Parallel robots provide the potential to be lever-aged for human-robot collaboration (HRC) due to low collision energies even at high speeds resulting from their reduced moving masses. However, the risk of unintended contact with the leg chains increases compared to the structure of serial robots. As a first step towards HRC, contact cases on the whole parallel robot structure are investigated and a disturbance observer based on generalized momenta and measurements of motor current is applied. In addition, a Kalman filter and a second-order sliding-mode observer based on generalized momenta are compared in terms of error and detection time. Gearless direct drives with low friction improve external force estimation and enable low impedance. The experimental validation is performed with two force-torque sensors and a kinetostatic model. This allows a new identification method of the motor torque constant of an assembled parallel robot to estimate external forces from the motor current and via a dynamics model. A Cartesian impedance control scheme for compliant robot-environmental dynamics with stiffness from 0.1-2N/mm and the force observation for low forces over the entire structure are validated. The observers are used for collisions and clamping at velocities of 0.4-0.9 m/s for detection within 9–58 ms and a reaction in the form of a zero-g mode.

Parallel robots (PRs) offer the potential for safe human-robot collaboration because of their low moving masses. Due to the in-parallel kinematic chains, the risk of contact in the form of collisions and clamping at a chain increases. Ensuring safety is investigated in this work through various contact reactions on a real planar PR. External forces are estimated based on proprioceptive information and a dynamics model, which allows contact detection. Retraction along the direction of the estimated line of action provides an instantaneous response to limit the occurring contact forces within the experiment to 70 N at a maximum velocity of 0.4 m/s. A reduction in the stiffness of a Cartesian impedance control is investigated as a further strategy. For clamping, a feedforward neural network (FNN) is trained and tested in different joint angle configurations to classify whether a collision or clamping occurs with an accuracy of 80%. A second FNN classifies the clamping kinematic chain to enable a subsequent kinematic projection of the clamping joint angle onto the rotational platform coordinates. In this way, a structure opening is performed in addition to the softer retraction movement. The reaction strategies are compared in real-world experiments at different velocities and controller stiffnesses to demonstrate their effectiveness. The results show that in all collision and clamping experiments the PR terminates the contact in less than 130 ms.

Parallel robots (PRs) allow for higher speeds in human-robot collaboration due to their lower moving masses but are more prone to unintended contact. For a safe reaction, knowledge of the location and force of a collision is useful. A novel algorithm for collision isolation and identification with proprioceptive information for a real PR is the scope of this work. To classify the collided body, the effects of contact forces at the links and platform of the PR are analyzed using a kinetostatic projection. This insight enables the derivation of features from the line of action of the estimated external force. The significance of these features is confirmed in experiments for various load cases. A feedforward neural network (FNN) classifies the collided body based on these physically modeled features. Generalization with the FNN to 300k load cases on the whole robot structure in other joint angle configurations is successfully performed with a collision-body classification accuracy of 84% in the experiments. Platform collisions are isolated and identified with an explicit solution, while a particle filter estimates the location and force of a contact on a kinematic chain. Updating the particle filter with estimated external joint torques leads to an isolation error of less than 3 cm and an identification error of 4 N in a real-world experiment.

Robot manipulators are often deployed in tool-symmetric tasks, which only requires defining end effector position and pointing direction. In this case six-axis serial industrial robots and full-mobility (spatial) parallel robots have one degree of task redundancy. Using Cardan angles as orientation coordinates, a unified formulation of the position-level and second-order inverse kinematics problem is set up for both robot types. An efficient scheme for difference-quotient approximation of gradients of performance criteria for projection into the task redundancy's nullspace is presented. The simulation example of a hexapod robot shows that avoiding and exiting parallel robot singularities of type II is possible with the nullspace of all joints. The nullspace controller scheme can be used in offline trajectory optimization and in online motion generation.

In electromechanical motion systems performance measures such as positioning accuracy, dynamic stiffness and control bandwidth are severely limited by backlash. Several control schemes based on backlash compensation or switching control of hybrid systems are known, but many of these approaches require the exact backlash width as an input parameter. Several existing approaches for backlash identification are limited in accuracy because the load-side velocity is required but it is not directly measurable. In this paper a method for backlash identification tailored to electromechanical motion systems with rotary motor and translationally moving load is proposed. A mobile sensor board with inertial measurement unit (IMU) is mounted temporarily on the load and serves to acquire the accelerations during the experiment. The connection to the host-PC is wireless, time synchronisation is not required. It is shown in experiments on a testbed with an adjustable backlash coupling but otherwise i ndustry-like equipment that high accuracies can be achieved.

