Jörn Malzahn

WALK‐MAN: A High‐Performance Humanoid Platform for Realistic Environments

In this work, we present WALK-MAN, a humanoid platform that has been developed to operate in realistic unstructured environment, and demonstrate new skills including powerful manipulation, robust balanced locomotion, high-strength capabilities, and physical sturdiness. To enable these capabilities, WALK-MAN design and actuation are based on the most recent advancements of series elastic actuator drives with unique performance features that differentiate the robot from previous state-of-the-art compliant actuated robots. Physical interaction performance is benefited by both active and passive adaptation, thanks to WALK-MAN actuation that combines customized high-performance modules with tuned torque/velocity curves and transmission elasticity for high-speed adaptation response and motion reactions to disturbances. WALK-MAN design also includes innovative design optimization features that consider the selection of kinematic structure and the placement of the actuators with the body structure to maximize the robot performance. Physical robustness is ensured with the integration of elastic transmission, proprioceptive sensing, and control. The WALK-MAN hardware was designed and built in 11 months, and the prototype of the robot was ready four months before DARPA Robotics Challenge (DRC) Finals. The motion generation of WALK-MAN is based on the unified motion-generation framework of whole-body locomotion and manipulation (termed loco-manipulation). WALK-MAN is able to execute simple loco-manipulation behaviors synthesized by combining different primitives defining the behavior of the center of gravity, the motion of the hands, legs, and head, the body attitude and posture, and the constrained body parts such as joint limits and contacts. The motion-generation framework including the specific motion modules and software architecture is discussed in detail. A rich perception system allows the robot to perceive and generate 3D representations of the environment as well as detect contacts and sense physical interaction force and moments. The operator station that pilots use to control the robot provides a rich pilot interface with different control modes and a number of teleoperated or semiautonomous command features. The capability of the robot and the performance of the individual motion control and perception modules were validated during the DRC in which the robot was able to demonstrate exceptional physical resilience and execute some of the tasks during the competition.

A modular compliant actuator for emerging high performance and fall-resilient humanoids

The application of humanoids in real world environments necessarily requires robots that can demonstrate physical resilience against strong physical interactions with the environment and impacts, that may occur during falling incidents, that are unavoidable. This paper introduces a modular high performance actuation unit designed to be robust against impacts and strong physical perturbations. The protection against impacts is achieved with the use of elastic transmission combined with soft cover elements on the link side. The paper introduce the details of the actuator design and implementation and discuss the effects of the soft cover and series elastic transmission on the reduction of the impact forces which reach the reduction drive of the actuator during impacts. The model of prototype joint, including the actuator unit, its elastic transmission and the driving link soft cover, is introduced and simulations were performed to study the effect of the elastic properties of the transmission and the soft cover on the reduction of the impact forces transmitted to the reduction drive. The results from the simulations are confirmed by experimental measurements on the real system under induced experimental impact trials, demonstrating the significant effect of the soft cover in the further reduction of impact forces. The performance of the proposed actuator unit in terms of physical robustness makes it ideal for the development of emerging humanoids robots that will be capable of surviving falls and recovers from them.

Input shaping and strain gauge feedback vibration control of an elastic robotic arm

Novel robotic applications, such as service robots, support the interaction between a human and a robot within the same workspace. These types of cooperative tasks require a safe robot operation in order to avoid physical harm to the human operator. In case of contact between the robot and human inherent safety is accomplished by means of lightweight flexible structures of reduced mass and stiffness that absorb contact forces. The flexible structure induces vibrations of the arm during motion, which complicates the precise kinematic control of the end effector pose. This contribution proposes input shaping in conjunction with strain gauge feedback control to suppress and compensate arm vibrations induced by robot motion. The control schemes are experimentally verified and the results demonstrate an efficient damping of link vibrations.

Dynamics identification of a damped multi elastic link robot arm under gravity.

The infinite dimensionality, varying, uncertainties or even unknown boundary conditions render the derivation and - in particular - the identification of accurate dynamics models for elastic link robots tedious and error prone. This contribution circumvents these challenges by the prior application of a model-free inner loop oscillation damping controller before modelling the robots dynamics. Then, the damped dynamics of a multi elastic link robot arm under gravity can be modelled with high accuracy. An analytical and a data-driven model for the damped dynamics are proposed and quantitatively compared. Both models can explain motor currents as well as link strain measurements in real-time. The paper includes an experimental model validation with different payloads in the entire workspace of the robot.

