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.

Traditional power reduction techniques such as DVFS or RAPL are challenging to use with web services because they significantly affect the services’ latency and throughput. Previous work suggested the use of controllers based on control theory or machine learning to reduce performance degradation under constrained power. However, generating these controllers is challenging as every web service applications running in a data center requires a power-performance model and a fine-tuned controller. In this paper, we present DDPC, a system for autonomic data-driven controller generation for power-latency management. DDPC automates the process of designing and deploying controllers for dynamic power allocation to manage the power-performance trade-offs for latency-sensitive web applications such as a social network. For each application, DDPC uses system identification techniques to learn an adaptive power-performance model that captures the application’s power-latency trade-offs which is then used to generate and deploy a Proportional-Integral (PI) power controller with gain-scheduling to dynamically manage the power allocation to the server running application using RAPL. We evaluate DDPC with two realistic latency-sensitive web applications under varying load scenarios. Our results show that DDPC is capable of autonomically generating and deploying controllers within a few minutes reducing the active power allocation of a web-server by more than 50% compared to state-of-the-art techniques while maintaining the latency well below the target of the application.

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 the framework of robotics, Reinforcement Learning (RL) deals with the learning of a task by the robot itself. This paper presents a hierarchical-planning approach in which the robot learns the optimal behavior for different levels in a decoupled way. For high-level discrete actions, Q-learning was chosen, whereas for the low level we utilize Policy Improvement with Path Integrals (PI2) algorithm to learn the parameters of policies, represented by rhythmic Dynamic Movement Primitives (DMPs). The paper studies the case of a 4-finger-gripper manipulator, which performs the task of continuously spinning a ball around a desired axis. The results demonstrate the efficacy of the hierarchical planning and the increased performance of the task when PI2is used in conjunction with rhythmic DMPs in a real environment.

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.

The spot pricing scheme has been considered to be resource-efficient for providers and cost-effective for consumers in the Cloud market. Nevertheless, unlike the static and straightforward strategies of trading on-demand and reserved Cloud services, the market-driven mechanism for trading spot service would be complicated for both implementation and understanding. The largely invisible market activities and their complex interactions could especially make Cloud consumers hesitate to enter the spot market. To reduce the complexity in understanding the Cloud spot market, we decided to reveal the backend information behind spot price variations. Inspired by the methodology of reverse engineering, we developed a Predator-Prey model that can simulate the interactions between demand and resource based on the visible spot price traces. The simulation results have shown some basic regular patterns of market activities with respect to Amazon's spot instance type m3.large. Although the findings of this study need further validation by using practical data, our work essentially suggests a promising approach (i.e.~using a Predator-Prey model) to investigate spot market activities.

3-DOF Gantry Tau is a type of parallel robot, consisting of six struts configured in three clusters, which provides three translational DOFs. It has increasing industrial use in applications where large workspace and high stiffness are required. In fact, the concept of dynamic isotropy, where all the natural frequencies of a system are equal, can be employed in order to effectively optimize the geometry of robots. However, no study on dynamic isotropy of Gantry Tau robots has yet been reported in the literature. In this paper, the problem of dynamic isotropy in 3-DOF Gantry Tau robots is analytically addressed. Firstly, the kinematics is established based on a general approach with 36 geometric variables. Jacobian and stiffness matrices are also investigated where the struts are considered to be axially flexible. Subsequently, analytical solutions to obtain both a decoupled stiffness matrix and a complete dynamic isotropy are presented. Finally, as an example, dynamically isotropic geometries of a Gantry Tau robot are calculated, for a reference platform, using the developed analytical method.

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.

Estimating Cartesian contact forces and torques enables external force supervision for robotic manipulators and even force-controlled applications while avoiding the need for additional external sensing. Redundant manipulators facilitate the problem of Cartesian contact force and torque estimation (CCFE) at the TCP, since an increased amount of joint level information is available for estimating the six components of external wrench. In this paper, we point out that the errors in CCFE estimates arising from inevitable uncertainties in available joint level information (either measurements or estimates) depend on the manipulator configuration. Based on this, an algorithm is proposed that resolves redundancy in an optimal way with respect of the achievable CCFE quality. For a given TCP pose, the joint configuration resulting in minimized CCFE errors is calculated. The proposed method is verified by means of experimental data gathered from a 7DOF manipulator.

