocarbons, especially in remote locations, ultra-deep water reservoirs (400 m or deeper) or in hostile environments. Those new exploration frontiers have been increasing the production complexity and logistic costs. In such conditions, oil exploration feasibility depends on new technologies to optimize production efficiency. One possible solution to this challenge is to increase the degree of automation in production units. New design concepts also consider the use of robotic devices in such scenarios. In this paper we present a robotics framework, SimUEP-Robotics (Robotics Simulator for Stationary Production Units - Unidades Estacionárias de Produção or UEPs, in Portuguese), aimed to enable planning the offshore platform robotizing using virtual reality techniques. The SimUEP-Robotics is based on ROS (Robot Operating System), a middleware for exchanging messages between different devices and processes that cooperate to accomplish a robotics task. SimUEP-Robotics is designed concerning the offshore requirements and is a flexible framework that allows the inclusion of new robots and devices in a virtual operation scenario. This capability enables the robotization and automation of offshore facilities that gradually evolve, starting from a complete virtual scenario towards a complete robotic system operating on a real platform, progressively including real devices. SimUEP-Robotics has an integrated Virtual Reality Engine (VR-Engine) specially tailored to provide realistic visualization of large offshore scene models in an immersive environment. The monitoring and management of remote operations of Stationary Production Units (SPU) is an activity that can also benefit by the usage of virtual reality scenarios due to the potential to reduce the complexity and difficulty in visualizing and validating simulations of operations performed by robots on a real SPU. The framework supports simultaneous simulation of multiple robots equipped with sensors and actuators like cameras, laser range finders and robotic manipulators. SimUEP-Robotics has also some specialized visualization tools like trajectory visualizer, ghostview robot animation, point-to-point measurement and a scenario editor that allows the user customize the target scenario accordingly. Through the use of those visualization tools it is possible, for example, to better understand the quality of the planned robot trajectory and propose new algorithms that can be further evaluated in the virtual environment. In conclusion, we argue that the validation process in an immersive virtual environment reduces risks and costs of real operation tests scenarios. SimUEP-Robotics has also an integrated Robotics-Simulator which is responsible for taking care of task planning and execution based on the information of the virtual scenario provided by the VR-Engine. To illustrate the effectiveness of the framework, different robotics applications were developed. One is an underwater application that calculates the whole dynamics of an operated ROV to simulate and test complex ROV operations in deep waters, like the connection of a flowline to a Christmas tree. The other one represents a topside offshore platform scenario where different virtual robots, derived from real mechanisms like Motoman DIA10, Puma 560, Seekur and others, operates. Results obtained on a pick and place task demonstrate the benefits of the proposed robotics framework for offshore applications.

In this paper a formation control for dynamic nonholonomic mobile robots with curvature constraints and unknown dynamic parameters is proposed. The control scheme is based on a saturated potential function which makes possible the design of decentralized formation controls scheme and avoids agents collisions considering its dimensions. For each agent, a cascaded control strategy combines a controller which takes into account its dynamic, and a kinematic control, leading to an overall globally stable system.

Describes the development and implementation of a dynamic positioning system for remotely operated underwater vehicles (ROVs) which is based on a mechanical passive arm (PA) for position measurement. The system, named DPSROV, is suited for tasks which require precise positioning within a limited range around a working site. The DPSROV was configured aiming at easy installation in most commercially available ROVs. The effectiveness of the DPSROV was tested on real ROVs. Two different control strategies were adopted: a conventional linear controller and a robust variable structure model-reference adaptive control. Experimental results are presented to illustrate the DPSROV performance.

This paper addresses the problem of designing adaptive output feedback variable structure control systems for uncertain plants. Most of it is devoted to give a state-of-the-art overview of this quite recent and potentially important technique. Connections of the new technique with standard parameter adaptive and variable structure technique are discussed. The paper also contains new results and ideas which have not yet been widely disseminated. >