This work presents a semantic map management approach for various environments by triggering multiple maps with different simultaneous localization and mapping (SLAM) configurations. A modular map structure allows to add, modify or delete maps without influencing other maps of different areas. The hierarchy level of our algorithm is above the utilized SLAM method. Evaluating laser scan data (e.g. the detection of passing a doorway) triggers a new map, automatically choosing the appropriate SLAM configuration from a manually predefined list. Single independent maps are connected by link-points, which are located in an overlapping zone of both maps, enabling global navigation over several maps. Loop- closures between maps are detected by an appearance-based method, using feature matching and iterative closest point (ICP) registration between point clouds. Based on the arrangement of maps and link-points, a topological graph is extracted for navigation purpose and tracking the global robot's position over several maps. Our approach is evaluated by mapping a university campus with multiple indoor and outdoor areas and abstracting a metrical-topological graph. It is compared to a single map running with different SLAM configurations. Our approach enhances the overall map quality compared to the single map approaches by automatically choosing predefined SLAM configurations for different environmental setups.

The derivation of bright-grey box models for electric drives with coupled mechanics, such as stacker cranes, robots and linear gantries is an important step in control design but often too time-consuming for the ordinary commissioning process. It requires structure and parameter identification in repeated trial and error loops. In this paper an automated genetic programming solution is proposed that can cope with various features, including highly non-linear mechanics (friction, backlash). The generated state space representation can readily be used for stability analysis, state control, Kalman filtering, etc. This, however, requires several special rules in the genetic programming procedure and an automated integration of features into the defining state space form. Simulations are carried out with industrial data to investigate the performance and robustness.

This paper presents a holistic approach for door opening with a cartesian impedance controlled mobile robot, a KUICA KMR iiwa. Based on a given map of the environment, the robot autonomously detects the door handle, opens doors and traverses doorways without knowledge of a door model or the door's geometry. The door handle detection uses a convolutional neural network (CNN)-based architecture to obtain the handle's bounding box in a RGB image that works robustly for various handle shapes and colors. We achieve a detection rate of 100% for an evaluation set of 38 different door handles, by always selecting for highest confidence score. Registered depth data segmentation defines the door plane to construct a handle coordinate frame. We introduce a control structure based on the task frame formalism that uses the handle frame for reference in an outer loop for the manipulator's impedance controller. It runs in soft real-time on an external computer with approximately 20 Hz since access to inner controller loops is not available for the KMR iiwa. With the approach proposed in this paper, the robot successfully opened and traversed for 22 out of 25 trials at five different doors.

An auto-tuning method of PD controllers for robotic manipulators is proposed. This method suggests a practical implementation of the particle swarm optimization technique in order to find optimal gain values achieving the best tracking of a predefined position trajectory. For this purpose, The integral of the absolute error IAE is used as a cost function for the optimization algorithm. The optimization is achieved by performing the desired movement of the robot iteratively and evaluating the cost function for every iteration. Therefor, the necessary constraints that guarantee a safe and stable movement of the robot are defined, which are: a maximum joint torque constraint, a maximum position error constraint and an oscillation constraint. A constraint handling approach is suggested for the optimization algorithm in order to adapt it to the problem in hand. Finally, the efficiency of the proposed method is verified through a practical experiment on a real robot.

Recent research has revealed that image-based methods can enhance accuracy and safety in laser microsurgery. In this study, non-rigid tracking using surgical stereo imaging and its application to laser ablation is discussed. A recently developed motion estimation framework based on piecewise affine deformation modeling is extended by a mesh refinement step and considering texture information. This compensates for tracking inaccuracies potentially caused by inconsistent feature matches or drift. To facilitate online application of the method, computational load is reduced by concurrent processing and affine-invariant fusion of tracking and refinement results. The residual latency-dependent tracking error is further minimized by Kalman filter-based upsampling, considering a motion model in disparity space. Accuracy is assessed in laparoscopic, beating heart, and laryngeal sequences with challenging conditions, such as partial occlusions and significant deformation. Performance is compared with that of state-of-the-art methods. In addition, the online capability of the method is evaluated by tracking two motion patterns performed by a high-precision parallel-kinematic platform. Related experiments are discussed for tissue substitute and porcine soft tissue in order to compare performances in an ideal scenario and in a setup mimicking clinical conditions. Regarding the soft tissue trial, the tracking error can be significantly reduced from 0.72 mm to below 0.05 mm with mesh refinement. To demonstrate online laser path adaptation during ablation, the non-rigid tracking framework is integrated into a setup consisting of a surgical Er:YAG laser, a three-axis scanning unit, and a low-noise stereo camera. Regardless of the error source, such as laser-to-camera registration, camera calibration, image-based tracking, and scanning latency, the ablation root mean square error is kept below 0.21 mm when the sample moves according to the aforementioned patterns. Final experiments regarding motion-compensated laser ablation of structurally deforming tissue highlight the potential of the method for vision-guided laser surgery.

