Design and Validation of a Low-Level Controller for Hierarchically Controlled Exoskeletons
Abstract
:1. Introduction
- An in-depth analysis and description of the low-level device and the required electrical designs for sensor acquisition, which may contain analog filters and other critical elements that dictate system dynamics and control performance.
- An overview of the networking strategy between the high-level PC and the low-level sensor acquisition devices, which typically have computation constraints, where the completion of communication, data acquisition, and control tasks is not guaranteed within a communication step.
- Results that validate the system performance in sensor acquisition and networking, separating the performance of the hardware from the rehabilitation strategy to better prepare the researcher to investigate the core research objectives of the robotic device and ensure the safety of the operator.
- A lack of readily available cost-effective, open-sourced, and flexible low-level data acquisition and control solutions for SEA-driven robotic systems.
- An in-depth description of the electrical design and networking strategy for collecting sensor feedback and controlling SEAs while communicating with a high-level controller that can be generalizable to many exoskeletal and other medical robots;
- Two sets of open-loop results, which provide a validation of the expected performance of sensor feedback from a quadrature encoder, absolute encoder, motor current, force sensor, and IMU, along with ensuring proper networking with the high-level controller;
- The hardware and software presented in this work are available open source(https://gitlab.com/trec-lab, accessed on 18 December 2022), giving researchers a direct strategy for achieving the data acquisition and control of an SEA system;
- The developed open-source repositories along with the design details discussed in this paper also provide the flexibility to slightly modify or add additional sensors according to the particular applications.
2. Sensor Interface Shield
2.1. Power Regulation
2.2. CAN Capability
2.3. Force Sensing
2.4. Motor Input
2.5. Encoders
2.6. Inertial Measurement Unit (IMU)
3. Motor Shield
3.1. Motor Current Feedback
3.2. Voltage Rails
4. High-Level Networking
- HALT: Triggers the LLC’s emergency-stop and turns motors off.
- CONTROL: Commands the LLC to achieve a desired actuator torque output. The LLC then sends the master the most updated sensor values.
- IDLE: Puts the low-level controllers in standby.
- INITIALIZATION: Sends initialization data that the microcontroller needs before it can start.
5. Results and Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- World Report on Disability. Available online: https://www.who.int/news-room/fact-sheets/detail/assistive-technology (accessed on 25 November 2022).
- Survey of Occupational Injuries. Available online: https://www.bls.gov/iif/nonfatal-injuries-and-illnesses-tables.htm#dafw (accessed on 25 November 2022).
- Alemi, M.M.; Madinei, S.; Kim, S.; Srinivasan, D.; Nussbaum, M.A. Effects of two passive back-support exoskeletons on muscle activity, energy expenditure, and subjective assessments during repetitive lifting. Hum. Factors 2020, 62, 458–474. [Google Scholar] [CrossRef]
