Single-master multislave (SMMS) teleoperation systems can perform multiple tasks remotely in a shorter time, cover large-scale areas, and adapt more easily to single-point failures, thereby effectively encompassing a broader range of applications. As the number of slave manipulators sharing a communication network increases, the limitation of communication bandwidth becomes critical. To alleviate bandwidth usage, the try-once-discard (TOD) scheduling protocol and event-triggered mechanisms are often employed separately. In this article, we combine both strategies to optimize network bandwidth and energy consumption for SMMS teleoperation systems. Specifically, we propose event-triggered control and communication schemes for a class of SMMS teleoperation systems using the TOD scheduling protocol. Considering dynamic uncertainties, the unavailability of relative velocities, and time-varying delays, we develop adaptive controllers with virtual observers based on event-triggered schemes to achieve master-slave synchronization. Stability criteria for the SMMS teleoperation systems under these event-triggered control and communication schemes are established, demonstrating that Zeno behavior is excluded. Finally, experiments are conducted to validate the effectiveness of the proposed algorithms.

Human-robot collaboration (HRC) in manufacturing environments requires that physical safety can be guaranteed. Control methods that implicitly regulate the interaction forces between a controlled robot and its environment, such as impedance control, are often used for safety in HRC. However, these methods could be complemented by restricting the robot operational space for additional safety guarantees. In this context, obstacle avoidance might benefit from considering a prediction of the controlled-robot motion and/or the behavior of the human collaborator. To this end, we proposed to include linearized Safety Control Barrier Functions (SCBFs) in a linear Model Predictive Control (MPC) strategy for robot impedance variation online. The convex optimization problem that was obtained from our proposal presented two advantages compared to nonlinear MPC alternatives. First, optimality was ensured in our method under linearity assumptions on human guidance and linearized robot dynamics, whereas a controller synthesized by nonlinear MPC strategies would depend on the fundamental characteristics of the problem. Second, our method enabled implementation at a faster control frequency, thus allowing a rapid adaptation to changes occurring in the robot environment. Finally, experimental validation was performed using a Franka Emika Panda robot in a human-robot collaborative scenario, and the stability of the method was shown using Lyapunov theory. Note to Practitioners—Modern-day industrial manufacturing environments are characterized by collaboration between human operators and robot manipulators. In this scenario, where humans and robots share workspace, physical safety is required. This research aims to improve safety in human-robot collaboration by proposing a novel robot control strategy. In our approach, the interaction forces between the controlled robot and its environment were regulated implicitly using impedance control, to allow, among other interactions, that an operator could manually guide the robot. Then, obstacle avoidance was included to modify the robot impedance behavior for restricting undesired collisions with, for example, the operator head, while ensuring stability of the method. Our main contribution is that the proposed formulation allows to consider a prediction of the robot motion and/or the operator behavior for robot obstacle avoidance. It was shown in experiments with a real robot that adding prediction capabilities reduced the risk of undesired collisions, while also decreasing the robot trajectory error. Moreover, the method could be implemented at a fast rate so that the robot could react rapidly to changes in its environment. Also, this implementation allowed to achieve a minimal variation with respect to the nominal impedance behavior of the robot. To conclude, this method is intended for scenarios where a robot is required to interact with its, possible restricted, environment: for example, a robot with a drill attached to its end-effector that is being guided to modify its trajectory, but where the operator should not be allowed to accidentally be harmed; or a robot performing a polishing task where a section of the polished object should remain unpolished. Therefore, using the proposed robot control strategy, a possible extension of this research would be to provide an improved prediction of the human operator intention depending on the desired robotic task and the role of the operator.

In this paper, an iterative learning strategy was developed to improve trajectory tracking for an impedance-controlled robot manipulator. In this learning strategy, an update law was proposed to modify the Cartesian reference of an impedance controller. Also, the conditions that ensure its convergence considering the dynamics of the robot were derived. Finally, an experimental evaluation was performed using a Franka Emika Panda robot in two different robot tasks, and its results showed that robot task completion was achieved in a lower number of iterations, while maintaining a smooth physical interaction between the robot and its surroundings.

In this paper, Safety Control Barrier Functions (SCBFs) were used to adjust the null-space compliant behavior of a redundant robot to improve safety in Human-Robot Collaboration (HRC) without modifying the robot behavior with respect to its main Cartesian task. A Lyapunov function was included in an energy storage formulation compatible with strict passivity to provide global asymptotic stability guarantees for the null-space compliance variation, and the necessary conditions for stability were formulated as inequality constraints of the optimization problem used for the null-space compliance variation. Experimental validation was performed using a Franka Emika Panda robot for a collaborative assembly application and its results showed that safety can be improved by using SCBFs simultaneously to the optimization of the robot configuration, while employing a single degree of freedom.

