River Rapids Conference Proceedings of the Society for Experimental Mechanics Series Special Topics in Structural Dynamics & Experimental Techniques, Volume 5 Nikolaos Dervilis Proceedings of the 37th IMAC, A Conference and Exposition on Structural Dynamics 2019 River Publishers
Conference Proceedings of the Society for Experimental Mechanics Series Series Editor Kristin B. Zimmerman, Ph.D. Society for Experimental Mechanics, Inc., Bethel, CT, USA
River Publishers Special Topics in Structural Dynamics & Experimental Techniques, Volume 5 Proceedings of the 37th IMAC, A Conference and Exposition on Structural Dynamics 2019 Nikolaos Dervilis Editor
Published, sold and distributed by: River Publishers Broagervej 10 9260 Gistrup Denmark www.riverpublishers.com ISBN 978-87-7004-986-3 (eBook) Conference Proceedings of the Society for Experimental Mechanics An imprint of River Publishers © The Society for Experimental Mechanics, Inc. 2020 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, or reproduction in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Preface Special Topics in Structural Dynamics & Experimental Techniques represents one of eight volumes of technical papers presented at the 37th IMAC, A Conference and Exposition on Structural Dynamics, organized by the Society for Experimental Mechanics and held in Orlando, Florida, January 28–31, 2019. The full proceedings also include volumes on Nonlinear Structures & Systems; Dynamics of Civil Structures; Model Validation and Uncertainty Quantification; Dynamics of Coupled Structures; Rotating Machinery, Optical Methods & Scanning LDV Methods; Sensors and Instrumentation, Aircraft/Aerospace, Energy Harvesting & Dynamic Environments Testing; and Topics in Modal Analysis & Testing. Each collection presents early findings from experimental and computational investigations on an important area within structural dynamics. Special Topics in Structural Dynamics represents papers on enabling technologies for general dynamics and both modal analysis measurements, system identification, and damage detection. The organizers would like to thank the authors, presenters, session organizers, and session chairs for their participation in this track. University of Sheffield, Sheffield, UK Nikolaos Dervilis v
Contents 1 A Step Towards Testing of Foot Prostheses Using Real-Time Substructuring (RTS)............................ 1 Christina Insam, Andreas Bartl, and Daniel J. Rixen 2 Augmented Reality for Interactive Robot Control ................................................................... 11 Levi Manring, John Pederson, Dillon Potts, Beth Boardman, David Mascarenas, Troy Harden, and Alessandro Cattaneo 3 Optimizing Logarithmic Decrement Damping Estimation via Uncertainty Analysis............................ 19 Jared A. Little and Brian P. Mann 4 A Simplified Current Blocking Piezoelectric Shunt Circuit for Multimodal Vibration Mitigation ............ 23 Ghislain Raze, Andy Jadoul, Valery Broun, and Gaetan Kerschen 5 Using the SEREP Idea for the Projection of Modal Coordinates to Different Finite Element Meshes ........ 27 Wolfgang Witteveen, Pöchacker Stefan, and Florian Pichler 6 Identification System for Structural Health Monitoring in Buildings ............................................. 31 Jesús Morales-Valdez, Luis Alvarez-Icaza, José Alberto Escobar, and Héctor Guerrero 7 Experimental and Numerical Study of the Second Order Moment of the First Passage Time of a Steel Strip Subjected to Forced and Parametric Excitations.............................................................. 39 E. Delhez, H. Vanvinckenroye, V. Denoël, and J.-C. Golinval 8 Three-Dimensional Mechanical Metamaterial for Vibration Suppression........................................ 43 Brittany C. Essink and Daniel J. Inman 9 Model Reduction of Self-Repeating Structures with Applications to Metamaterial Modeling ................. 49 Ryan Romeo and Ryan Schultz 10 Imager-Based Techniques for Analyzing Metallic Melt Pools for Additive Manufacturing .................... 63 Cedric Hayes, Caleb Schelle, Greg Taylor, Bridget Martinez, Garrett Kenyon, Thomas Lienert, Yongchao Yang, and David Mascareñas 11 Full-Field Mode Shape Analysis, Alignment and Averaging Across Measurements............................. 71 Wesley Scott, Matthew Adams, Yongchao Yang, and David Mascareñas 12 Investigating Engineering Data by Probabilistic Measures......................................................... 77 L. A. Bull, K. Worden, T. J. Rogers, E. J. Cross, and N. Dervilis 13 Multi-Input Multi-Output Swept Sine Control: A Steepest Descent Solution for a Challenging Problem.... 83 Umberto Musella, Bart Peeters, Francesco Marulo, and Patrick Guillaume 14 Study on Developing Micro-Scale Artificial Hair Cells.............................................................. 95 Sheyda Davaria, V. V. N. Sriram Malladi, Lukas Avilovas, Phillip Dobson, Andrea Cammarano, and Pablo A. Tarazaga 15 Dynamic Characteristic Identification ................................................................................ 101 Clay Jordan and Tommy Hazelwood vii
viii Contents 16 One Year Monitoring of a Wind Turbine Foundations.............................................................. 107 Marta Berardengo, Stefano Manzoni, Marcello Vanali, and Francescantonio Lucà 17 On the Application of Domain Adaptation in SHM................................................................. 111 X. Liu and K. Worden