A typical instrument for different tasks in minimally invasive surgery is a flexible endoscope. To overcome the problem of restricted motion of today's systems, a hyper-redundant active shaft concept is developed. It features good resistance with respect to manipulation forces through its unique binary, electromagnetic actuation concept. With an active control of each element of the hyper-redundant endoscope shaft and in combination with automated feed motion, it is controlled in a “follow-the-leader” fashion. In general, this approach is characterized by very good path following capabilities. However, due to its binary actuation concept, joint angles cannot be interpolated continuously. Therefore, an adaption to binary actuation with optimized switching sequences to achieve an appropriate path following performance despite of the restricted motion is proposed.

Bone-attached robots and so-called microstereo-tactic frames are attracting increasing interest in the field of robot-assisted surgery due to the promising targeting accuracy they provide. The authors propose a passive Stewart-Gough platform which is attached to a patient's head via bone anchors. It serves as an instrument guidance in straight line incisions, such as minimally invasive cochlear implantation. In this contribution a modified version of the mechanism's kinematics is proposed, which reduces the number of required bone anchors from six to three. Furthermore, a novel statistical approach to optimize the design variables of the moving platform for accuracy is presented. It is characterized by the ability to take the probability distributions of all relevant error sources as well as the given task redundancy and reconfigurability of the mechanism into account. Based on identified ranges of trajectories and possible bone anchor locations, the optimization problem is solved for a representative number of 1,000 ‘virtual patients’. The optimum mechanism design is obtained by analyzing the resulting distributions of design variables. Monte Carlo simulation is used to compare its targeting errors to those of a previous prototype. Results reveal that the targeting error is significantly reduced in comparison to an initial prototype thanks to the proposed optimization strategy.

This paper presents a new university course combining theoretical lectures with a robot competition. The main intention is to offer a practical course including hands-on experiences being close to current research topics in the field of mobile robotics. For this purpose, a commercial mobile robot is equipped with state of the art sensors, allowing autonomous execution of manipulation tasks. The course consists of three thematical sections. At the beginning of each, lectures provide the theoretical background. On this basis, the participants are addressed to develop algorithms and to solve specific tasks, delivered in homework packages, self-reliantly or in a team. Finally, the developed software components need to be merged to solve a predefined scenario, e.g. autonomous part handling. At the end of a thematic section, students demonstrate their developed solutions within a challenge and explain their approaches in a presentation. Starting with teleoperation and object recognition, the RobotChallenge ends up with navigation in unknown terrain. Besides others, the participants acquire soft-skills, such as project and team management. Being carried out for the first time in winter term 2011/2012, the RobotChallenge successfully promotes profound understanding of mobile robotics that is applied during practical experiences. It turns out, that aspiring to win competions lead to a high motivation of the students w.r.t. development of appropriate solutions.

In this paper experimental results are presented to compare the performance of kinematically redundant parallel robots with respect to their non-redundant counterparts. The main purpose is to validate existing simulated, i.e. claimed, findings demonstrating the advantages of kinematic redundancy in terms of singularity avoidance and, therefore, accuracy and precision. Exemplarily, the kinematically redundant prototype of the Institute at Mechatronic Systems is introduced. It is based on the well known planar 3RRR mechanism. In order to achieve kinematic redundancy, a prismatic actuator is added to the structure allowing one base joint to move linearly. As a result, the mechanism is able to reconfigure, i.e. optimize, its geometry according to different performance criteria and motion strategies. While performing a geometrical reconfiguration and following several desired (optimized) trajectories the pose of the end-effector is determined using an external measurement device. Hence, in addition to the encoder data of the actuators the performance can be analyzed without using any (uncertain) kinematic models. This allows for a meaningful comparative evaluation of the performance of kinematically redundant mechanisms.

The collaborative research center SFB453 (www.sfb453.de) aims to realize high-fidelity telepresence and teleaction systems. Telepresence and teleaction systems extend the human workspace to remote locations in order to overcome barriers like distance, scaling, danger or the human skin. Using a human-system interface the human operator controls a remotely located teleoperator. Multi-modal feedback in form of visual, auditory, and haptic data is used to increase the feeling of telepresence. Different application areas including minimally invasive surgery, on-orbit servicing, microassembly as well as tele-manufacturing and tele-maintenance are targeted.