This article addresses the distributed cooperative control design for a class of sampled-data teleoperation systems with multiple slave mobile manipulators grasping an object in the presence of communication bandwidth limitation and time delays. Discrete-time information transmission with time-varying delays is assumed, and the Round-Robin (RR) scheduling protocol is used to regulate the data transmission from the multiple slaves to the master. The control task is to guarantee the task-space position synchronization between the master and the grasped object with the mobile bases in a fixed formation. A fully distributed control strategy including neural-network-based task-space synchronization controllers and neural-network-based null-space formation controllers is proposed, where the radial basis function (RBF) neural networks with adaptive estimation of approximation errors are used to compensate the dynamical uncertainties. The stability and the synchronization/formation features of the single-master-multiple-slaves (SMMS) teleoperation system are analyzed, and the relationship among the control parameters, the upper bound of the time delays, and the maximum allowable sampling interval is established. Experiments are implemented to validate the effectiveness of the proposed control algorithm.

Collaborative robots have been designed to per-form tasks where human cooperation may occur. Additionally, undesired collisions can happen in the robot’s environment. A contact classifier may be needed if robot trajectory recalculation is to be activated depending on the source of robot–environment contact. For this reason, we have evaluated a fast contact detection and classification method and we propose necessary modifications and extensions so that it is able to detect a contact in any direction and distinguish if it has been caused by voluntary human cooperation or by accidental collision with a static obstacle for kinesthetic teaching applications. Robot compliance control is used for trajectory following as an active strategy to ensure safety of the robot and its environment. Only sensor data that are conventionally available in commercial collaborative robots, such as joint-torque sensors and joint-position encoders/resolvers, are used in our method. Moreover, fast contact detection is ensured by using the frequency content of the estimated external forces, whereas external force direction and sense relative to the robot’s motion is used to classify its source. Our method has been experimentally proven to be successful in a collaborative assembly task for a number of different experimentally recorded trajectories and with the intervention of different operators.

Novel methods for the inference of radiation intensity functions defined over known surfaces are proposed, intended for use in surveying applications with mobile spectrometers. Previous approaches, based on the maximum likelihood expectation maximization (ML-EM) framework with Poisson likelihoods, are extended to better handle spatially continuous intensity statistics using ideas from Gaussian filtering. The resulting algorithm is evaluated against a classical ML-EM method, and a recently proposed sparse additive point source localization (APSL) algorithm in a Monte-Carlo simulation study. The new generalized ASPL (GASPL) is shown to compare favorably in terms of estimation accuracy when the true intensity is not well described by a set of point sources. Finally, the GASPL is used in an experiment where a detector is mounted to an unmanned aerial vehicle to estimate the intensity and location of radioactive sources placed in a meadow.

Model Predictive Control (MPC) is an efficient point-to-point trajectory-generation method for robots that can be used in situations that occur under time constraints. The motion plan can be recalculated online to increase the accuracy of the trajectory when getting close to the goal position. We have implemented this strategy in a Franka Emika Panda robot, a redundant collaborative robot, by extending previous research that was performed on a 6-DOF robot. We have also used null-space motion to ensure a continuous movement of all joints during the entire trajectory execution as an approach to avoid joint stiction and allow accurate kinesthetic teaching. As is conventional for collaborative and industrial robots, the Panda robot is equipped with an internal controller, which allows to send position and velocity references directly to the robot. Therefore, null-space motion can be added directly to the MPC-generated velocity references. The observed trajectory deviation caused by discretization approximations of the Jacobian matrix when implementing null-space motion has been corrected experimentally using sensor feedback for the real-time velocity-reference recalculation and by performing a fast sampling of the null-space vector. Null-space motion has been experimentally seen to contribute to reducing the friction torque dispersion present in static joints.

We consider the problem of feasible coordination control for multiple homogeneous or heterogeneous mobile vehicles subject to various constraints (nonholonomic motion constraints, holonomic coordination constraints, equality/inequality constraints etc). We develop a general framework involving differential-algebraic equations and viability theory to describe and determine coordination feasibility for a coordinated motion control under heterogeneous vehicle dynamics and various constraints. A heuristic algorithm is proposed for generating feasible trajectories for each individual vehicle. We show several application examples and simulation experiments on multi-vehicle coordination under various constraints to validate the theory and the effectiveness of the proposed algorithm and control schemes.