Chapter 1 A Step Towards Testing of Foot Prostheses Using Real-Time Substructuring (RTS) Christina Insam, Andreas Bartl, and Daniel J. Rixen Abstract Despite extensive research in prostheses development, amputees still have to cope with severe limits. Tasks, such as climbing stairs and running or walking on soft ground are demanding and represent obstacles in everyday life. Design verification of new devices helps to accelerate the development. However, current test procedures do not include the dynamic interaction between a prosthesis and the human. Real-time Substructuring (RTS) enables investigation of the dynamic behavior of a system, here human and prosthesis, by splitting it into numerically simulated components and one physical component. As this test imitates real dynamic conditions, foot prostheses can be improved during the development stage. In this preliminary study, a one-dimensional mass-spring-mass system is investigated. The upper mass, representing the human being, is simulated numerically on the computer. It is coupled virtually to a prosthesis, represented here as a spring-mass system, which is mounted on a Stewart Platform. Both systems exchange displacement and force information. The upper mass tries to follow a periodic desired trajectory, which is influenced by the coupling. This paper describes the experimental setup and the effect of delay compensation. In addition, it is shown how the accuracy and stability of the RTS simulation depends on the problem description, i.e. how much the system is governed by the mechanical properties of the numerical part. Although we are specifically considering the application of RTS to prosthetics, the current research tackles generic problems that will also help to enhance other applications involving contact, e.g. the docking of satellites. Keywords Real-time substructuring · Testing of prosthetic feet · Stewart platform · Real-time hybrid testing with contact · Force compensation 1.1 Introduction Testing of foot prostheses is an expedient step in the development of foot prostheses. The aim of prostheses is, in general, to emulate the behavior of the missing body part. Despite great advances in the design of foot prostheses, they need to be improved further to enable amputees to resume a normal everyday life. Walking on uneven and soft ground is one of the challenging situations for patients because a healthy human foot can balance actively in this situation. Testing procedures during the early development stage help to accelerate prosthesis development and improvement [1]. Currently, there are different methods for testing foot prostheses: one approach is that the force and position data are taken from gait analysis laboratories and incorporated on test benches [2]. Another method for validating the newly developed design is via finite element modeling [1]. Moreover, prostheses are also evaluated via in-vivo testing. Patients are asked to wear the device and give feedback. The results from these tests are important, as they give a response on how well the amputees cope with the prosthesis in everyday life. However, even though this represents valuable feedback for prostheses developers, it is not without difficulties as it is not reproducible and unsafe for the amputees [1, 2]. The main advantage of robot-driven test benches is that they can also mimic potentially dangerous situations, such as tripping, in a safe and reproducible environment. Therefore, it would be advantageous to combine the advantages of all mentioned methods; a promising approach is the use of Real-Time Substructuring. Real-Time Substructuring (RTS) or Real-Time Hybrid Testing is a method for investigating the dynamic behavior of complex mechanical systems. The mechanical system is split into two parts (or substructures) and each of the parts is analyzed—one is analyzed in a numerical simulation, the other is investigated on the test bench which is driven by an actuator. Both simulations are coupled in real-time in order to assess the dynamic behavior of the whole system [3, 4]. C. Insam( ) · A. Bartl · D. J. Rixen Chair of Applied Mechanics, Faculty of Mechanical Engineering, Technical University of Munich, Garching, Germany e-mail: christina.insam@tum.de; andreas.bartl@tum.de; rixen@tum.de © Society for Experimental Mechanics, Inc. 2020 N. Dervilis (ed.), Special Topics in Structural Dynamics & Experimental Techniques, Volume 5, Conference Proceedings of the Society for Experimental Mechanics Series, https://doi.org/10.1007/978-3-030-12243-0_1 1
2 C. Insam et al. Fig. 1.1 Stewart Platform used for the RTS simulation This method was established and extensively used for earthquake engineering [5] but is nowadays also used in diverse fields, e.g. for the simulation of wind turbine blades [6] or spacecraft parachute deployment [7]. The applicability of the RTS method in biomechanics has also been proven by Herrmann et al. [8]. They tested the dislocation of artificial hip and knee joints under physiological conditions. The idea of testing foot prostheses using the RTS method was also proposed in [9]. The human is modeled in a multibody simulation which is solved numerically. The prosthesis is mounted on the test bench and force as well as displacement values are exchanged in real-time between the two systems. In [9] it is assumed that stability problems occur due to the highly nonlinear system properties and the discontinuity due to contact. Extensive research on stability problems due to delay and unknown dynamics of the transfer system (actuator and controller, force sensor) have been conducted over the last decades, e.g. by Horiuchi et al. [10], Darby et al. [11], Bonnet [12], and Bartl et al. [13]. Furthermore, Boge and Ma [14] addressed contact issues for their application satellite docking. In general, time delay of the transfer system brings negative damping into the simulated system. Therefore, they actively balance the energy input and output so that the total energy does not increase and the system remains stable. In this paper, we present the experimental setup for testing foot prostheses using RTS. The presented experimental setup is tested in an RTS simulation, where a mass-spring-mass system that can move in vertical direction serves as the mechanical system being tested. The effects of delay compensation using polynomial extrapolation [10] are investigated. Section 1.2 introduces the test bench and the coupling problem. The mechanical system as well as the required force compensation are presented and derived in Sect. 1.3. Section 1.4 presents the results of the experiments. It also highlights issues regarding contact problems for the stability and accuracy of RTS simulations. Finally, a brief summary and outlook are given in Sect. 1.5. 