As a cost-effective option for Cloud consumers, spot service has been considered to be a significant supplement for building a full-fledged market economy for the Cloud ecosystem. However, unlike the static and straightforward way of trading on-demand and reserved Cloud services, the market-driven regulations of employing spot service could be too complicated for Cloud consumers to comprehensively understand. In particular, it would be both difficult and tedious for potential consumers to determine suitable bids from time to time. To reduce the complexity in applying spot resources, we propose to use a feedback control to help make bidding decisions. Based on an arccotangent-function-type system model, our novel bidding mechanism imitates fuzzy and intuitive human activities to refine and issue new bids according to previous errors. The validation is conducted by using Amazon's historical spot price trace to perform a set of simulations and comparisons. The result shows that the feedback control-based mechanism obtains a better trade-off between bidding rationality and success rate than the other five comparable strategies. Although this mechanism is only for black-box bidding (price prediction) at this current stage, it can be conveniently and gradually upgraded to take into account external constraints in the future.

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.

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.

The flexibility demands in manufacturing are severe, e.g., for rapid-change-over to new product variants, while robots are flexible machines that potentially can be adapted to a large variety of production tasks. Task definitions such as explicit robot programs are hardly reusable from an application point-of-view. To improve the situation, a knowledge-based approach exploiting distributed declarative information and cloud computing offers many possibilities for knowledge exchange and reuse, and it has the potential to facilitate new business models for industrial solutions. However, there are many unresolved questions yet, e.g., those related to reliability, consistency, or legal responsibility. To investigate some of these issues, different knowledge-based architectures have been prototyped and evaluated by confronting the solution candidates with key application demands. The conclusion is that distributed cloud-based approaches offer many possibilities, but there is still a need for further research and better infrastructure before this approach can become industrially attractive.

Tight human-robot interaction and collaboration will characterize future robot tasks. Robot working environments will be increasingly unstructured, as safety barriers will be removed to allow a continuous cooperation of robotic and human workers. Such a working scenario calls for novel safety systems capable of combining productivity with workers' safety. In this paper, a method for the definition of a task-consistent collision avoidance safety strategy is presented. A classification of task constraints based on relevance for task completion is introduced. Control of task constraints enforcement is performed through a state machine. A template for such state machine is proposed. Experimental validation of the proposed safety system on a dual-arm industrial robot prototype is presented.

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.

We consider planning and implementation of fast motions for industrial manipulators constrained to a given geometric path. With such a problem formulation, which is quite reasonable for many standard operation scenarios, it is intuitively clear that a feedback controller should be designed to achieve orbital stabilization of a time-optimal trajectory instead asymptotic. We propose an algorithm to convert an asymptotically stabilizing controller into an orbitally stabilizing one and check achievable performance in simulations and, more importantly, in experiments performed on a standard industrial robot ABB IRB 140 with the IRC5-system extended with an open control interface. It is verified that the proposed re-design allows significantly reduced deviations of the actual trajectories from the desired one at high speeds not only for a chosen base feedback design but also outperforming the state-of-the-art commercial implementation offered by ABB Robotics.

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.

The use of industrial robots in the aircraft industry has been hampered by a combination of poor accuracy of the robots and poor calibration of the workcell, and also manufacturing variability in composite parts. A way to handle these difficulties is using force control. An experimental case where a semi-compliant rib is aligned to multiple surfaces is used as an example to show this. The constraint-based task specification framework is used for the modelling and control, and the search and alignment sequence required for the assembly is modeled with a state machine. An implementation on an industrial robot system is presented and experimental data is evaluated. The described approach is easy to apply to other fields and more complicated assembly operations as well.

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.

Multi-step ahead response time prediction of CPU constrained computing systems is vital for admission control, overload protection and optimization of resource allocation in these systems. CPU constrained computing systems such as web servers can be modeled as single server queuing systems. These systems are stochastic and nonlinear. Thus, a well-designed nonlinear prediction scheme would be able to represent the dynamics of such a system much better than a linear scheme. A nonlinear autoregressive neural network with exogenous inputs based multi-step ahead response time predictor has been developed. The proposed estimator has many promising characteristics that make it a viable candidate for being implemented in admission control products for computing systems. It has a simple structure, is nonlinear, supports multi-step ahead prediction, and works very well under time variant and non-stationary scenarios such as single server queuing systems under time varying mean arrival rate. Performance of the proposed predictor is evaluated through simulation. Simulations show that the proposed predictor is able to predict the response times of single server queuing systems in multi-step ahead with very good precision represented by very small mean absolute and mean squared prediction errors.