The problem of planning a trajectory for robots starting in an initial state and reaching a final state in a desired interval of time is tackled. We propose an approach based on model predictive control to solve the problem of point-to-point trajectory generation for a given final time. We discuss various choices of models, objective functions, and constraints for generating trajectories to transfer the state of the robot, while respecting physical limitations on the motion as well as fulfilling computational real-time requirements. Extensive simulation results illustrate the use of the approach, and experiments on an industrial robot in a challenging ball-catching task show the effectiveness of the approach also in demanding scenarios with real-time constraints on the computation. This paper was motivated by the problem of generating movements to transfer a robot from its current state to a new position and velocity at a certain time, when the target state and the final time may require correction at a high rate. For example, for picking small objects from a conveyor belt with a variable feed rate, an off-line planning would fail, since the motion has to be adjusted as soon as the speed is changed. Under the assumption that the desired pickup position and velocity and arrival time can be predicted, the approach in this paper is applicable. The movements can be optimized, for example, for energy efficiency or for reduction of vibrations in the robot. We discuss how to mathematically express the desired performance criteria and other requirements on the motion, such as not violating a maximum joint speed. Quick reactions to sensor inputs are computationally demanding. Thus, we limit ourselves to a class of motion-generation problems that lends itself to numerical optimization. Additionally, we save computation power by gradually refining the motion.

In this research, it is shown that robot joint torques can be used to recognize contact force transients induced during robotic manipulation, thus detecting when a task is completed. The approach does not assume any external sensor, which is a benefit compared to the state of the art. The joint torque data are used as input to a recurrent neural network (RNN), and the output of the RNN indicates whether the task is completed. A real-time application for force transient detection is developed, and verified experimentally on an industrial robot.

Programming robots for tasks involving force interaction is difficult, since both the knowledge of the task and the dynamics of the robots are necessary. An immersive haptic interface for task demonstration is proposed, where the operator can sense and act through the robot. This is achieved by coupling two robotic systems with virtual constraints such that they have the same coordinates in the operational space disregarding a fixed offset. Limitations caused by the singular configurations or the reach of the robots are naturally reflected to either side as haptic feedback.

In modern day industry, robots are indispensable for achieving high production rates and competitiveness. In small and medium scale enterprises, where the production may shift rapidly, it is vital to be able to reprogram robots quickly. Kinesthetic teaching, also known as lead-through programming (LTP), provides a fast approach for teaching a trajectory. In this approach, a trajectory is demonstrated by physical interaction with the robot, i.e., the user manually guides the manipulator. This paper presents a sensorless approach to LTP for redundant robots that eliminates the need for expensive force/torque sensors. The active implementation enhances the passive LTP by an admittance control in joint space based on the external forces applied by the user, estimated with a Kalman filter using the generalized momentum formulation. To improve the quality of the estimation and hence LTP, we use a dithering technique. The active LTP has been implemented on ABB YuMi robot and experimental comparison with an earlier passive LTP is presented.

The concept of dynamical movement primitives (DMPs) has become popular for modeling of motion, commonly applied to robots. This paper presents a framework that allows a robot operator to adjust DMPs in an intuitive way. Given a generated trajectory with a faulty last part, the operator can use lead-through programming to demonstrate a corrective trajectory. A modified DMP is formed, based on the first part of the faulty trajectory and the last part of the corrective one. A real-time application is presented and verified experimentally.

We provide a framework for 6 DOF pose estimation in seam-tracking applications using particle filtering. The particle filter algorithm developed incorporates measurements from both a 2 DOF laser seam tracker and the robot forward kinematics under an assumed external force. Special attention is paid to modeling of disturbances in the respective measurements, and methods are developed to assist the selection of sensor configurations for optimal estimation performance. The developed estimation algorithm and simulation environment are provided as an open-source, extendable package, written with an intended balance between readability and performance.

A robotic assembly task is usually implemented as a sequence of simple motions, and the transitions between the motions are made when some events occur. These events can usually be detected with thresholds on some signal, but faster response is possible by detecting the transient on that signal. This paper considers the problem of detecting these transients. A force-controlled assembly task is used as an experimental case, and transients in measured force/torque data are considered. A systematic approach to train machine-learning based classifiers is presented. The classifiers are further implemented in the assembly task, resulting in a 15%reduction of the total assembly time.

For using industrial robots in applications where the robot physically interacts with the environment, such as assembly, force control is usually needed. A force sensor may, however, be expensive and add mass to the system. An alternative is therefore to estimate the external force using the motor torques. This paper considers the problem of force estimation for the case when the robot is not moving, where the Coulomb friction constitutes a fundamental difficulty. A dithering feedforward torque is used to decrease the Coulomb friction uncertainty, and hence improve the force estimation accuracy when the robot is not moving. The method is validated experimentally through an implementation on an industrial robot. A lead-through scenario is also presented.

This article presents a linear, iterative method to solve the eye-to-hand calibration problem between a wrist-mounted laser scanner and the tool flange of a robot. Measurement data are acquired from a set of non parallel planes where after the plane equations and desired rigid transformation matrix are found in a two-step, iterative fashion. The method is shown to handle large error in the initial estimate of the transform and results are verified in both simulations and experiments using a seam tracking laser sensor for welding applications.

