Friedrich Lange

Learning accurate path control of industrial robots with joint elasticity

An adaptive architecture for feedforward control of industrial robots with standard positional controller is presented. This approach explicitly considers robots with elastic joints. It assumes that the real position of the tool centre point (TCP) can be recorded for offline evaluation. Compensation of elasticity is trained in a feedforward controller. Adaptation takes place without any knowledge of the physical system model. The performance of the method is demonstrated in experiments with a 6-axis industrial robot KUKA KR6/1 for which real path errors during full speed motion are reduced by 70%. Reductions of 50% can be expected for untrained paths or other robots of the same type.

Predictive vision based control of high speed industrial robot paths

A predictive architecture is presented to react on sensor data in the case of high speed motion and low bandwidth sensor data. This concept is used for the vision based control of an industrial robot to track a contour at a speed of 1.6 m/s. The vision task can be performed very fast since only 2 rows of the image are analyzed. In this way an accuracy of 0.3 mm is reached in spite of uncertainties in robots kinematic parameters. Vision and control work asynchronously so that even delay times are tolerable during sensing as long as the time-instant of the exposure is known.

Learning force control with position controlled robots

The paper applies a previously presented method for accurate tracking of paths to force control. This approach is very simple since it does not require a joint torque/motor current interface but only a positional interface. It can be applied with elastic end-effectors (sensors) as well as with stiff environments where most elasticity is in the robot joints. In both cases deviations from the desired forces are transferred to positional deviations on joint level. The resulting path can then be controlled with high accuracy by a learned feedforward controller including the influence of the forces. The approach can be applied to the sensing of a contour or to the tracking of a known contour with high speed.

Iterative self-improvement of force feedback control in contour tracking

A very general three-level learning method for self-improvement of the parameters of a force feedback controller is demonstrated in contour tracking tasks. It is assumed that no model is known a priori, either of the robot or of the contour to be tracked. The system identifies such a model, including information about its reliability. The model and estimated noise were used to generate optimal control actions for the sample trajectory. They were then used for estimation of the parameters of the controller. This controller then produces a new trajectory, which in turn could be optimized and trained. Kalman filter techniques were applied in all adaptation levels involved. Learning was possible off-line or online. The model and controller may be based on linear difference equations or include nonlinear mappings as associative or tabular memories or neural networks. It was shown that even for a linear controller substantial improvements could be attained as no assumptions were needed about the bandwidth.<<ETX>>

Force and trajectory control of industrial robots in stiff contact

Position-based force control is presented, incorporating compliance in the robot joints and possibly in a force- / torque-sensor and/or the environment. First, the total compliance is identified. Then, in the control phase, the desired pose of the tool center point is computed from the force control error. Thus standard position control may be applied. This leads to an inherently stable control scheme, even with a low sampling rate of the sensor interface and unknown environmental compliance. The method is designed for applications of industrial robots, e.g. assembly tasks. Parallel control considers the existence of a reference trajectory which allows feedforward in force controlled directions. The paper further examines couplings between forces and torques, which are important for partially constrained configurations. A possible impact force is considered when colliding with an unexpected object.

Robot based system for the automation of flow assembly lines

In this paper a promising approach to the automation of flow assembly lines is presented. The developed system uses a standard industrial robot and synchronizes it to the product in all degrees of freedom. The synchronization is enabled by dividing the assembly process in different phases and controlling the robot in each phase with an adequate sensor system. Besides that a compliance is integrated into the gripper system in order to reduce high contact forces and tolerate high frequent pose deviations. Main advantages of the synchronized assembly are the avoidance of buffers and the reduction of the throughput time.

Predictive Visual Tracking of Lines by Industrial Robots

Many tasks for industrial robots can be described by high precision line following at high speed. This can be executed accurately if the lines are sensed by a camera since then not only the desired pose at the current time step is sensable, but also a segment of the desired path can be predicted. We propose polynomials to represent the progression of the elements of the desired pose. This allows us to realize a dynamical sensor control architecture that considers the two main problems: low sampling rate and delays in image processing, and deviations from commanded paths due to the robot dynamics. In contrast to previous publications we now present the complete formulae to control translation and orientation of the robot by tracking (curved) lines that are visible for a single eye-in-hand camera. Experiments using off-the-shelf hardware show that the robot can be precisely controlled at high speed.

New aspects of input shaping control to damp oscillations of a compliant force sensor

Compliance in robot mounted force/torque sensors is useful for soft mating of parts. However it generates nearly undamped oscillations when moving the end-effector in free space. In this paper, input shaping control is investigated to damp such unwanted flexible modes. We present a new design technique that creates long impulse sequences to adapt input shaping to systems with long sampling period and to compensate the resulting time delay. This makes the method feasible for industrial robots. In addition to the conventional input shaping which causes oscillations to stop only after applying the last impulse, we also minimize the quadratic control error until this time step is reached.

Learning to improve the path accuracy of position controlled robots

A learning method is presented which improves the dynamic accuracy of conventional industrial robots with integrated position control. The method is based on feedforward control being able to follow off-line programmed trajectories with high speed and negligible pose errors. For learning, the robot has to be moved along a given path. The algorithm then estimates a simple model. This model is used to build a controller which is able to modify positional commands, thus reducing the positional path error from some millimeters to approximately 0.2 mm for a Manutec r2 robot. This improvement is valid also for other, non-trained trajectories. For repetitive control of a single path the error is even lower. Measurements of path accuracy are verified using data of a force/torque sensor during tracking a known contour.<<ETX>>

Revised force control using a compliant sensor with a position controlled robot

A different way of force control is presented, that is especially advantageous for position controlled robots. Instead of usual force control laws we rely on the well tuned position control loop and just use the force sensor to measure the target pose or to predict the desired trajectory. In combination with a compliant sensor we introduce an inherently stable framework of force control which almost inhibits all control errors. After an unexpected impact the force error is reduced independently from the sensors bandwidth or delays in signal processing. Thus the (inevitable) impact force is more significant than the measured force control errors. The special case of a sensor that is mounted far away from a vertex-face contact is discussed, too.