- Kalita, B.; Narayan, J.; Dwivedy, S.K. Development of active lower limb robotic-based orthosis and exoskeleton devices: A systematic review. Int. J. Soc. Robot. 2021, 13, 775–793. [Google Scholar] [CrossRef]
- Kuo, C.C.; Chen, S.C.; Chen, T.Y.; Ho, T.J.; Lin, J.G.; Lu, T.W. Effects of long-term Tai-Chi Chuan practice on whole-body balance control during obstacle-crossing in the elderly. Sci. Rep. 2022, 12, 2660. [Google Scholar] [CrossRef]
- Alves, F.; Cruz, S.; Ribeiro, A.; Bastos Silva, A.; Martins, J.; Cunha, I. Walkability Index for Elderly Health: A Proposal. Sustainability 2020, 12, 7360. [Google Scholar] [CrossRef]
- Forte, G.; Leemhuis, E.; Favieri, F.; Casagrande, M.; Giannini, A.M.; De Gennaro, L.; Pazzaglia, M. Exoskeletons for Mobility after Spinal Cord Injury: A Personalized Embodied Approach. J. Pers. Med. 2022, 12, 380. [Google Scholar] [CrossRef] [PubMed]
- Angekumbura, C.; Dilshani, T.; Perera, K.; Jayarathna, S.; Kahandawarachchi, K.; Udara, S. A review of methods to detect divided attention impairments in Alzheimer’s disease. Procedia Comput. Sci. 2022, 198, 193–202. [Google Scholar] [CrossRef]
- Plaza, A.; Hernandez, M.; Puyuelo, G.; Garces, E.; Garcia, E. Lower-limb medical and rehabilitation exoskeletons: A review of the current designs. IEEE Rev. Biomed. Eng. 2021, 16, 278–291. [Google Scholar] [CrossRef]
- Shi, D.; Zhang, W.; Zhang, W.; Ding, X. A review on lower limb rehabilitation exoskeleton robots. Chin. J. Mech. Eng. 2019, 32, 74. [Google Scholar] [CrossRef] [Green Version]
- Andersson, R.; Björsell, N. The Energy Consumption and Robust Case Torque Control of a Rehabilitation Hip Exoskeleton. Appl. Sci. 2022, 12, 11104. [Google Scholar] [CrossRef]
- Veneman, J.F.; Kruidhof, R.; Hekman, E.E.G.; Ekkelenkamp, R.; Van Asseldonk, E.H.F.; van der Kooij, H. Design and Evaluation of the LOPES Exoskeleton Robot for Interactive Gait Rehabilitation. IEEE Trans. Neural Syst. Rehabil. Eng. 2007, 15, 379–386. [Google Scholar] [CrossRef]
- Meuleman, J.; van Asseldonk, E.; van Oort, G.; Rietman, H.; van der Kooij, H. LOPES II—Design and evaluation of an admittance controlled gait training robot with shadow-leg approach. IEEE Trans. Neural Syst. Rehabil. Eng. 2015, 24, 352–363. [Google Scholar] [CrossRef] [PubMed]
- Banala, S.K.; Agrawal, S.K.; Kim, S.H.; Scholz, J.P. Novel Gait Adaptation and Neuromotor Training Results Using an Active Leg Exoskeleton. IEEE/ASME Trans. Mech. 2010, 15, 216–225. [Google Scholar] [CrossRef]
- Vantilt, J.; Tanghe, K.; Afschrift, M.; Bruijnes, A.K.; Junius, K.; Geeroms, J.; Aertbeliën, E.; De Groote, F.; Lefeber, D.; Jonkers, I.; et al. Model-based control for exoskeletons with series elastic actuators evaluated on sit-to-stand movements. J. Neuroeng. Rehabil. 2019, 16, 65. [Google Scholar] [CrossRef] [PubMed]
- Beiter, B.; Herron, C.; Leonessa, A. Whole Body Control for Haptic Interaction with VR. In Proceedings of the 2022 American Control Conference (ACC), Atlanta, GA, USA, 8–10 June 2022; pp. 653–658. [Google Scholar]
- Pratt, J.; Krupp, B.; Morse, C.; Collins, S. The RoboKnee: An exoskeleton for enhancing strength and endurance during walking. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA ’04), New Orleans, LA, USA, 26 April–1 May 2004; Volume 3, pp. 2430–2435. [Google Scholar] [CrossRef]