1.2 Experimental Setup A Stewart Platform, shown in Fig. 1.1, is used as an actuator for the RTS simulations. Stewart Platforms are six DOFs manipulators with parallel kinematics. Therefore, they offer the benefits of high stiffness and accuracy making them suitable for use, for example, in milling machines and driving simulators [15]. The Stewart Platform used in this investigation, is driven by six electric motors and is position-controlled by a decentralized cascade controller (PPI) [15]. For parallel robots, the inverse kinematics can be directly calculated using kinematic relationships in contrast to serial robots, where NewtonRaphson iterations or similar methods need to be used. Thus, the desired displacement command for the Tool Center Point
1 A Step Towards Testing of Foot Prostheses Using Real-Time Substructuring (RTS) 3 Transfer System Delay Compensation PPI cascade Force Sensor Physical Substructure DSP Numerical Substructure Fext Fig. 1.2 Control loop for RTS testing using a Stewart Platform [16] (center of upper platform) can be converted to a displacement command of each leg. Additionally, there is a velocity feedforward in the control loop of the position-control that improves the controlled behavior of the Stewart Platform. The sample time of the controller is set to 1 ms. The whole RTS simulation consists of a virtual and an experimental part as well as of a transfer system that imposes the desired displacement command. If the transfer system was ideal, i.e. it did not have any time delay or its own dynamics, the coupling of the virtual and experimental part would be perfect and the RTS simulation could perfectly imitate the dynamic behavior of the mechanical system [4]. The control loop for the RTS simulation is depicted in Fig. 1.2. The numerical model of the virtual part (blue), which is needed for the RTS simulation, is implemented in a Simulink Model. The realtime execution is done using a dSpace MicroLabBox as a Digital Signal Processor (DSP). The behavior of the numerical substructure is solved using a time integration scheme. The behavior depends on the current state as well as the loading and possible external forces Fext . The calculated displacement at the interface between the virtual and physical substructure is sent to the transfer system (orange). The transfer system includes the Stewart Platform as an actuator with its inner control loop (position control) and a potential outer control loop for delay compensation. The Stewart Platform executes the desired displacement originating from the numerical substructure. The physical substructure (green), which is connected to a force sensor and mounted on the actuator, is being moved by this displacement. The force at the interface of the physical substructure is measured by a six DOFs force-torque sensor and fed back to the DSP. The next numerical simulation is performed on the numerical substructure and, depending on the new loading, updates the displacement command for the Stewart Platform. 1.3 Problem Description In order to test foot prostheses with the experimental setup described in Sect. 1.2, the feasibility of the proposed idea must be validated. In addition, there are several research questions that must be solved to obtain a running and reliable RTS simulation. Therefore, the first simulations are done with the simple mechanical system described in Sect. 1.3.1. In Sect. 1.3.2, the force compensation of gravitational forces is derived. Finally, this section summarizes the delay compensation technique of [10] in Sect. 1.3.3. 1.3.1 Mechanical System The system that we investigate is a mass-spring-mass system as depicted in Fig. 1.3a. The system consists of two masses, the upper mass mV and the lower mass mE. Following the later application mV mE is set. The spring stiffness of the massless spring is set to be kE. Indices V and E imply the substructure to which the part belongs; mass mV is simulated
4 C. Insam et al. mV kE mE z h0 (a) (b) Fig. 1.3 Description and realization of the mechanical system. (a) Overall system for RTS simulation. (b) Experimental setup with mass mE and spring (stiffness kE) z(V) z(E) g mV · ¨z (V) mV · g Fint z(V) Fext Fint (a) g mV · ¨z (V) mV · g Fint Fm z Fext z(V) −2Δl stat Fint =Fm+mEg (b) Fig. 1.4 Comparison of the dynamic behavior between validation simulation and measurement in the experimental setup. The RTS simulation consists of the numerical substructure (blue), the physical substructure (green) and the transfer system (orange). (a) Forces and displacements in the reference system. (b) Forces and displacements in the experimental setup numerically (blue), whereas mass mE and the spring (stiffness kE) are tested on the test bench (green). Gravity acts in the negative z−direction and the height of mass mE at time zero, i.e. when the simulation begins, is h0. The behavior of the spring is linear over the range used in the experiments. The spring has an undeformed spring length of l0. Furthermore, it is assumed, that the real system does not include any damping. The test bench realization is shown in Fig. 1.3b. The experimental substructure is connected to the Tool Center Point of the Stewart Platform by a force sensor and can be brought into contact by the actuator. 