Adaptation and Learning for Manipulators and Machining

This thesis presents methods for improving the accuracy and efficiency of tasks performed using different kinds of industrial manipulators, with a focus on the application of machining. Industrial robots offer a flexible and cost-efficient alternative to machine tools for machining, but cannot achieve as high accuracy out of the box. This is mainly caused by non-ideal properties in the robot joints such as backlash and compliance, in combination with the strong process forces that affect the robot during machining operations. In this thesis, three different approaches to improving the robotic machining accuracy are presented. First, a macro/micro-manipulator approach is considered, where an external compensation mechanism is used in combination with the robot, for compensation of high-frequency Cartesian errors. Two different milling scenarios are evaluated, where a significant increase in accuracy was obtained. The accuracy specification of 50 μm was reached for both scenarios. Because of the limited workspace and the higher bandwidth of the compensation mechanism compared to the robot, two different mid-ranging approaches for control of the relative position between the robot and the compensator are developed and evaluated. Second, modeling and identification of robot joints is considered. The proposed method relies on clamping the manipulator end effector and actuating the joints, while measuring joint motor torque and motor position. The joint stiffness and backlash can subsequently be extracted from the measurements, to be used for compensation of the deflections that occur during machining. Third, a model-based iterative learning control (ILC) approach is proposed, where feedback is provided from three different sensors of varying investment costs. Using position measurements from an optical tracking system, an error decrease of up to 84 % was obtained. Measurements of end-effector forces yielded an error decrease of 55 %, and a force-estimation method based on joint motor torques decreased the error by 38 %. Further investigation of ILC methods is considered for a different kind of manipulator, a marine vibrator, for the application of marine seismic acquisition. A frequency-domain ILC strategy is proposed, in order to attenuate undesired overtones and improve the tracking accuracy. The harmonics were suppressed after approximately 20 iterations of the ILC algorithm, and the absolute tracking error was r educed by a factor of approximately 50. The final problem considered in this thesis concerns increasing the efficiency of machining tasks, by minimizing cycle times. A force-control approach is proposed to maximize the feed rate, and a learning algorithm for path planning of the machining path is employed for the case of machining in non-isotropic materials, such as wood. The cycle time was decreased by 14 % with the use of force control, and on average an additional 28 % decrease was achieved by use of a learning algorithm. Furthermore, by means of reinforcement learning, the path-planning algorithm is refined to provide optimal solutions and to incorporate an increased number of machining directions

Force Controlled Grinding-The Cutting Edge

Sharpening knives by hand is both time-consuming and exhausting, and may still not always yield perfect results. This thesis investigates the possibility of sharpening knives with the use of a force-controlled industrial robot, regardless of the knifes shape. The procedure is performed by first identifying the shape of the knife, using Matlab Simulink to simulate the identifiation; two different types of force control are evaluated. Using an ABB IRB140B robot, the best performing controller is then used to identify a real knife. Based on the shape recorded by the robot, grinding experiments were successfully performed

Control Strategies for Machining with Industrial Robots

This thesis presents methods for improving machining with industrial robots using control, with focus on increasing positioning accuracy and controlling feed rate. The strong process forces arising during high-speed machining operations, combined with the limited stiffness of industrial robots, have hampered the usage of industrial robots in high-end machining tasks. However, since such manipulators may offer flexible and cost-effective machining solutions compared to conventional machine tools, it is of interest to increase the achievable accuracy using industrial robots. In this thesis, several different methods to increase the machining accuracy are presented. 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, is considered. Position control results from experiments are provided, as well as an experimental verification of the benefit of utilizing the online compensation scheme. It is shown that the milling surface accuracy achieved with the proposed compensation mechanism is increased by up to three times compared to the uncompensated case. Because of the limited workspace and the higher bandwidth of the compensator compared to the robot, a mid-ranging approach for control of the relative position between the robot and the compensator is proposed. An adaptive, model-based solution is presented, which is verified through simulations as well as experiments, where a close correspondence with the simulations was achieved. Comparing the IAE from experiments using the proposed controller to previously established methods, a performance increase of up to 56 % is obtained. Additionally, two different approaches to increasing the accuracy of the machining task are also presented in this thesis. The first method is based on identifying a stiffness model of the robot, and using online force measurements in order to modify the position of the robot to compensate for position deflections. The second approach uses online measurements from an optical tracking system to suppress position deviations. In milling experiments performed in aluminium, the absolute accuracy was increased by up to a factor of approximately 6 and 9, for the two approaches, respectively. Robotic machining is often performed using position feedback with a conservative feed rate, to avoid excessive process forces. By controlling the applied force, realized by adjusting the feed rate of the workpiece, precise control over the material removal can be exercised. This will in turn lead to maximization of the time-efficiency of the machining task, since the maximum amount of material can be removed per time unit. This thesis presents an adaptive force controller, based on a derived model of the machining process and an identified model of the Cartesian dynamics of the robot. The controller is evaluated in both simulation and an experimental setup

