Christopher Schindlbeck

Bio: Christopher Schindlbeck is an academic researcher from Leibniz University of Hanover. The author has contributed to research in topics: Wavefront & Compensation (engineering). The author has an hindex of 3, co-authored 10 publications receiving 133 citations.

Unified passivity-based Cartesian force/impedance control for rigid and flexible joint robots via task-energy tanks

Abstract: In this paper we propose a novel hybrid Cartesian force/impedance controller that is equipped with energy tanks to preserve passivity. Our approach overcomes the problems of (hybrid) force control, impedance control, and set-point based indirect force control. It allows accurate force tracking, full compliant impedance behavior, and safe contact resemblance simultaneously by introducing a controller shaping function that robustly handles unexpected contact loss and avoids chattering behavior that switching based approaches suffer from. Furthermore, we propose a constructive way of initiating the energy tanks via the concept of task energy. This represents an estimate of the energy consumption of a given force control task prior to execution. The controller can be applied to both rigid body and flexible joint dynamics. To show the validity of our approach, several simulations and experiments with the KUKA/DLR LWR-III are carried out.

Predictor-corrector framework for the sequential assembly of optical systems based on wavefront sensing.

Abstract: Alignment of optical components is crucial for the assembly of optical systems to ensure their full functionality. In this paper we present a novel predictor-corrector framework for the sequential assembly of serial optical systems. Therein, we use a hybrid optical simulation model that comprises virtual and identified component positions. The hybrid model is constantly adapted throughout the assembly process with the help of nonlinear identification techniques and wavefront measurements. This enables prediction of the future wavefront at the detector plane and therefore allows for taking corrective measures accordingly during the assembly process if a user-defined tolerance on the wavefront error is violated. We present a novel notation for the so-called hybrid model and outline the work flow of the presented predictor-corrector framework. A beam expander is assembled as demonstrator for experimental verification of the framework. The optical setup consists of a laser, two bi-convex spherical lenses each mounted to a five degree-of-freedom stage to misalign and correct components, and a Shack-Hartmann sensor for wavefront measurements.

Process-Integrated State Estimation of Optical Systems With Macro–Micro Manipulators Based on Wavefront Filtering

Abstract: To date, the assembly of optical systems is still not fully automated. Automated assembly can be facilitated by having an optical simulation at hand, which, in turn, requires knowledge about the current state of the optical system. Due to the strict demands on the positioning tolerances, the uncertainties of the positioning system play an important role and lead to non-negligible deviations from the nominal poses. Therefore, the actual poses of the optical components need to be estimated in order to correct misaligned components. Furthermore, it is beneficial to develop methods that utilize the dedicated primary sensor and to avoid additional external sensors. In this letter, we employ a macro-micro manipulator for moving optical components and utilize filtering methods for realizing an in-process state estimation. For this, the uncertainty of the positioning system, as well as sensor noise, need to be identified, which lay the groundwork for methods such as the extended Kalman filter, iterated extended Kalman filter, unscented Kalman filter, or particle filter. In this letter, we compare these methods in simulation with current nonlinear approaches from the literature with respect to the estimation error. Experimental verification is carried out by a macro-micro manipulator comprised of a Cartesian piezo-driven 3-DOF positioning system attached to a 6-DOF industrial robot. With the proposed filtering approach and macro-micro manipulator, the pose of a bi-convex lens is estimated via a wavefront sensor.

Increasing milling precision for macro-micro-manipulators with disturbance rejection control via visual feedback

Abstract: Industrial robotic manipulators can be augmented by a micro-positioning unit in order to increase their precision resulting in a so called macro-micro-manipulator. The micro-positioning unit is typically driven by piezoelectric actuators due to their beneficial properties. However, contact forces during interaction tasks induce deviations from the nominal path that can not be observed due to compliance, lack of sensors in the micro-positioning unit, or unknown interaction dynamics in constrained environments. In this paper, a model-free and decoupled disturbance rejection controller via visual feedback for macro-micro-manipulators is presented. An external stereoscopic vision system is employed to detect deviations from the nominal trajectory. We outline an image segmentation algorithm and the utilized camera calibration technique is based on two-view geometry. Afterwards, the disturbance rejection controller including visual feedback for the macro-micro-manipulator is described. In order to demonstrate the 3D capability of the proposed approach, a microscopic staircase is milled. For comparison, the milling experiment is executed without and with active disturbance rejection by the micro-positioning unit in order to show the increase in precision during the milling task. Results show that the arithmetic mean roughness falls below 2 μm for the step profiles and the maximum surface height deviation is less than ±10 μm for each steps.

Recursive Online Compensation of Piezoelectric Nonlinearities via a Modified Prandtl-Ishlinskii Approach

Abstract: Piezoelectric actuators are often employed in micro- and nanopositioning devices due to their extremely fine positioning resolution but exhibit strong nonlinear effects (predominantly hysteresis and creep) which pose a considerable challenge for the control community. For online compensation of these effects, the modified Prandtl-Ishlinskii model is particularly suitable since its inverse can be found analytically by a parameter transformation. However, this model-based approach has not yet made its way to devices that target positioning tasks. Therein, trajectories typically contain segments with varying final times, small ranges of motion, or stationary states such that the hysteresis and creep effects are not optimally excited and frequency-dependence is induced which ultimately leads to a deteriorated compensation performance. This paper proposes an online approach to overcome the problem by taking into account prior hysteresis/creep information in a recursive manner based on databases for better tracking performance and a more robust compensation of the aforementioned nonlinearities. In order to show the efficacy of the proposed approach, experimental results are provided by using trajectories with varying final times, stationary states and alternating small/large ranges of motion on a micro-positioning unit driven by piezoelectric actuators.