1.3.2 Compensation of Gravity Effects The test setup is shown in Fig. 1.3b. The device under test that is mounted on its platform can be brought into contact with a ceiling but not with the ground. We could have inverted the Stewart Platform, however, the construction effort that would have been required prevented us from doing so. Consequently, the dynamic behavior of the system changes because the effect of gravity differs in the experimental setup and in the reference model, which must be included and compensated for in the RTS simulation. The considerations are visualized in Fig. 1.4, where Fig. 1.4a shows the reference model and Fig. 1.4b shows the experimental realization. The position z(V) stands for the position of mass mV and hence the position of the interface. Likewise, the position of mass mE is denoted by z (E). The numerical simulation consists of the mass mV (blue) which is under the following loads: the inertia force mV · ¨z (V), the gravitational force mV · g, the force at the interface Fint and a (time-dependent) external force Fext . As the mass mE and its corresponding accelerations ¨z (E) are in the same range as the noise of the force sensor, the inertia forces of mass mE are henceforth assumed to be negligible. In addition, it is assumed that
1 A Step Towards Testing of Foot Prostheses Using Real-Time Substructuring (RTS) 5 there is no delay in the transfer system (orange). In the real-time experiments, the force offset is set when the experimental part is already mounted on the test bench. Hence, the measured force is Fm =0Nif themass mE is not in contact. However, the force that acts at the interface of the reference system is Fint =mE·g. Since the interface forceFint acts on the numerical part and is thus required for a correct RTS simulation, the measured force must be corrected by Fint =Fm+mE · g. (1.1) In other words, the difference between the measured forces and the interface forces in the reference system is 2mEg, which results from the different orientation of the gravitational force. However, due to setting the offset of the force sensor when the mass mE is already mounted, we only have to compensate mEg according to Eq. (1.1). 1.3.3 Delay Compensation The delay and dynamics of the transfer system can lead to instability and inaccuracy of the RTS simulation, as they bring negative damping into the system. To compensate, there are a lot of different delay compensation techniques. For the experiments presented in Sect. 1.4, we used a polynomial extrapolation as published in [10]. The formulation is z (t) = 2 i=0 ai · z(t −i · τ) with a0 =3, a1 =−1 and a2 =3, if a polynomial of degree n = 2 is used and the time delay of the transfer system is τ. Here, the position value that is calculated by the numerical simulation at the current time t is z and the command that is sent to the actuator is z . z is an extrapolated value that depends on the value of z in the past. 1.4 Experiment This section presents the experiments performed and results obtained with the mechanical system presented in Sect. 1.3. Firstly, the load case is described with the corresponding simulation parameters in Sect. 1.4.1 and then the results are shown in Sect. 1.4.2. 1.4.1 Loading Condition and Simulation Parameters The aim of our research is to carry out a RTS simulation of a human walking with a prosthesis. A major question is how a human being stabilizes its body so that it does not fall, even if there are disturbances. One approach used in robotics for stabilizing bipeds is to use a planned trajectory and track this trajectory with a controller [17]. Using this approach, we prescribed a desired trajectory zd (and ˙zd) for the mass mV in the numerical substructure. A PD controller (parameters Kp and Kd) attempts to follow this trajectory by adding an external force, if the desired motion is not tracked: Fext =(mV +mE) · g +Kp · (zd −z (V)) +Kd · (˙zd − ˙z (V)). The first term corresponds to the static external force for holding the system in the air. Hence, the external force Fext that acts on the system as displayed in Fig. 1.4 is the force that is required to keep mass mV on the desired trajectory. The higher the values Kp andKd, the faster the controller that tries to hold mass mV on the desired trajectoryzd. Even small deviations from the trajectory are adjusted in a short period. If the parameters of the PD controller are reduced, the ability of the mass to follow its desired trajectory is reduced as well. Returning to the aim of testing a prosthesis using the RTS approach, this is similar to the case where the forces coming from a badly designed prosthesis are so large compared to the equilibrating forces, that the patient needs for example to tension a muscle much more than usual or tilt the hip. The dynamic behavior of the mechanical system is more relevant in this case and not dominated by the properties of the PD controller.