- Pratt, G.; Williamson, M. Series elastic actuators. In Proceedings of the Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots, Pittsburgh, PA, USA, 5–9 August 1995; Volume 1, pp. 399–406. [Google Scholar] [CrossRef] [Green Version]
- Ragonesi, D.; Agrawal, S.; Sample, W.; Rahman, T. Series elastic actuator control of a powered exoskeleton. In Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA, 30 August–3 September 2011; pp. 3515–3518. [Google Scholar] [CrossRef]
- Knabe, C.; Lee, B.; Orekhov, V.; Hong, D. Design of a compact, lightweight, electromechanical linear series elastic actuator. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference; American Society of Mechanical Engineers: New York, NY, USA, 2014; Volume 46377, p. V05BT08A014. [Google Scholar]
- Zhao, Y.; Paine, N.; Jorgensen, S.J.; Sentis, L. Impedance Control and Performance Measure of Series Elastic Actuators. IEEE Trans. Ind. Electron. 2018, 65, 2817–2827. [Google Scholar] [CrossRef]
- Orekhov, V.L.; Knabe, C.S.; Hopkins, M.A.; Hong, D.W. An unlumped model for linear series elastic actuators with ball screw drives. In Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany, 28 September–3 October 2015; pp. 2224–2230. [Google Scholar] [CrossRef]
- Liu, L.; Leonhardt, S.; Ngo, C.; Misgeld, B.J.E. Impedance-Controlled Variable Stiffness Actuator for Lower Limb Robot Applications. IEEE Trans. Autom. Sci. Eng. 2020, 17, 991–1004. [Google Scholar] [CrossRef]
- Orekhov, V.; Lahr, D.; Lee, B.; Hong, D. Configurable Compliance for Series Elastic Actuators. In 37th Mechanisms and Robotics Conference, International Design Engineering Technical Conferences and Computers and Information in Engineering Conference; American Society of Mechanical Engineers: New York, NY, USA, 2013; Volume 55942, p. V06BT07A021. [Google Scholar] [CrossRef]
- Hopkins, M.A.; Ressler, S.A.; Lahr, D.F.; Leonessa, A.; Hong, D.W. Embedded joint-space control of a series elastic humanoid. In Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany, 28 Septembe–2 October 2015; pp. 3358–3365. [Google Scholar]
- Batty, T.; Ehrampoosh, A.; Shirinzadeh, B.; Zhong, Y.; Smith, J. A Transparent Teleoperated Robotic Surgical System with Predictive Haptic Feedback and Force Modelling. Sensors 2022, 22, 9770. [Google Scholar] [CrossRef]
- Chellal, A.A.; Lima, J.; Gonçalves, J.; Fernandes, F.P.; Pacheco, F.; Monteiro, F.; Brito, T.; Soares, S. Robot-Assisted Rehabilitation Architecture Supported by a Distributed Data Acquisition System. Sensors 2022, 22, 9532. [Google Scholar] [CrossRef]
- Kim, J.W.; Choi, Y.L.; Jeong, S.H.; Han, J. A Care Robot with Ethical Sensing System for Older Adults at Home. Sensors 2022, 22, 7515. [Google Scholar] [CrossRef]
- Rogowski, A. Scenario-Based Programming of Voice-Controlled Medical Robotic Systems. Sensors 2022, 22, 9520. [Google Scholar] [CrossRef]
- Pei, Y.; Han, T.; Zallek, C.M.; Liu, T.; Yang, L.; Hsiao-Wecksler, E.T. Design and Clinical Validation of a Robotic Ankle-Foot Simulator with Series Elastic Actuator for Ankle Clonus Assessment Training. IEEE Robot. Autom. Lett. 2021, 6, 3793–3800. [Google Scholar] [CrossRef]
- Slovich, M.; Paine, N.; Kemper, K.; Metzger, A.; Edsinger, A.; Weber, J.; Sentis, L. Hume: A bipedal robot for human-centered hyperagility. In Dynamic Walking Conference; 2012; Available online: https://www.ihmc.us/dwc2012files/Sentis.pdf (accessed on 25 November 2022).