This paper investigates both positional dependence in systems with friction and the influence an increase in temperature has on the friction behavior. The positional dependence is modeled with a Radial Basis Function network and the temperature dependence is modeled as a first order system with the power loss due to friction as input, eliminating the need for temperature sensing. The proposed methods are evaluated in both simulations and experiments on two industrial robots with strong positional and temperature friction dependence.

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.

Industrial robots are important when the degree of automation in industry is increased. To enable the use of robots also when the products change rapidly, the programming must be quick and easy to perform. One way to accomplish this is to use lead-through programming, i.e., the user manually guides the robot. This paper presents a sensorless approach, and thus avoids the need for a typically expensive sensor. The method is based on disabling low-level joint controllers combined with gravity compensation. It is reported how the performance can be improved by compensating for friction. Further, a method for detecting small external torques is described, based on the use of the low-level joint controllers with increased integral gain. The lead-through programming is experimentally evaluated using two different industrial robots.

Nocturnal hypoglycemia is a common and potentially very dangerous condition facing persons with insulin-treated diabetes. To reduce the risk of going low while sleeping, many patient wilfully elevate their glucose level before bedtime, thereby also eroding the conditions for a sound glucose control for the next day. Recent advances in sensor technology allow for real time frequent monitoring of the glucose level, and the road map towards an artificial pancreas involves combining an insulin pump with such sensors. The first steps towards a more autonomous insulin pump have been taken by allowing the pump to suspend the insulin supply when a hypoglycemic episode is imminent. This feature relies on algorithms for predicting the nocturnal event. In this paper, we present a novel model for this purpose. In comparison to previous methods, a better trade-off between sensitivity and false alarm rate was achieved, as well as improved warning time.

This paper proposes a method for structural enhancement of a 3D sampled curve. The curve is assumed to be organized, but corrupted with low frequency noise. The proposed method approaches the notion of curve reconstruction in a novel way, where information about the structure in a scanned surface is used to reconstruct the curve. Principal Component Analysis is carried out on successive neighborhoods along the curve to estimate reduced dimensionality spaces, which allows polynomial reconstruction. The effectiveness of the proposed method is verified by both simulations and experiments.

This paper proposes a new surface recognition method by a robot. Nowadays, demands for “Real World Haptics” are increasing. Tactile sensation which is acquired by rubbing motion is important for haptics. Therefore, surface recognition based on haptic information is focused in this paper. First, the surface sensing is needed to collect feature values. There are some researches which focus on sensing and analysis of surface tactile information by using sensors with robots. However, there are some disadvantages of using sensors such as signal noise. From that reason, this paper proposes getting surface haptic information without sensors at the tip of the robot. Second, a pattern recognition method needs to be applied for surface recognition. There are some pattern recognition methods. Self-organizing maps (SOM) is one of the solutions. SOM is able to summarize high dimensional data to low dimension with preserving the topological properties of data. SOM is suitable for multi-class recognition. From these reasons, this paper proposes the surface recognition based on haptic information using SOM. Multi class surface recognition is achieved by the proposed method. The validity of the proposed method was confirmed through 7 surface recognition experiments.

For many robot operations force control is needed, but force sensors may be expensive and add mass to the system. An alternative is to use the motor torques, though friction causes large disturbances. The Coulomb friction can be quite well known when a joint is moving, but has much larger uncertainties for velocities close to zero. This paper presents a method for force estimation that accounts for the velocity-dependent uncertainty of the Coulomb friction and combines data from several joints to produce accurate estimates. The estimate is calculated by solving a convex optimization problem in real time. The proposed method was experimentally evaluated on a force-controlled dual-arm assembly operation and validated with data from a force sensor. The estimates were shown to improve with the number of joints used, and the method can even exploit data from an arm that is controlled not to move.

This paper considers the problem of performing mid-ranging control of two closed-loop controlled systems that have internal saturations. The problem originates from previous work in machining with industrial robots, where an external compensation mechanism is used to compensate for position errors. Because of the limited workspace and the considerably higher bandwidth of the compensator, a mid-ranging control approach is proposed. An adaptive, modelbased solution is presented, which is verified through simulations and experiments, where a close correspondence of the obtained results is achieved. Comparing the IAE of experiments using the proposed controller to previously established methods, a performance increase of up to 56 % is obtained.

Industrial robots are usually position controlled, which requires high accuracy of the robot and the workcell. Some tasks, such as assembly, are difficult to achieve by only using position sensing. This work presents a framework for robotic assembly, where a standard position-based robot program is integrated with an external controller performing force-controlled skills. The framework is used to assemble emergency stop buttons that were tailored to be assembled by humans.