6 C. Insam et al. Table 1.1 Simulation parameters Parameter Value Parameter Value mV 9.62kg fd 0.25 Hz and 1 Hz mE 0.38kg zmax 0.005m kE 10,000N/m Kp 2· 10 6 kg/s 2 and104 kg/s 2 h0 0.01m Kd 500kg/s and 50kg/s τ 10ms The trajectory is chosen to be of sinusoidal shape zd(t) =h0 +l0 + lstat − h0 + zmax 2 + h0 + zmax 2 · cos(2πfdt) =z (V) 0 − h0 + zmax 2 + h0 + zmax 2 cos(2πfdt), starting at the initial position z(V)(t = 0) = z (V) 0 and using lstat = mE·g kE . Here, the initial height of mass mE is z(E)(t =0) =h0, the frequency of the cosine is fd and the maximum deflection of the spring is zmax (neglecting the static sag lstat due to the weight mE · g). The velocity at time zero is ˙z (V) d,0 = 0. In the numerical simulation, Heun’s method (ode2 in Simulink) was used as a time integration scheme and the sample time was taken to be 1 ms. The properties of the mechanical system and the parameters for the RTS simulation were chosen as follows: All results were compared to the validation simulation, where the reference mass-spring-mass system was simulated fully numerical by solving the differential equation with Simulink and an explicit Runge-Kutta time integration scheme (ode45). The contact was considered according to the penalization method, i.e. a spring acts as the mass mE penetrates the ground. The spring constant was defined as k =10 9 N/m. The solver returned the positions and velocities of both masses, i.e. z(V), z(E), ˙z(V) and respectively ˙z(E). Consequently, the interface force Fint can be calculated using the spring constant kE, Fint =kE · z (V) −z(E) −l0 . (1.2) 1.4.2 Results The first experiments included a sinusoidal trajectory with fd =0.25Hz and fd =1 Hz. Preliminary experiments revealed that the delay of the Stewart Platform is τ ≈ 10 ms for this kind and amplitude of excitation. Kp and Kd were chosen to be Kp = 2 · 10 6 kg/s 2 and Kd = 500kg/s, respectively, which means that the controller that keeps the upper mass on the desired trajectory zd has a strong influence. All other parameters were taken from Table 1.1. The results are shown in Fig. 1.5. Each figure includes the validation simulation of the reference system (dashed line) and the measured solution from our RTS simulation (solid line). Figure 1.5a and b shows the displacement of mass mV for an excitation of fd =0.25Hz and fd =1 Hz. Figure 1.5c and d shows the interface force that was measured and adjusted as given in Eq. (1.1) compared to the interface force from the validation simulation, see Eq. (1.2). Due to numerical oscillation and rounding errors, the interface force of the validation simulation oscillates when there is no contact between mE and the ground. The results in Fig. 1.5 show that the results from the RTS experiment closely correspond to the results from the validation simulation. As the parameters Kp andKd are quite high, the system dynamics are dominated by this controller that keeps mV on the desired trajectory. Nevertheless, these first results show that our implementation is correct and that the experimental setup (introduced in Sect. 1.2) can be used for RTS simulations. Similar experiments were also conducted using a delay compensation. Nevertheless, the results are not improved because the results without delay compensation already sufficiently emulate the system dynamics. So far, we have analyzed the feasibility of RTS experiments with the experimental setup. However, in the case of prostheses testing, we assume that the human will not control its desired trajectory as closely as the controller in the first experiments. Rather it will behave like a floating system. Hence, we have reduced the parameters to Kp =10 4 kg/s 2 and Kd = 50kg/s, respectively and performed similar tests. The prescribed trajectory had a frequency of fd = 0.25Hz. The
1 A Step Towards Testing of Foot Prostheses Using Real-Time Substructuring (RTS) 7 Christina Insam*1 3 1,2,3 1 0 2 4 6 8 10 7 7.5 8 ·10−2 Time in s Displacement in m z(V) validation z(V) RTS simulation (a) 0 1 2 3 4 5 7 7.5 8 ·10−2 Time in s Displacement in m z(V) validation z(V) RTS simulation (b) 0 2 4 6 8 10 −40 −20 0 Time in s Force inN Fint validation Fint RTS simulation (c) 0 1 2 3 4 5 −40 −20 0 Time in s Force inN Fint validation Fint RTS simulation (d) Fig. 1.5 Comparison of displacements and forces at the interface between validation simulation (dashed line) and real-time hybrid test (solid line). Experiments were conducted without delay compensation at 0.25 Hz and 1 Hz. (a) Displacement 0.25Hz. (b) Displacement 1 Hz. (c) Interface force 0.25Hz. (d) Interface force 1 Hz experiments were conducted without and with a delay compensation as introduced in Sect. 1.3.3 with a polynomial of degree n =2. The system dynamics are now much more influenced by contact problems and delay of the transfer system, as can be seen in Fig. 1.6. Figure 1.6a and b shows the displacement at the interface (z(V)) without and respectively with delay compensation. Figure 1.6c and d shows the corresponding forces at the interface Fint . Previously, the PD controller had higher values for Kp and Kd. This helped to stabilize the whole mechanical system, as the dynamics that are taken into account through the transfer system and the contact forces were suppressed. Now, the results show that coupling during contact makes the RTS simulation unstable. The displacement at the interface oscillates around the desired position with increasing amplitude. This effect is smaller, if we use the polynomial extrapolation as delay compensation (Fig. 1.6b and d). As the influence of the transfer system and the contact is increased (parameters Kp and Kd reduced), stability problems that can occur during RTS simulation with contact become clearly apparent. Furthermore, the accuracy of the dynamic analysis is reduced. 1.5 Conclusions We have presented the experimental setup for RTS tests using a Stewart Platform. Ongoing research aims to test foot prostheses using this approach. First experiments show the applicability of this kind of actuator for real-time tests. The experimental setup differs from the reference model in respect of its orientation to external forces. This paper describes the relation between measured forces and interface forces that are required for a correct RTS simulation. As a first step towards testing of foot prostheses, we assume a simplified system for RTS simulations. The mechanical system investigated in this paper, is a mass-spring-mass system that moves in the vertical direction and is brought into contact with the ground. The upper mass is controlled by a PD controller (parameters Kp and Kd) so that it follows a desired, defined trajectory.