Force Controlled Knife-Grinding with Industrial Robot

This paper investigates the application of sharpening knives using a force controlled industrial robot, for an arbitrary knife shape and orientation. The problem is divided into different parts: calibration of the knife by identifying its unknown orientation, identification of the knife blade contour and estimation of its position in the robot frame through force control, and grinding of the knife, following the path defined by the earlier identified shape, while applying the desired contact force to the revolving grinding wheels. The experimental results show that the knives can be sharpened satisfactorily. An industrial application has also been developed and tested, and it has produced a sharpening quality equal or greater to that achieved manually

Adaptive Internal Model Control for Mid-Ranging of Closed-Loop Systems with Internal Saturation

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, model-based 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

Modeling and Control of a Piezo-Actuated High-Dynamic Compensation Mechanism for Industrial Robots

This paper presents a method for modeling and control of a piezo-actuated high-dynamic compensation mechanism (HDCM) for usage together with an industrial robot during a machining operation, such as milling in aluminum. The spindle is attached to the compensation mechanism and the robot holds 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. Experimental results performed on a prototype of the HDCM are also provided

On Cognitive Robot Woodworking in SMErobotics

This paper details and discusses work performed at the woodworking SME Mivelaz Techniques Bois SA within the SMErobotics FP7 project. The aim is to improve non-expert handling of the cell by introduction of cognitive abilities in the robot system. Three areas are considered; intuitive programming, process adaptation and system integration. Proposed cognitive components are described together with experiments performed

Learning Approach to Cycle-Time-Minimization of Wood Milling Using Adaptive Force Control

A majority of the machining processes in the industry of today is performed using position-controlled machine tools, where conservative feed rates have to be used in order to avoid excessive process forces. By instead controlling the process forces, the feed rate, and consequently the material removal rate, can be maximized. In turn, this leads to decreased cycle times and cost savings. Furthermore, path planning with respect to time-minimization for milling processes, especially in non-isotropic materials, is not straightforward. This paper presents a model-based adaptive force controller that achieves optimal feed rates, in combination with a learning algorithm to obtain the optimal machining path, in terms of minimizing the milling duration. The proposed solution is evaluated in both simulation and experiments, where an industrial robot is used to perform rough-cut wood-milling. Cycle-time reductions of 14% using force control compared to position control were achieved, and on average an additional 28% cycle-time reduction with the proposed learning algorithm

Increasing Time-Efficiency and Accuracy of Robotic Machining Processes Using Model-Based Adaptive Force Control

Machining processes in the industry of today are rarely performed using industrial robots. In the cases where robots are used, machining is often performed using position control with a conservative feed-rate, to avoid excessive process forces. There is a great benefit in controlling the process forces instead, so as to improve the time-efficiency by applying the maximum allowed force, and thus removing the maximum amount of material per time unit. This paper presents a novel adaptive force controller, based on a derived model of the machining process and an identified model of the robot dynamics. The controller is evaluated in both simulation and an experimental setup. Further, industrial robots generally suffer from low stiffness, which can cause the robot to deviate from the desired path because of strong process forces. The present paper solves this by employing a stiffness model to continuously modify the robot trajectory to compensate for the deviations. The adaptive force controller in combination with the stiffness compensation is evaluated in experiments, with satisfying results

Continuous-Time Gray-Box Identification of Mechanical Systems Using Subspace-Based Identification Methods

We consider the problem of gray-box identification of dynamic models for mechanical systems. In particular, the problem is approached by means of continuous-time system identification using subspace-based methods based on discrete-time input-output identification data. A method is developed, with the property that the structure of the model resulting from fundamental physical first principles is obtained and the parameter matrices have a clear physical interpretation. The proposed method is subsequently successfully validated in both simulation and using experimental data from a micro manipulator. In both cases the identified models exhibit good fit to the input-output data. The results indicate that the proposed method can be useful in the context of model-based control design in, for example, impedance force control for robots and manipulators, but also for modal analysis of mechanical systems