The traditional way of controlling an industrial robot is to program it to follow desired trajectories. This approach is sufficient as long as the accuracy of the robot and the calibration of the workcell is good enough. In robotic assembly these conditions are usually not fulfilled, because of uncertainties, e.g., variability in involved parts and objects not gripped accurately. Using force control is one way to handle these difficulties. This paper presents a method of doing force control without a force sensor. The method is based on detuning of the low-level joint control loops, and the force is estimated from the control error. It is experimentally verified in a small part assembly task with a kinematically redundant robotic manipulator.

This paper presents a novel control architecture for enhanced operator awareness and improved safety in telesurgical systems. We introduce an intermediate layer between the master and slave sides which allows us to modify the slave motion for safety and the force feedback for operator awareness. The intermediate layer is then designed with a performance objective for the specific task at hand. The control scheme is validated via experiments using a suture and an industrial manipulator. More specifically we show that operator awareness should be implemented as an integrated part of the safety level to maintain safety during surgery.

In absence of covariance data, Kalman filters are usually initialized by guessing the initial state. Making the variance of the initial state estimate large makes sure that the estimate converges quickly and that the influence of the initial guess soon will be negligible. If, however, only very few measurements are available during the estimation process and an estimate is wanted as soon as possible, this might not be enough. This paper presents a method to initialize the Kalman filter without any knowledge about the distribution of the initial state and without making any guesses.

Modern industrial robots are fast and have very good repetitional accuracy, which have made them indispensable in many manufacturing applications. However, they are usually programmed to follow desired trajectories and only get feedback from position sensors. This works fine as long as the environment is very well structured, but does not give good robustness to objects not being positioned or gripped accurately. A solution is to use additional sensing, such as force sensors and vision. How to combine the data from the different sensors and use it in a good way to control the robot is still an area of research. This paper describes an assembly scenario where a switch should be snapped into place in a box. Force sensing is used to resolve the uncertain position of the parts and detect the snap at the end of the operation. During the assembly an uncertain distance is estimated to improve the performance. By performing the assembly several times, learning is used to generate feed-forward data, which is used to speed up the assembly.

A drilling task requires a mechanism with five degrees of freedom, in order to achieve the correct position and orientation of the drilling tool. When performed with a standard 6-axes industrial robot, this task leaves an extra degree of freedom that can be exploited in order to achieve any additional criterion. Unfortunately, typical industrial robotic control architectures do not allow the user to modify the inverse kinematics algorithm, and thus to solve task redundancy following any specified criterion. In this paper a method to enforce an arbitrary redundancy resolution criterion on top of an industrial robot controller is discussed and applied to the execution of a drilling task. The extra degree of freedom is used to perform a torque-effective drilling. Experimental results achieved on the ABB IRB 140 industrial robot are presented.

This paper presents a method for modeling and control of a piezo-actuated high-dynamic compensation mechanism for usage together with an industrial robot during a machining operation, such as milling in aluminium. The machining spindle was attached to the compensation mechanism and the robot held the workpiece. Due to the inherent resonant character of mechanical constructions of this type, and the nonlinear phenomena appearing in piezo actuators, control of the compensation mechanism is a challenging problem. This paper presents models of the construction, experimentally identified using subspace-based identification methods. A subsequent control scheme, based on the identified models, utilizing state feedback for controlling the position of the spindle is outlined. Results from experiments performed on a prototype of the compensation mechanism are also provided.

To be able to determine the position of a static object in 3D space by means of computer vision, it has to be seen by cameras from at least two different view points. The same applies for measuring the position of a moving object based on images captured at one single time instant. However, if the cameras are not synchronized in time, or if a moving object is not visible in all images, one can not rely on using matching pictures for making accurate position estimates of dynamical objects. This paper presents a strategy to track an object with known dynamical model, using a series of images where no pair has to be captured simultaneously. It even allows tracking of a point object in 3D space using a single static camera.

Stability problems associated with haptics and robot control with obstacle avoidance are analyzed. Obstacle avoidance algorithms are revised to accomplish stable redesign using absolute stability and passivity theory. A modification of potential functions for haptic rendering and obstacle avoidance allowing stable operation for high stiffness is proposed. The modification leads to velocity-dependent potential-like repulsive stable haptic force interaction with obstacles. Using strictly positive real re-design, stable force interaction can be provided also for high stiffness of manipulated objects or obstacles.

A method for computer assisted kinematic calibration of a modular Gantry-Tau parallel robot is presented and tested in an experiment. A computer tool developed executes the first step of a kinematic calibration, the choice of appropriate measurement points, using a priori knowledge about kinematic parameters, e.g. obtained with a measuring tape. This step is performed by intersecting the robot's kinematic workspace and the area surveyable by the measurement device and overlaying it with a grid of a desired number of measurement points. A simulation determines automatically whether the choice leads to an accurate calibration and outputs a trajectory readable by a robot controller. In a calibration experiment it is shown that the method gives results with an accuracy comparable to that of manual calibration. The method allows non-experts to execute kinematic calibration of modular robots after reconfiguration thus making possible the use of modular robots in small size enterprises where such robots can answer the need for flexibility required with regularly changing tasks.