8 C. Insam et al. Christina Insam* 3 0 2 4 6 8 10 7 7.5 8 ·10−2 Time in s Displacement in m z(V) validation z(V) RTS simulation (a) (c) Christina Insam* 0 2 4 6 8 10 7 7.5 8 ·10−2 Time in s Displacement in m z(V) validation z(V) RTS simulation (b) (d) 0 2 4 6 8 10 −30 −20 −10 0 Time in s Force inN Fint validation Fint RTS simulation 0 2 4 6 8 10 − 30 −20 −10 0 Time in s Force inN Fint validation Fint RTS simulation Fig. 1.6 Comparison of displacements and forces at the interface between validation simulation (dashed line) and real-time hybrid test (solid line) with smaller controller values for Kp and Kd. Experiments were conducted with a trajectory of frequency 0.25 Hz without and with delay compensation. (a) Displacement 0.25 Hz. (b) Displacement 0.25 Hz with delay compensation. (c) Interface Force 0.25 Hz. (d) Interface Force 0.25 Hz with delay compensation Depending on the parameters of the PD controller, the dynamic behavior of the mechanical system is dominated by the PD controller or by the dynamics through coupling, i.e. the dynamics of the transfer system and forces when the device under test comes into contact. Experiments were conducted with different values for Kp and Kd as well as with delay compensation. The technique adopted for delay compensation is a polynomial extrapolation with degree n = 2. Results show that the influence of the coupling dynamics causes the RTS simulation to become unstable. The accuracy improves when using the delay compensation, although the system is still unstable in the RTS simulation. These preliminary results reveal the main obstacles that will also occur in the RTS simulation of a human walking with a prostheses. Hence, we need to improve the current test setup to obtain more accurate results even for those systems where the numerical part is floating. References 1. Tryggvason, H., Starker, F., Lecompte, C., Jónsdóttir, F.: Modeling and simulation in the design process of a prosthetic foot. In: Proceedings of the 58th SIMS, pp. 398–404 (2016). https://doi.org/10.3384/ecp17138398 2. Marinelli, C.: Design, development and engineering of a bench for testing lower limb prosthesis, with focus on high-technological solutions. PhD thesis (2016) 3. Nakashima, M., Masaoka, N.: Real-time on-line test for MDOF systems. Earthq. Eng. Struct. Dyn. 28(4), 393–420 (1999). https://doi.org/10. 1002/(SICI)1096-9845(199904)28:4<393::AIDEQE823>3.0.CO;2-C 4. Bonnet, P.A., Lim, C.N., Williams, M.S., Blakeborough, A., Neild, S.A., Stoten, D.P., Taylor, C.A.: Real-time hybrid experiments with Newmark integration, MCSmd outer-loop control and multi-tasking strategies. Earthq. Eng. Struct. Dyn. 36, 119–141 (2007). https://doi. org/10.1002/eqe.arXiv:1403.5481 5. Blakeborough, A., Williams, M.S., Darby, A.P., Williams, D.M.: The development of real-time substructure testing. Philos. Trans. R. Soc. Lond. A Math. Phys. Eng. Sci. 359(1786), 1869–1891 (2001)
1 A Step Towards Testing of Foot Prostheses Using Real-Time Substructuring (RTS) 9 6. Bosse, D., Radner, D., Schelenz, R., Jacobs, G.: Analysis and application of hardware in the loop wind loads for full scale nacelle ground testing. DEWI Mag. 43, 65–70 (2013) 7. Harris, M.J., Christenson, R.E.: Experimental test of spacecraft parachute deployment using real-time hybrid substructuring. In: Sensors and Instrumentation, Aircraft/Aerospace and Energy Harvesting, Conference Proceedings of the Society for Experimental Mechanics Series, vol. 8, pp. 67–70 (2018) 8. Herrmann, S., Kluess, D., Kaehler, M., Grawe, R., Rachholz, R., Souffrant, R., Zierath, J., Bader, R., Woernle, C.: A novel approach for dynamic testing of total hip dislocation under physiological conditions. PLoS One 10(12), 1–24 (2015). https://doi.org/10.1371/journal.pone. 0145798 9. Yang, Z., Iravani, P., Plummer, A., Pan, M.: Investigation of hardware-in-the-loop walking/running test with spring mass system. In: Towards Autonomous Robotic Systems, pp. 126–133 (2017). https://doi.org/10.1007/978-3-319-64107-2 10. Horiuchi, T., Inoue, M., Konno, T., Namita, Y.: Real-time hybrid experimental system with actuator delay compensation and its application to a piping system with energy absorber. Earthq. Eng. Struct. Dyn. 28(10), 1121–1141 (1999). https://doi.org/10.1002/(SICI)10969845(199910)28:10<1121::AIDEQE858>3.0.CO;2-O 11. Darby, A.P., Williams, M.S., Blakeborough, A.: Stability and delay compensation for real-time substructure testing. J. Eng. Mech. 128(12), 1276–1284 (2002). https://doi.org/10.1061/(ASCE)0733-9399(2002)128:12(1276) 12. Bonnet, P.A.: The development of multi-axis real-time substructure testing. PhD thesis, University of Oxford (2006) 13. Bartl, A., Mayet, J., Rixen, D.J.: Adaptive feedforward compensation for real time hybrid testing with harmonic excitation. In: Proceedings of the 11th International Conference on Engineering Vibration September (2015) 14. Boge, T., Ma, O.: Using advanced industrial robotics for spacecraft rendezvous and docking simulation. In: Proceedings – IEEE International Conference on Robotics and Automation (2011). https://doi.org/10.1109/ICRA.2011.5980583 15. Riebe, S., Ulbrich, H.: Experiments on linear and nonlinear control of a multi-DOF parallel mechanism. In: Proceedings of the ASME 2003 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (DETC) (2003) 16. Ottobock Foot Prosthesis, Duderstadt. http://www.ottobock.de. Accessed 04 Mar 2018 17. Wittmann, R., Hildebrandt, A.C., Ewald, A., Buschmann, T.: An estimation model for footstep modifications of biped robots. In: IEEE International Conference on Intelligent Robots and Systems, pp. 2572–2578 (2014). https://doi.org/10.1109/IROS.2014.6942913
Chapter 2 Augmented Reality for Interactive Robot Control Levi Manring, John Pederson, Dillon Potts, Beth Boardman, David Mascarenas, Troy Harden, and Alessandro Cattaneo Abstract Robots are widely used to support mission-critical, high-risk and complex operations. Human supervision and remote robot control are often required to operate robots in unpredictable and changing scenarios. Often, robots are controlled remotely by technicians via joystick interfaces which require training and experience to operate. To improve robot usage and practicality, we propose using augmented reality (AR) to create a more intuitive, less training-intensive means of controlling robots than traditional joystick control. AR is a creative platform for developing robot control systems, because AR combines the real world (the environment around the user, the physical robot, etc.) with the digital world (holograms, digital displays, etc.); it can even interpret physical gestures, such as pinching two fingers. In this research, a Microsoft Hololens headset is used to create an AR environment to control a Yaskawa Motoman SIA5D robot. The control process begins with the user placing an interactable holographic robot in 3D space. The user can then select between two control methods: manual control and automatic control. In manual control, the user