- Lahr, D.; Orekhov, V.; Lee, B.; Hong, D. Early developments of a parallelly actuated humanoid, SAFFiR. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference; American Society of Mechanical Engineers: New York, NY, USA, 2013; Volume 55942, p. V06BT07A054. [Google Scholar]
- Lee, B.K.T.S. Design of a Humanoid Robot for Disaster Response. Master’s Thesis, Virginia Tech, Blacksburg, VA, USA, 2014. [Google Scholar]
- Knabe, C.; Griffin, R.J.; Burton, J.; Cantor-Cooke, G.; Dantanarayana, L.; Day, G.; Ebeling-Koning, O.; Hahn, E.; Hopkins, M.A.; Neal, J.; et al. Team VALOR’s ESCHER: A Novel Electromechanical Biped for the DARPA Robotics Challenge. J. Field Robot. 2017, 34, 912–939. [Google Scholar] [CrossRef]
- Welch, S.B.; Runyon, C.D.; Beiter, B.B.; Herron, C.W.; Kalita, B.; Leonessa, A. A Mapping Approach to Achieve Torque Control for Parallel-Actuated Robotic Systems. In 2022 ASME’s International Mechanical Engineering Congress & Exposition (IMECE); ASME: New York, NY, USA, 2022; in press. [Google Scholar]
- Ahn, J.; Kim, D.; Bang, S.; Paine, N.; Sentis, L. Control of a High Performance Bipedal Robot Using Viscoelastic Liquid Cooled Actuators. In Proceedings of the 2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids), Toronto, ON, Canada, 15–17 October 2019; pp. 146–153. [Google Scholar] [CrossRef] [Green Version]
- EasyCAT PRO. Available online: https://www.bausano.net/en/hardware/easycat-pro.html (accessed on 22 April 2022).
- Texas Instruments. Tiva TM4C123GH6PM Microcontroller Data Sheet; Texas Instruments: Austin, TX, USA, 2014. [Google Scholar]
- Beckhoff. EtherCAT—The Ethernet Fieldbus. Available online: https://www.beckhoff.com/en-us/products/i-o/ethercat/ (accessed on 22 April 2022).
- Kogelis, M.; Fuge, Z.J.; Herron, C.W.; Kalita, B.; Leonessa, A. Design of Low-Level Hardware for a Multi-Layered Control Architecture. In 2022 ASME’s International Mechanical Engineering Congress & Exposition (IMECE); ASME: New York, NY, USA, 2022; in press. [Google Scholar]
- Tremaroli, N.J.; Stelmack, M.A.; Herron, C.W.; Kalita, B.; Leonessa, A. Flexible Low-Level Control Software Framework for Achieving Critical Real-Time Deadlines. In 2022 ASME’s International Mechanical Engineering Congress & Exposition (IMECE); ASME: New York, NY, USA, 2022; in press. [Google Scholar]
- Rohm Semiconductor. BA7805FP Datasheet; Rohm: Kyoto, Japan, 2020. [Google Scholar]
- Ressler, S.A. Design and Implementation of a Dual Axis Motor Controller for Parallel and Serial Series Elastic Actuators. Master’s Thesis, Virginia Tech, Blacksburg, VA, USA, 2014. [Google Scholar]
- Shah, S. Design and Implementation of a Scalable Real-Time Motor Controller Architecture for Humanoid Robots and Exoskeletons. Master’s Thesis, Virginia Tech, Blacksburg, VA, USA, 2017. [Google Scholar]
- Futek Inc. Futek Model LCM200—Ultra Light Miniature Universal Threaded Load Cell; Futek Inc.: Irvine, CA, USA, 2011. [Google Scholar]
- Corporation, B.B. TI INA125 Datasheet; Texas Instruments: Tucson, AZ, USA, 1998. [Google Scholar]
- Advanced Motion Controls. Analog Servo Drive AZBDC12A8 Datasheet; Advanced Motion Controls: Camarillo, CA, USA, 2018. [Google Scholar]
- Pizá, R.; Carbonell, R.; Casanova, V.; Cuenca, Á.; Salt Llobregat, J.J. Nonuniform Dual-Rate Extended Kalman-Filter-Based Sensor Fusion for Path-Following Control of a Holonomic Mobile Robot with Four Mecanum Wheels. Appl. Sci. 2022, 12, 3560. [Google Scholar] [CrossRef]