Control of robotic systems subject to friction phenomena is an important issue since growing demands on accuracy require elimination of friction disturbances. If several models-e.g., friction model, rigid-body dynamics-are required to describe the behavior with high precision, each model requires the knowledge of numerous parameters (perhaps time-varying) as well as an increased number of signals and sensors. In order to tackle this double limitation, an observer is proposed, addressing both the problem of velocity reconstruction and friction estimation in the joint of an inverted pendulum. Firstly, an adaptive two-level velocity observer is defined to reconstruct relevant unmeasured velocities, using estimation of the friction model error. Secondly, the observer is exploited for model-based friction compensation. Capabilities of the algorithm proposed are demonstrated by means of experiments on the Furuta pendulum.

Force feedback is necessary for accurate force control in robotic manipulators, and thus far, wrist force/torque (F/T) sensors have been used. But an important problem arises when only these types of sensors are used. In a dynamic situation where the manipulator moves in either free or constrained space, the interaction forces and moments at the contact point and also the noncontact ones are measured by the mentioned sensor. In this paper, an estimator based on a sensor fusion strategy integrating the measurements of three different sensors (a wrist F/T sensor, an inertial sensor, and joint sensors) was developed to determine the contact force and torque exerted by the manipulator to its environment. The resulting observer helps to overcome some difficulties of uncertain world models and unknown environments since it reduces the high-frequency and low-frequency spectral contents, i.e., the low-frequency component due to inertia of a heavy tool mass and the high-frequency component due to impacts. The new improvement was experimentally validated in a force/position impedance control loop applied to a StÄubli RX60 industrial robotic platform.

Affordable and competitive industrial automation is of key importance for small and medium enterprises, in Europe and elsewhere. A key factor is the introduction of new robot automation concepts that ease fast deployment and extend available task repertoire. The Gantry-Tau manipulator is a new robot concept. In contrast to other parallel kinematic manipulators (PKMs), it has a large working range. The high stiffness makes it ideal for a wide range of tasks such as grinding, deburring, and cutting. An additional aspect of such a PKM is the modularity, which in this work has been studied in terms of possibilities for assembly and mechanical reconfiguration at the end-user site, integration of such a kinematically different robot with a standard industrial controller, and new needs for methods/tools to support simple (re)configuration. What is needed for fully utilizing the modularity of the concept in typical SME manufacturing scenarios? A range of software tools and methods were found to be useful and necessary for efficient engineering and integration. For experimental evaluation, a full-scale prototype robot was designed and built, the kinematic software was developed and integrated into the ABB kinematics software, robot CAD software was adapted to the configuration needs, and both simulations and physical experiments were carried out. Our findings make us believe that enhanced software tools should be integrated on a higher symbolic (or meta-) level to better support transformation of data and code generation, but also that the Gantry-Tau type of robot (with adequate software support) will bring a new dimension of flexibility into SME manufacturing.

In this article, a new kinematic and a dynamic model for a 3-degree-of-freedom Gantry-Tau parallel kinematic robot are presented. Similar to an earlier proposed kinematic model, the dynamic model is based on the assumption of parallel actuator axes and constant end-effector orientation. The new kinematic model takes into account rotations of actuator axes which do not affect the end-effector orientation. Results from a calibration experiment show an improved positioning accuracy of the new kinematic model. A more general model of the robot including possible geometric inaccuracies causing deviations from the model assumptions has been developed in the modeling language Modelica. In a simulation, the proposed dynamic model is tested as a feedforward term in a control application and is found to improve the tracking performance considerably.

The problem of robot drilling presents a significant challenge, due to the comparatively low mechanical stiffness of typical serial industrial robots. This compliance makes the robot deflect due to the cutting forces, resulting in poor hole quality. Recently, functionality for high-bandwidth force control has found its way into industrial robot control systems. This could potentially open up the possibility of robotic drilling systems with improved performance, using only standard systems without costly extra hardware and calibration techniques. In this paper, we present methods and systems for force-controlled robot drilling, based on active suppression of drill sliding through a model-based force control scheme. The methods are validated in a number of drilling experiments using an industrial robot.

