In tendon-driven robotic hands, tendons are usually routed along several pulleys. The resulting friction is often substantial, and must therefore be modelled and estimated, for instance for accurate control and contact detection. Common approaches for friction estimation consider special dedicated setups, where the parameters of a static or dynamic friction model at a single contact point are determined. In this paper, we rather combine such individual friction models into an overall friction model for the entire finger. Furthermore, we propose a method for estimating the parameters of this overall model in situ, i.e. from trajectories executed on the assembled hand, avoiding the need for dedicated setups. An important component of the proposed model is a varying bias for treating friction at low velocities, allowing a simpler static friction model to be used. We demonstrate that our approach enables contacts to be detected more accurately on the DLR David hand, without additional sensors.

In contrast to underactuated robotic hands the DLR AWIWI II hand of the David robot is fully controllable because each finger with 4 joints is actuated by 6 or 8 tendons respectively. For such fingers all joint angles (generalized positions) or joint torques (generalized forces) can be controlled independently. Usually, the specifications in joint space are converted to desired tendon forces or motor torques, which are regulated by an inner loop impedance controller. However, this conversion typically exhibits couplings between the components of the joint angle vector or the joint torque vector respectively, which arise when using the well known equations. Therefore the usual force control and position control schemes are reviewed and a generic computation of the desired tendon forces is presented. This is also done for the control of the Cartesian position and force at the finger endpoint. Thus the main contribution of the paper is the inhibition of couplings in joint space or at the Cartesian endpoint. This is demonstrated in simulations of the index finger of the DLR David hand.

Industrial robots are often used for the simulation of satellites during on orbit servicing. In order to cover also the docking phase, both robots are equipped with force-torque sensors, and the measured forces and torques are taken to compute the desired motion of the position controlled robots. Since the system dynamics of robots and of free floating bodies obviously differ, for each robot we distinguish between the really executed and the assumed satellite motion. The difference between the two motions is used to adapt the measured forces in such a way that they correspond to the satellite's trajectory. In this way the docking procedure can be visualized by two robots which closely follow the satellites' trajectories. Stability of the robot control is not compromised even if the dynamics of the satellites and the robots are totally different. Simulation results verify the approach.

Stopping the motion of industrial robots in response to warnings or unexpected sensor data is a special case of trajectory generation. In contrast to emergency stops, here the robot has to satisfy the limits of the acceleration and the jerk. In addition, during the deceleration the robot must follow the path accurately, i.e., the shape of the original path may not be left. This is usually done by scaling the desired velocity. However, for curved paths, e.g. those generated by blending of linear motion commands, by sensor corrections, or directly by splines, this method may leave the desired path. The problem is solved by interpolation using the arc length. In contrast to other methods, here the constraints are considered directly, resulting in a time-efficient computation. Finally, the proposed method prevents a rebound caused by the jerk limits when reaching zero velocity. Experiments are presented using a stiff KUKA robot whose path is exactly tracked during deceleration.

The paper considers an a priori given robot trajectory which has to be recomputed when online sensed information on the environment is available. Then the original trajectory is adapted in order to continue the so far commanded motion by the sensed geometric shape. The adapted trajectory has to comply with restrictions on velocity, acceleration and jerk. Furthermore it is desired to converge to the original trajectory. At least if the robot is in contact with the environment it is further essential that the geometrical path is not left when modifying the trajectory. This means that preferably only the temporal profile is changed by scaling or rescaling the velocity. In order to inhibit overshooting, future restrictions are predicted and backtracked in the case of a violation. All this computation is done within a single sampling step, i.e. within 4 ms for a standard KUKA industrial robot. This precludes accurate optimization algorithms. When applied without an a priori given trajectory the method results in an near time-optimal solution.

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.

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 sensor's 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.

Typical industrial assembly tasks require an accuracy that cannot be realized by only feedback control if a minimum speed is given by a conveyor. Feed-forward has proven to be advantageous, using predictions of the desired trajectory which will be computed from sensor values. These predictions are improved by a model based classification of the sensor data to typical scenarios. In contrast to linear controllers this assures the fastest possible response to external disturbances, in spite of large dynamical delays. The method is demonstrated by assembling wheels to a car body that is moved by a conveyor, fusing sensor data using an extended Kalman filter.

Within assembly lines, wheel assembly to continuously conveyed car bodies is still executed by human workers using a device that compensates the weight of the wheel. This paper presents a solution in which a robot autonomously assembles and fixes the wheels. The approach uses a sensor-driven control strategy that compensates a possible temporal or spatial offset. Three types of sensors are proposed for adequate perception of the wheel hub. Their signals are fused by a Kalman filter that allows predictions in the time domain. Finally, a feed-forward controller is used, that is designed to consider the predictions in order to minimize dynamical delays. The control is driven by a special task description that extents usual robot programming methods.

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.

When industrial robot arms are controlled using sensor data the performance is dependent on the sensor sampling rate, on delays in signal processing, and on the robot dynamics. The paper presents an approach in which control is inherently stable as long as the time instant of sensing is known, independently of delays. In addition to sensor data the method uses the actual robot pose to compute a desired pose which is then controlled by the existing positional control loop. Updated sensor data affect the system as a refined target for positional control. So the positional control and the use of sensor data are decoupled. This is useful for the integration of a priori information on the task. The method is applicable especially for force control tasks as contour following and for visual servoing.

For precise control of robots along paths which are sensed online it is of fundamental importance to have a calibrated system. In addition to the identification of the sensor parameters - in our case the camera calibration - we focus on the adaptation of parameters that characterize the integration of the sensor into the control system or the application. The most important of such parameters are identified best when evaluating an application task, after a short pre-calibration phase. The method is demonstrated in experiments in which a robot arm follows a curved line at high speed.

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.

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 robot's 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.

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.

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.>

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.>