- Nazaruddin, Y.; Tamba, T.; Faruqi, I.; Waluya, M.; Widyotriatmo, A. On Using Unscented Kalman Filter Based Multi Sensors Fusion for Train Localization. In Proceedings of the 2019 12th Asian Control Conference (ASCC), Kitakyushu, Japan, 9–12 June 2019; pp. 1137–1142. [Google Scholar]
- Maxon. Encoder MR Type ML, 128–1000 CPT, 3 Channels, with Line Driver Datasheet; Maxon: Sachseln, Switzerland, 2020. [Google Scholar]
- Shi, F.; Zhao, M.; Anzai, T.; Chen, X.; Okada, K.; Inaba, M. External wrench estimation for multilink aerial robot by center of mass estimator based on distributed IMU system. In Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada, 20–24 May 2019; pp. 1891–1897. [Google Scholar]
- InvenSense. MPU9250 Datasheet; InvenSense: San Jose, CA, USA, 2016. [Google Scholar]
- Madgwick, S. An efficient orientation filter for inertial and inertial/magnetic sensor arrays. Rep. x-io Univ. Bristol (UK) 2010, 25, 113–118. [Google Scholar]
- Zumbahlen, H. Twin T Notch Filter; Rev. A; Analog Devices: Norwood, MA, USA, 2017. [Google Scholar]
- Drew, J. Easy, ±5V Split-Voltage Power Supply for Analog Circuits Draws only 720 nA at No Load. Available online: https://www.analog.com/en/technical-articles/easy-split-voltage-power-supply-for-analog-circuits-draws-only-720na.html (accessed on 26 November 2022).
- IHMC. Open Robotics Software. Available online: ihmcrobotics.github.io (accessed on 25 November 2022).
- IHMC. EtherCAT Master Repository. Available online: https://github.com/ihmcrobotics/ihmc-ethercat-master (accessed on 25 November 2022).
Data Type | Data Element | Byte Size |
---|---|---|
uint8_t | Signal To Master | 1 |
uint8_t | Master Process ID | 1 |
float[] | Force (Actuator 0 and 1) | 8 (4 each) |
uint32_t[] | Motor Encoder Raw Position (Actuator 0 and 1) | 8 (4 each) |
int8_t[] | Motor Encoder Direction (Actuator 0 and 1) | 2 (1 each) |
int32_t[] | Motor Encoder Raw Velocity (Actuator 0 and 1) | 8 (4 each) |
float[] | Motor Current (Actuator 0 and 1) | 8 (4 each) |
float[] | Joint Angle (0 and 1) | 8 (4 each) |
float[] | Accelerometer (X, Y, and Z) | 12 (4 each) |
float[] | Gyroscope (X, Y, and Z) | 12 (4 each) |
float[] | Magnetometer (X, Y, and Z) | 12 (4 each) |
float[] | Force–Torque Force (X, Y, and Z) | 12 (4 each) |
float[] | Force–Torque Torque (X, Y, and Z) | 12 (4 each) |
uint8_t[] | Remaining Bytes | 24 |
Data Type | Data Element | Byte Size |
---|---|---|
uint8_t | Signal From Master | 1 |
uint8_t | Master Process ID | 1 |
uint8_t[] | Direction (Actuator 0 and 1) | 2 (1 each) |
float[] | PWM (Actuator 0 and 1) | 8 (4 each) |
uint8_t[] | Remaining Bytes | 116 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Herron, C.W.; Fuge, Z.J.; Kogelis, M.; Tremaroli, N.J.; Kalita, B.; Leonessa, A. Design and Validation of a Low-Level Controller for Hierarchically Controlled Exoskeletons. Sensors 2023, 23, 1014. https://doi.org/10.3390/s23021014
Herron CW, Fuge ZJ, Kogelis M, Tremaroli NJ, Kalita B, Leonessa A. Design and Validation of a Low-Level Controller for Hierarchically Controlled Exoskeletons. Sensors. 2023; 23(2):1014. https://doi.org/10.3390/s23021014
Chicago/Turabian StyleHerron, Connor W., Zachary J. Fuge, Madeline Kogelis, Nicholas J. Tremaroli, Bhaben Kalita, and Alexander Leonessa. 2023. "Design and Validation of a Low-Level Controller for Hierarchically Controlled Exoskeletons" Sensors 23, no. 2: 1014. https://doi.org/10.3390/s23021014