The Furuta pendulum consists of an arm rotating in the horizontal plane and a pendulum attached to its end. Rotation of the arm is controlled by a DC motor, while the pendulum is moving freely in the plane, orthogonal to the arm. Motivated, in particular, by possible applications for walking/running/balancing robots, we consider the Furuta pendulum as a system for which synchronized periodic motions of all the generalized coordinates are to be created and stabilized. The goal is to achieve, via appropriate feedback control action, orbitally exponentially stable oscillations of the pendulum of various shapes around its upright and downward positions, accompanied with oscillations of the arm. Our approach is based on the idea of stabilization of a particular virtual holonomic constraint imposed on the configuration coordinates, which has been theoretically developed recently. Here, we elaborate on the complete design procedure. The results are illustrated not only through numerical simulations but also through successful experimental tests.

In this work, we present implementation and experiment of the theory of dynamic force sensing for robotic manipulators. In the robot manipulation context, end-effector contact forces may be difficult to measure due to tool interference, yet indirect measurement such as from wrist-mounted force sensors provide force measurement contaminated by inertial forces of the tool distal to the force sensor. In order to extract the contact force exerted by the robot, it is necessary to separate the contact forces from inertial forces. We propose a complete formulation and implementation of a new control strategy based on multi-sensor fusion with three different sensors - that is, encoders mounted at each joint of the robot with six degrees of freedom, a wrist force sensor and accelerometers - whose goal is to obtain a suitable contact force estimator. These new observers contribute to overcome many of the difficulties of uncertain world models and unknown environments, which limit the domain of application of contemporary robots used without external sensory feedback; in addition, the final observer can be applied to any kind of real robot system. An impedance control scheme was proposed to verify the improvement. The experiments were carried out on an industrial Staubli RX60 manipulator with open control system architecture

Many contact operations in robotics require accurate positioning, which is made difficult by the presence of rapidly varying interaction forces and compliances in gear boxes and links. In order to compensate for such effects, rapid feedback from the measured tool position in several degrees of freedom is needed. This paper presents a dynamic visual tracking technique based directly on intensity measurements in the image, which can be used to obtain state estimates at a very high rate, and with very short input-output latency. Methods for analysis of the stability and sensitivity to disturbances are presented, and an improved version for better disturbance suppression of illumination variations and noise is developed. Positioning experiments using an industrial robot with camera feedback at 250 Hz are used to validate the approach

In this paper, the results from a joint industry-academia project in industrial robotic force control are presented. The extension and implementation of an external sensor system for an industrial robot system, which can be used for high-bandwidth force control, are described. Results from two industrial applications using the system are presented, a stub grinding application using a new compliant grinding end-effector integrated with the robot control system, and a deburring application with a stiff tool requiring high-bandwidth force control in six degrees of freedom. Using the system an easily reconfigurable control structure was achieved, which was able to control contact forces with a sampling bandwidth of an order of magnitude higher than for conventional robot controllers

In this paper, we propose a method for self-calibration of a robotic manipulator force observer, which fuses information from force sensors and accelerometers in order to estimate the contact force exerted by a manipulator to its environment, by means of active motion. In robotic operation, during contact transition accelerometers and force sensors play a very important role and serve to overcome many of the difficulties of uncertain world models and unknown environments, limiting the domain of application of current robots used without external sensory provided. The calibration procedure helps to improve the performance as well as enhanced stability and robustness for the transition phase. A variety of accelerometers were used to validate the procedure. A dynamic model of the robot-grinding tool using the new sensors was obtained by system identification. An impedance control scheme was proposed to verify the improvement. The experiments were carried out on an ABB industrial robot with open control system architecture.

In this work, we present implementation and experiment of the theory of dynamic force sensing for robotic manipulators, which uses a sensor fusion technique in order to extract the contact force exerted by the end-effector of the manipulator from those measured by a wrist force sensor, which are corrupted by the inertial forces on the end-effector. We propose a new control strategy based on multisensor fusion with three different sensors— that is, encoders mounted at each joint of the robot with six degrees of freedom, a wrist force sensor and an accelerometer— whose goal is to obtain a suitable contact force estimator for the three Cartesian axes. This new observer contributes to overcome many of the difficulties of uncertain world models and unknown environments, which limit the domain of application of currents robots used without external sensory feedback. An impedance control scheme was proposed to verify the improvement. The experiments were carried out on an ABB industrial robot with open control system architecture.

In this paper, robotic sensor fusion of acceleration and force measurement is considered. We discuss the problem of using accelerometers close to the end-effectors of robotic manipulators and how it may improve the force control performance. We introduce a new model-based observer approach to sensor fusion of information from various different sensors. During contact transition, accelerometers and force sensors play a very important role and it can overcome many of the difficulties of uncertain models and unknown environments, which limit the domain of application of currents robots used without external sensory feedback. A model of the robot-grinding tool using the new sensors was obtained by system identification. An impedance control scheme was proposed to verify the improvement The experiments were carried out on an ABB industrial robot with open control system architecture.

