Andreas Stolt

Force controlled robotic assembly without a force sensor

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.

On the integration of skilled robot motions for productivity in manufacturing

Robots used in manufacturing today are tailored to their tasks by system integration based on expert knowledge concerning both production and machine control. For upcoming new generations of even more flexible robot solutions, in applications such as dexterous assembly, the robot setup and programming gets even more challenging. Reuse of solutions in terms of parameters, controls, process tuning, and of software modules in general then gets increasingly important. There has been valuable progress within reuse of automation solutions when machines comply with standards and behave according to nominal models. However, more flexible robots with sensor-based manipulation skills and cognitive functions for human interaction are far too complex to manage, and solutions are rarely reusable since knowledge is either implicit in imperative software or not captured in machine readable form. We propose techniques that build on existing knowledge by converting structured data into an RDF-based knowledge base. By enhancements of industrial control systems and available engineering tools, such knowledge can be gradually extended as part of the interaction during the definition of the robot task.

Force controlled assembly of emergency stop button

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.

Robotic force estimation using motor torques and modeling of low velocity friction disturbances

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.

Robotic force estimation using dithering to decrease the low velocity friction uncertainties

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.

On force control for assembly and deburring of castings

Traditional industrial robots have problems interacting with an uncalibrated, ill-defined environment where part geometry and position may vary. Active force control technology has therefore been suggested as a solution to add the extra sensory dimension needed to handle manufacturing tasks like assembly and deburring. The technology is proposed to give increased flexibility compared to other solutions and force control systems are available commercially. Active force control installations, however, are still uncommon in industry. This paper presents two cases of force control applications; assembly of a compliant carbon fiber structure and deburring/cleaning of iron castings. Based on these two cases, some issues are raised concerning how the technology can be further developed to fit the industrial setting, and the proposed benefits are re-examined and refined. The two cases show that programming, parameter setting and ease of use are critical components in lowering the industrial threshold, together with increased possibilities for application-specific compensation and filtering. Force control does, however, show great potential in extending the boundaries for variance in product and equipment like grippers and fixtures as well as decreasing the need for calibration of for example virtual models used for programming compared to traditional automated solutions.

Robotic assembly of emergency stop buttons

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.

Adaptation of Force Control Parameters in Robotic Assembly

Industrial robots are usually programmed to follow desired trajectories, and are very good at position-controlled tasks. New applications, however, often require physical contact between the robot and its environment, and then the position control accuracy is generally not sufficient. Force control is a suitable alternative. The environment is often stiff, and then it is crucial to design appropriate force controllers, which is non-trivial for a robot programmer. This paper presents an adaptive algorithm for choosing force control parameters, based on identification of a contact model. The algorithm is experimentally verified in an assembly task with an industrial robot.

Detection of contact force transients in robotic assembly

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.

Reactive Task Adaptation Based on Hierarchical Constraints Classification for Safe Industrial Robots

A widespread and flexible use of robots in rapidly changing working environments could be greatly enhanced by human-robot interaction and collaboration. Humans and robots have complementary skills. The robotic worker can relieve the human from repetitive work, and the human can make robot deployment easier by managing nonstandard or particularly skilful operations. Such a scenario, however, requires new safety systems to preserve human workers from potential danger and at the same time to make human-robot interaction productive and advantageous. In this paper, a system for safe and task consistent human-robot interaction integrated with an industrial controller is proposed. The robot executes evasive motions to avoid impacts with obstacles consistently with the task. A classification of constraints constituting the task is proposed and a safety strategy based on such classification is defined. This paper finally presents integration of the safety system with an industrial controller and experimental validation on an assembly operation.