Advanced telecommunication applications are often based on a multi-tier architecture, with application servers and database servers. With a rapidly increasing development of cloud computing and data centers, characterizations of the dynamics for database servers during changing workloads will be a key factor for analysis and performance improvements in these applications. We propose a multi-step ahead response time predictor for database queries based on a nonlinear autoregressive neural network model with exogenous inputs. The estimator shows many promising characteristics which make it a viable candidate for being implemented in admission control products for database servers. Performance of the proposed predictor is evaluated through experiments on a lab setup with a MySQL-server.

For the last years, automation is widely used to relieve humans from repetitive tasks, primarily and firstly within manufacturing. However, for products with less ideal (or hard to model) properties, and when forces depends on human interaction, auto mated testing has not been explored until now. This work presents the analysis of the (human-dependent) motions/forces based on a fully implemented test case for car-seat testing. For emulation of the corresponding mechanical wear, an experimental test bench was developed. A sensor mat with a pressure gauge net was used in the test bench to determine the relevant loads, and the corresponding movements performed by the humans when sitting in a car seat were acquired by means of a photogrammetry system. Finally, to automate the reproduction of such movements by means of a dummy held by a robot, several controllers have been developed to regulate the force applied by the dummy on the seat. Simplicity and force-control performance for the human replication was also investigated in this work, showing the benefit of freely programmable (open) force control. The developed system has many practical applications, as allowing the analysis of the wearing caused by these movements on the seat upholstery. Thus, force controlled testing of fabrics using robots is a viable option.

Parallel robots are generally said to be more accurate but to have a smaller workspace compared to serial robots. With the Gantry-Tau robot, a parallel robot with a large, reconfigurable workspace was presented. This article tries to identify the maximum achievable end-effector positioning accuracy of the Gantry-Tau robot. To this end, a couple of new kinematic error models are presented and evaluated. The sources of the remaining positioning errors (0.09 mm) are discussed.

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.

High motion performance, stiffness, and accuracy are crucial for industrial robot applications, but these requirements are in practice contradictory. Using a novel type of robot, the so called Gantry Tau, new combinations of stiffness and accuracy are in principle possible, except for the backlash in the drive-trains of each joint. Existing techniques for backlash reduction are either mechanically complex or limit the mechanical bandwidth. This paper presents an approach based on dual motors connected in parallel to the load, such that the entire robot structure can be made practically backlash free by means of software and feedback control. Different control strategies are presented and evaluated in experiments ranging from a lab servo process via a table-sized robot to a large industrial implementation with several meters of working range. Special emphasis was on a dual motor test rig with a linear high-resolution scale (not yet used for feedback) where the combined motor torque was fully utilized for high acceleration, while reducing backlash by over 96%.

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.

Resource management in IT-enterprises gain more and more attention due to high operation costs. For instance, web sites are subject to very changing traffic-loads over the year, over the day, or even over the minute. Online adaption to the changing environment is one way to reduce losses in the operation. Control systems based on feedback provide methods for such adaption, but is in nature slow, since changes in the environment has to propagate through the system before being compensated. Therefore, feed-forward systems can be introduced that has shown to improve the transient performance. However, earlier proposed feed-forward systems have been based on offline estimation. In this article we show that off-line estimations can be problematic in online applications. Therefore, we propose a method where parameters are estimated online, and thus also adapts to the changing environment. We compare our solution to two other control strategies proposed in the literature, which are based on off-line estimation of certain parameters. We evaluate the controllers with both discrete-event simulations and experiments in our testbed. The investigations show the strength of our proposed control system.

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.

All service control nodes can be modelled as a server system with one or more servers processing incoming requests. In this paper we show how non-linear control theory may be used when analyzing admission control mechanisms for server systems. Two models are developed, one linear and one non-linear. We show that, due to the non-linearities appearing in a real server system, linear control theory is sufficient when designing controllers for these systems. With non-linear analysis, however, the dynamics of a server system may be analysed and taken care of by choosing the controller parameters appropriately.

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.