In this paper five different optimization strategies for kinematically redundant mechanisms, i.e. mechanisms having additional actuator(s) in at least one kinematic chain, are presented. They are based on two main approaches, a discrete optimization and a classical continuous optimization. Exemplarily, a planar, kinematically redundant 3RRR-based mechanism is introduced. The position of its redundant actuator, i.e. the robot geometry, is optimized according to an optimization criterion that is denoted as the gain of the maximal homogenized pose error. Several analysis examples demonstrate the effectiveness of kinematic redundancy with respect to the introduced optimization procedures. It is shown that in comparison to discrete approaches, classical continuousbased optimization strategies do not necessarily lead to more appropriate results in terms of performance improvement.

This video presents the in-house developed DLR MiroSurge robotic system for surgery. As shown, the system is suitable for both minimally invasive and open surgery. Essential part of the system is the MIRO robot: The soft robotics feature enables intuitive interaction with the robot.

This paper presents a novel system for accurate placement of pedicle screws. The system consists of a new light-weight (<10 kg), kinematically redundant, and fully torque controlled robot. Additionally, the pose of the robot tool-center point is tracked by an optical navigation system, serving as an external reference source. Therefore, it is possible to measure and to compensate deviations between the intraoperative and the preoperatively planned pose. The robotic arm itself is impedance controlled. This allows for a new intuitive man-machine-interface as the joint units are equipped with torque sensors: the robot can be moved just by pulling/pushing its structure. The surgeon has full control of the robot at every step of the intervention. The hand-eye-coordination problems known from manual pedicle screw placement can be omitted

In this paper, a generic approach to optimize the design of an actuated carrier for the DLR multi-arm surgical system is presented. The carrier is attached to the ceiling of the operating room and provides additional degrees of freedom to the surgical robots with the purpose of automatic, optimal positioning of their bases as well as guaranteeing high stiffness. Standard workspaces of minimally invasive as well as open surgical procedures are considered and optimization criteria are derived. The minimum necessary degrees of freedom of the carrier are obtained as well as the optimal segment dimensions by use of an optimization with genetic algorithms

Minimally invasive surgery in combination with ultrasound (US) imaging imposes high demands on the sur geon’s hand-eye-coordination capabilities. A possible solution to reduce these requirements is minimally invasive robotic surgery in which the instrument is guided by visual servoing towards the goal defined by the surgeon in the US image. This approach requires robust tracking of the instrument in the US image sequences which is known to be difficult due to poor image quality. This paper presents computer vision algorithms and results of visual servoing experiments. Adaptive thresholding according to Otsu’s method allows to cope with large intensity variations of the instrument echo. Subsequently applied morphological operations suppress noise and echo artefacts. A fast labelling algorithm based on run length coding allows for realtime labelling of the regions. A heuristic exploiting region size and region velocity helps to overcome ambiguities. The overall computation time is less than 10 ms per frame on a standard PC. The tracking algorithm requires no information about texture and shape which are known to be very unreliable in US image sequences. Experimental results for different instrument materials (polyvinyl chloride, polyurethane, nylon, and plexiglas) are given, illustrating the performance of the proposed approach: when chosing the appropriate material the reconstructed trajectories are smooth and only few outliers occur. As a consequence, the visual servoing loop showed to be robust and stable.

Visual servoing is a possible solution to assist the surgeon in performing tasks under ultrasound (US) imaging. To this aim, a system was developed that allows the surgeon to select a desired instrument location on a US image. Then a robot is programmed to automatically move the instrument towards the selected location. This approach requires robust tracking of the instrument in the US image, together with modeling of the overall system and implementation of a visual servoing loop. This paper presents geometrical and kinematic models of the system, as well as the control loop design, which is validated through both numerical simulations, and results of in vitro experiments.

Minimally invasive surgery (MIS) challenges the surgeon's skills due to his separation from the operation area, which can be reached with long instruments only. Therefore, the surgeon loses access to the manipulation forces inside the patient. This reduces his dexterity when performing the operation. A new compact and lightweight robot for MIS is presented which allows for the measurement of manipulation forces. The main advantage of this concept is that no miniaturized force sensor has to be integrated into surgical instruments and inserted into the patient. Rather, outside the patient a standard sensor is attached to a modified trocar, which allows for the undisturbed measurement of manipulation forces. This approach reduces costs and sterilizability demands. Results of first force control experiments are presented to show the feasibility of the concepts.

Telepresence in minimally invasive surgery (MIS) is a promising application for robotics because the robot enhances the manipulation and sensation capabilities of the surgeon. Many of these surgical robots are equipped with passive joints that guarantee that no forces are exerted to the entry point. Due to these passive joints, which are dependent on the entry point's position, new algorithms for cartesian control have to be developed. After introducing the kinematics of the robot, used in the DLR minimally invasive robot surgery scenario, we show how the entry point (trocar) can be estimated, followed by the calculation of the inverse kinematics. To allow cartesian velocity control of the robot system we build an observer that is used to close the velocity control loop. Experiments show the performance of the chosen algorithms.