Admittance-Based Controller Design for Physical Human–Robot Interaction in the Constrained Task Space

Abstract: In this article, an admittance-based controller for physical human–robot interaction (pHRI) is presented to perform the coordinated operation in the constrained task space. An admittance model and a soft saturation function are employed to generate a differentiable reference trajectory to ensure that the end-effector motion of the manipulator complies with the human operation and avoids collision with surroundings. Then, an adaptive neural network (NN) controller involving integral barrier Lyapunov function (IBLF) is designed to deal with tracking issues. Meanwhile, the controller can guarantee the end-effector of the manipulator limited in the constrained task space. A learning method based on the radial basis function NN (RBFNN) is involved in controller design to compensate for the dynamic uncertainties and improve tracking performance. The IBLF method is provided to prevent violations of the constrained task space. We prove that all states of the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB) by utilizing the Lyapunov stability principles. At last, the effectiveness of the proposed algorithm is verified on a Baxter robot experiment platform. Note to Practitioners —This work is motivated by the neglect of safety in existing controller design in physical human–robot interaction (pHRI), which exists in industry and services, such as assembly and medical care. It is considerably required in the controller design for rigorously handling constraints. Therefore, in this article, we propose a novel admittance-based human–robot interaction controller. The developed controller has the following functionalities: 1) ensuring reference trajectory remaining in the constrained task space: a differentiable reference trajectory is shaped by the desired admittance model and a soft saturation function; 2) solving uncertainties of robotic dynamics: a learning approach based on radial basis function neural network (RBFNN) is involved in controller design; and 3) ensuring the end-effector of the manipulator remaining in the constrained task space: different from other barrier Lyapunov function (BLF), integral BLF (IBLF) is proposed to constrain system output directly rather than tracking error, which may be more convenient for controller designers. The controller can be potentially applied in many areas. First, it can be used in the rehabilitation robot to avoid injuring the patient by limiting the motion. Second, it can ensure the end-effector of the industrial manipulator in a prescribed task region. In some industrial tasks, dangerous or damageable tools are mounted on the end-effector, and it will hurt humans and bring damage to the robot when the end-effector is out of the prescribed task region. Third, it may bring a new idea to the designed controller for avoiding collisions in pHRI when collisions occur in the prescribed trajectory of end-effector.

A Human–Robot Co-Manipulation Approach Based on Human Sensorimotor Information

Abstract: This paper aims to improve the interaction and coordination between the human and the robot in cooperative execution of complex, powerful, and dynamic tasks. We propose a novel approach that integrates online information about the human motor function and manipulability properties into the hybrid controller of the assistive robot. Through this human-in-the-loop framework, the robot can adapt to the human motor behavior and provide the appropriate assistive response in different phases of the cooperative task. We experimentally evaluate the proposed approach in two human–robot co-manipulation tasks that require specific complementary behavior from the two agents. Results suggest that the proposed technique, which relies on a minimum degree of task-level pre-programming, can achieve an enhanced physical human–robot interaction performance and deliver appropriate level of assistance to the human operator.

Robotic assembly solution by human-in-the-loop teaching method based on real-time stiffness modulation

Abstract: We propose a novel human-in-the-loop approach for teaching robots how to solve assembly tasks in unpredictable and unstructured environments. In the proposed method the human sensorimotor system is integrated into the robot control loop though a teleoperation setup. The approach combines a 3-DoF end-effector force feedback with an interface for modulation of the robot end-effector stiffness. When operating in unpredictable and unstructured environments, modulation of limb impedance is essential in terms of successful task execution, stability and safety. We developed a novel hand-held stiffness control interface that is controlled by the motion of the human finger. A teaching approach was then used to achieve autonomous robot operation. In the experiments, we analysed and solved two part-assembly tasks: sliding a bolt fitting inside a groove and driving a self-tapping screw into a material of unknown properties. We experimentally compared the proposed method to complementary robot learning methods and analysed the potential benefits of direct stiffness modulation in the force-feedback teleoperation.

A Tutorial Survey and Comparison of Impedance Control on Robotic Manipulation

Abstract: There have been significant interests and efforts in the field of impedance control on robotic manipulation over last decades. Impedance control aims to achieve the desired mechanical interaction between the robotic equipment and its environment. This paper gives the overview and comparison of basic concepts and principles, implementation strategies, crucial techniques, and practical applications concerning the impedance control of robotic manipulation. This work attempts to serve as a tutorial to people outside the field and to promote discussion of a unified vision of impedance control within the field of robotic manipulation. The goal is to help readers quickly get into the problems of their interests related to impedance control of robotic manipulation and to provide guidance and insights in finding appropriate strategies and solutions.