Vibration control of a multi-flexible-link robot arm under gravity

This paper proposes a novel independent joint control concept on a 3 DOF flexible link robot subject to deflections caused by gravity. The scheme dampens induced link oscillations in the presence of configuration dependant plant frequency and damping variations by integrating link strain feedback and impulse based input shaping. The approach is robust and the control does not depend on a dynamic model at runtime. The damping efficiency is evaluated experimentally in terms of strain measurement as well as end-effector position across the entire workspace.

Vibration control of a multi-link flexible robot arm with Fiber-Bragg-Grating sensors

Flexible, lightweight manipulators offer some advantages in contrast to rigid arms, such as compact and lighter drives, energy efficiency, reduced masses and costs. This paper presents a novel approach for vibration damping of a multi-link flexible arm. The strain of the elastic arms is measured with Fiber-Bragg-Grating (FBG) sensors and provides the feedback signal to dampen their flexural dynamics. A dynamic model of a three link arm is derived that accounts for the rigid and flexural dynamics including gravity. The arm vibrations are damped by nonlinear strain feedback. The controller is general and robust and its design does not require a model of the flexural dynamics. In the context of closed loop vibration control FBG sensors offer a better signal to noise ratio compared to strain gauges, which allows a higher static gain in the feedback loop with more efficient dissipation of vibrational energy. The feasibility and effectiveness of the proposed vibration control scheme in conjunction with FBG sensors is verified and analyzed in simulations and confirmed in experiments with a flexible three link robot arm.

Link elasticity exploited for payload estimation and force control

Link elasticity is commonly understood to be a detrimental side-effect of imperfect mechanical designs of robotic arms and comparable machinery. In contrast to this notion, this paper demonstrates a novel approach to exploit intrinsic robot link compliance in order to estimate a priori unknown payload masses, measure and also control end effector forces. In this way, the intrinsic link elasticity can be seen as an enabler for new sensing and control capabilities instead of a purely detrimental effect.

Collision Detection and Reaction for a Multi-Elastic-Link Robot Arm

Abstract In contrast to the common understanding of robot link elasticity as a detrimental effect the paper presents a novel approach to beneficially exploit the intrinsic link compliance for the detection and reaction to unpredicted collisions between the robot and its environment. The paper employs an inner loop controller to rapidly attenuate structural oscillations. Next, a linear relationship between the actuator joint torques as well as the damped link surface strains is derived to accurately model the residual dynamics. The model is identified for an experimental multi-elastic link robot arm under the influence of gravity. Experimental results are provided using a generalized momentum based technique for the detection and reaction to collisions with fragile objects placed on a force sensor as well as interactions with a human.

Data based kinematic model of a multi-flexible-link robot arm for varying payloads

Reducing weight and inertias of conventional robot arms with an elastic structure allows safer interactive cooperation between humans and robots. While the end effector pose of a rigid robot is determined by the forward kinematic chain, the pose of elastic arms results from a superposition of the rigid kinematics and the pose dependent deflection caused by gravity. This property complicates the computation of forward and inverse kinematics in particular in case of dynamic loads. This paper presents a machine learning approach to extract various nonlinear regression models of the forward and inverse kinematics of a three degrees of freedom (DOF) flexible-link robot arm with dynamic loads from experimental data. The forward model predicts the target pose, given the joint angles and the strain signals while the inverse kinematic model predicts the joint angles required to assume a target pose. The transformation of the original features onto suitable nonlinear features substantially improves the generalisation ability of the both forward and inverse kinematic model. The closed loop inverse kinematic controller archieves a pose accuracy of 3 mm and the results show that the learned model can solve the inverse kinematics problem of flexible robot arms with sufficient accuracy even with unknown payloads.

On the Sensor Design of Torque Controlled Actuators: A Comparison Study of Strain Gauge and Encoder-Based Principles

Joint torque sensing represents one of the foundations and vital components of modern robotic systems that target to match closely the physical interaction performance of biological systems through the realization of torque controlled actuators. However, despite decades of studies on the development of different torque sensors, the design of accurate and reliable torque sensors still remains challenging for the majority of the robotics community preventing the use of the technology. This letter proposes and evaluates two joint torque sensing elements based on strain gauge and deflection-encoder principles. The two designs are elaborated and their performance from different perspectives and practical factors are evaluated including resolution, nonaxial moments load crosstalk, torque ripple rejection, bandwidth, noise/residual offset level, and thermal/time dependent signal drift. The letter reveals the practical details and the pros and cons of each sensor principle providing valuable contributions into the field toward the realization of higher fidelity joint torque sensing performance.