can move the end effector of the holographic robot and the physical robot will respond immediately. In automatic control, the user can move the end effector of the holographic robot to a desired location, view a holographic preview of the motion, and select execute if the motion plan is satisfactory. In this preview mode, the user is able to preview both the motion of the robot and the torques experienced by the joints of the manipulator. This gives the user additional feedback on the planned motion. In this project we succeeded in creating an AR control system that makes controlling a robotic manipulator intuitive and effective. Keywords Augmented reality · Microsoft Hololens · Robotic arm · Force feedback · Motion planning 2.1 Introduction Augmented reality (AR) is an emerging field that offers new tools for human-based control and interaction with complex systems. AR combines the sensory experience of a user with information from a digital system; this contrasts with virtual reality, which seeks to eliminate the user’s perception of the real world. Physical-digital interaction allows for intuitive control of digital systems with sensory feedback that can be more easily interpreted by the user. Intuitive AR systems are used in applications such as virtual surgery training and robotic tele-operation. AR is particularly advantageous for robotic control where direct human control is needed; because conditions and objectives are unpredictable or rapidly changing, decisions must be made by an operator in real time. Current robotic control is difficult because of non-intuitive controls (e.g. moving a joystick to the right to rotate a gripper) and a lack of force feedback, since haptic feedback interfaces are still niche products. Without force feedback, it is difficult for an operator to have a sense of how much force a robot is exerting on an object; this often leads to over- or under-applying L.Manring Department of Mechanical Engineering and Materials Science, Pratt School of Engineering, Duke University, Durham, NC, USA J. Pederson Department of Mechanical Engineering, The Fu Foundation School of Engineering and Applied Science, Columbia University, New York, NY, USA D. Potts Department of Mechanical Engineering, Ira A. Fulton College, Brigham Young University, Provo, UT, USA B. Boardman · T. Harden · A. Cattaneo ( ) Engineering Technology and Design Division, Los Alamos National Laboratory, Los Alamos, NM, USA e-mail: cattaneo@lanl.gov D. Mascarenas Engineering Institute, Los Alamos National Laboratory, Los Alamos, NM, USA © Society for Experimental Mechanics, Inc. 2020 N. Dervilis (ed.), Special Topics in Structural Dynamics & Experimental Techniques, Volume 5, Conference Proceedings of the Society for Experimental Mechanics Series, https://doi.org/10.1007/978-3-030-12243-0_2 11
12 L. Manring et al. forces, which can be detrimental in delicate operations. These two limitations require human operators to have extensive training and experience. By implementing AR robotic control, the amount of training required to operate robots will be reduced, and operators will be able to control applied forces more accurately. Improving human-robot control will allow for a wider utilization of robots in many fields where real-time operator control is necessary. 2.2 Background Augmented reality (AR) is emerging as a technology that is able to connect people to a growing number of tools in a more user-friendly manner. As a result, AR applications are growing in complexity and in scope, which can be seen in the following published applications. There are multiple AR mediums, and in [1] touch-screen devices are used to implement AR as a feature in a game that encourages exercise in young people. The authors developed their research game using Unity3D, the Vuforia AR library, and OpenGL to create an interactive AR world game-space on touch-screen devices. Ref. [2] uses spatial AR. This involves projecting AR from a projector (as opposed to the classic uses of touchscreen devices, computers, and glasses). This research sought to develop a control method to control the position and orientation of such a projector to be used for an even wider workspace. The advantage of the system proposed in this work lies in its understanding of kinematic control without knowing the exact kinematic specifications of a projector pan/tilt mount (such as an ad hoc assembly). In [3], the authors propose an AR system that generates pattern identifications by analyzing infrared optical markers projected in actual space measured by an infrared camera. For this system, the operator gives commands by projecting markers on a position on a screen. AR applications continue to be developed to assist in the daily activities of people affected by adverse health conditions [4]. This paper proposes a unique application for AR to assist people affected by paralysis in performing daily tasks. The authors use a BMI (brain-machine interface) to measure the brain-waves of the user be means of electroencephalography. They also use gaze-selection via pupil tracking. The BMI enables to denote either a “command” state or a “rest” state based on the brain activity of the user, and the gaze-selection is used to denote direction and goals for the robotic arm. The researchers found that across the board, AR assisted the user in selection and movement of obstacles using the robotic arm. The researchers propose in future work the importance of changing the interface medium from a computer screen to wearable AR glasses. As research in the field of robotics continues to grow, AR is being used more often to reduce the complexity of controlling robots. In [5], the authors propose a mixed reality human-robot interface to assist operators in tele-operation for remote maintenance tasks. In this system, visual inspection and corrective task execution are two phases activated by the operator. For the second phase, a virtual robot helps the operator visualize the movement of the robotic arm before the task is executed. This allows the user to create/refine path movements in advance of completing them. However, this system also requires that the obstacles in the environment are known about a priori. Part of their interest in future work is to input depth sensors to detect objects in the surroundings. In [6], the authors seek to perform something similar to [5] by overlaying a computer graphic/hologram on top of the actual robot that is updated based on user input. The purpose of this graphic is to account for the time delay between when the user sends a command and when the physical robot responds. The graphic will move immediately, the actual robot will follow the graphic, and they will eventually meet. In addition, digital handles are projected over the real robotic hand which can be selected and moved to rotate the robotic arm. Another AR-based system to facilitate programming robots and planning trajectories is presented in [7]. This research incorporates