A flexible method for six-degree-of-freedom combined vision/force control for interaction with a stiff uncalibrated environment is presented. An edge-based rigid-body tracker is used in an observer-based controller, and combined with a six-degree-of-freedom force- or impedance controller. The effect of error sources such as image space measurement noise and calibration errors are considered. Finally, the method is validated in simulations and a surface following experiment using an industrial robot.

This paper presents theoretical and experimental results on observer-based impedance control for force control without velocity feedback. As the velocity may not available to measurement, which is often the case for industrial robots, an observer was designed to reconstruct velocity in such a way that it be useful for stabilizing feedback control and to modification of the damping in the impedance relationship. A good model of the robot joint used was obtained by system identification. Experiments were carried out on an ABB industrial robot 2000 to demonstrate results on observer-based SPR feedback applied in the design. Stability was shown via a modified Popov criterion.

In this paper a method for contour-based rigid body tracking with simultaneous camera calibration is developed. The method works for a single eye-in-hand camera with unknown hand-eye transformation, viewing a stationary object with unknown position. The method uses dual quaternions to express the relationship between the camera and end-effector screws. It is shown how using the measured motion of the robot end-effector can improve the accuracy of the estimation, even if the relative position and orientation between sensor and actuator is completely unknown. The method is evaluated in simulations on images from a real-time 3D rendering system. The system is shown to be able to track the pose of rigid objects and changes in intrinsic camera parameters, using only rough initial values for the parameters. The method is finally validated in an experiment using real images from a camera mounted on an industrial robot.

When designing flexible multi-sensor based robot systems, one important problem is how to combine the measurements from disparate sensors such as cameras and force sensors. In this paper, we present a method for combining direct force control and visual servoing in the presence of unknown planar surfaces. The control algorithm involves a force feedback control loop and a vision based reference trajectory as a feed-forward signal. The vision system is based on a constrained image-based visual servoing algorithm designed for surface following, where the location and orientation of the planar constraint surface is estimated online using position-, force- and visual data. We show how data from a simple and efficient camera calibration method can be used in combination with force and position data to improve the estimation and reference trajectories. The method is validated through experiments involving force controlled drawing on an unknown surface. The robot will grasp a pen and use it to draw lines between a number of markers drawn on a white-board, while the contact force is kept constant. Despite its simplicity, the performance of the method is satisfactory.

Iterative learning control (ILC) is used as a means for task execution of a time sub-optimal trajectory. The use of cascaded ILC procedures in a robot application is experimentally shown to drastically enlarge the region of convergence and efficiently compensate for unmodeled dynamics in the motion system. The paper deals with a control problem common in machines for packaging fluids. Objectives considered are the duration of motion, maximum liquid slosh during the motion, and the residual slosh after the motion.

In this paper we present an observer-based adaptive control scheme for robot manipulators, for which we have both unmeasured velocity and uncertain parameters. Using the observer backstepping method, a reduced-order adaptive velocity observer can be designed independently from the state-feedback controller, which uses damping terms to compensate the presence of the estimation error in the tracking error dynamics. The resulting closed-loop system is semiglobally asymptotically stable with respect to the estimation error and tracking errors. Furthermore a simulated example shows the performance of the control scheme applied to a two-link manipulator.

Based on information produced by a 200 kHz ultrasonic echo device developed for robotics and other industrial purposes, a method for object identification was developed and is presented in this paper. Interpretation of the echos from the reflecting objects, complexity analysis and information extraction were made by system identification methods such as impulse response analysis and state-space realization. A clustering-type discrimination of different objects based on the echo identification result was made in a subsequent step using the least-squares method. Experimental evaluation showed that the method is effective for object recognition.

This paper presents algorithms for continuous-time quadratic optimization of impedance control. Explicit solutions to the Hamilton-Jacobi equation for optimal control of rigid-body motion are found by solving an algebraic matrix equation. System stability is investigated according to Lyapunov function theory, and it is shown that global asymptotic stability holds. The solution results in design parameters in the form of square weighting matrices or impedance matrices as known from linear quadratic optimal control. The proposed optimal control is useful both for motion control and force control.>

Algorithms are presented for continuous-time quadratic optimization of motion control. Explicit solutions to the Hamilton-Jacobi equation for optimal control of rigid-body motion are found by solving an algebraic matrix equation. The system stability is investigated according to Lyapunov function theory, and it is shown that global asymptotic stability holds. It is also shown how optimal control and adaptive control may act in concert in the case of unknown or uncertain system parameters. The solution results in natural design parameters in the form of square weighting matrices as known from linear quadratic optimal control. The proposed optimal control is useful for motion control, trajectory planning, and motion analysis.>

Algorithms for continuous-time direct adaptive control of robot manipulators are presented. Lyapunov theory is used for controller design and stability investigation. Algorithms for rapid continuous-time adaptive control are also presented.>