robot dynamics into taskoptimized executable robot paths that are also collision free. A method for assisting in robot end-effector planning using AR is presented in [8]. The authors implement a trajectory optimization scheme to assist in end-effector location and angle. This research also considers the system dynamics of the robot while checking for collision detection and simulating the torque and velocity of each robot joint. If the torque/velocity are outside the designated limits, that joint is highlighted as one that has a high probability of deviating from a planned trajectory. The package Roboop is used for robot kinematics and dynamics modeling (this package is no longer available). The authors are able to demonstrate an increased performance in a trajectory that is obtained by incorporating robot dynamics versus one that only considers kinematics. There has been a significant amount of work in the area of providing force-feedback to the user using AR. In [9], the authors employ the principle of reverse electrovibration using AR, where a weak electrical signal is injected anywhere on the user body to create an oscillating electrical field around the user’s fingers. Using this principle, the user can have a perception of texture that is physically not present. The work of the authors resulted in a device, REVEL, that allows tactile textures to be modified in real time. One of the primary benefits of this system is that it allows tactile feedback without the use of gloves, since it only requires the user to wear a small tactile passive signal generator that can be attached anywhere on the user’s body. The research presented in [10] seeks to overcome the issues with time delay as it influences tele-operated systems. The authors propose a virtual model which can estimate the real-time force feedback and give visual information using AR to
2 Augmented Reality for Interactive Robot Control 13 the operator, which reduces the effects of time delay. In [11], the authors present an interesting method for training surgeons using force feedback in combination with AR. This gives the trainee information on collision detection and location. The motivation of this research is to reduce the risks associated with training new surgeons in an actual surgery. A method for providing haptic feedback in an AR setting is presented in [12]. The goal of this research is to enable AR users to have feedback on roughness and friction of surfaces. This paper presents a novel method of using an ungrounded haptic stylus with actuators to overlay texture vibration and friction forces when the user touches an object, which allows the virtual and real worlds to mix. These AR methods have also been extended to control multiple robots, as shown in [13], where a method for implementing AR for single-user control of four Mindstorms NXT robots is presented. In particular, the authors use Point-and-Go and path planning as two methods to reduce mission completion times as compared to typical joystick control methods. They use a Probabilistic Roadmap Planner as their path planner for the robots to provide a higher level of automation than the Pointand-Go method. This resulted in significantly lower mission completion times and a strong preference by test users for the path-planning AR method. In [14], the authors seek to use ROS and Unity to simulate multiple UAVs to easily verify and develop flight control and navigation algorithms. They also present a layout of the system level structure that is very useful for reconstructing similar applications (including Windows, Ubuntu, ROS, and Unity connections and how it works together). The authors also discuss Unity Socket, which uses TCP/IP to exchange data between ROS and Unity3D. There are a number of publications that detail the connection between ROS and Unity3D as a means for implementing AR in robotic systems. In [15], the authors seek to reduce the costs of developing real robots and eliminate some of the cost of performing real-world human-robot interaction experiments. They present a modified virtual reality system named SIGVerse 3.0 which includes a connection mechanism to bind Unity to the ROS environment, enabling users to develop robot software in ROS and place a virtual robot model into Unity applications. In addition, this paper includes a helpful table of functions and limitations of related systems which show the compatibility and usefulness of different platforms, showing that their new version of SIGVerse is preferable. In [16], the authors cover the main advantages of different software for visualization and robotic control, particularly using ROS and Unity3D. ROS is based on message passing while Unity3D is tied to a rendering loop where commands are made/updated each frame. Because of this difference, interaction between the two programs is difficult. ROSbridge is used to connect ROS with the outside world. ROSbridge uses WebSocket protocol to communicate JSON (JavaScript Object Notation) strings. The process of communicating between ROS and Unity3D involves transmitting JSON-encoded messages through a WebSocket. In [17], a system is presented for simulating executing the task of monitoring an industrial process, with a particular focus on shortening optimization time for a robot and make the final commissioning more efficient (this is related to the expensive process of tuning and calibrating a one-of-a-kind robot). One of the key parts of their implementation is the use of ROSbridge, which establishes a connection between Unity3D and ROS which allows for invoking ROS services. Our application of AR is a unique system that allows the user to control a robot by means of holograms projected by a Microsoft Hololens AR headset and provides the user torque feedback that can assist in robot operation. 2.3 Methodology 2.3.1 Overview To implement robotic control with augmented reality, a Microsoft Hololens headset, used to create an AR environment, communicates with a controllable robotic arm, the Yaskawa Motoman SIA5D. The layout of this system can be seen in Fig. 2.1. The Hololens is a Windows 10 based device that creates and modifies the AR environment. A Unity development environment, which utilizes C# in a .NET framework and scripting backend, creates the AR scene. The Hololens projects holograms into the real-world view of the user. Users interact with the holograms through hand motions. Hand motions, known as gestures, are used to make selections and move holograms. One example of a gesture is the ability to “click”, which occurs when the user moves thumb and index finger from an open position to a closed position. To interface with the SIA5D Motoman robot, we used the conventional Robot Operating System (ROS) (version indigo). The Hololens sends and receives information from the robotic arm using the ROS. The communication between the Hololens and ROS is done via a ROSbridge websocket.
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