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Exploring the dynamic properties and motion reproducibility of a small upper-body humanoid robot with 13-DOF pneumatic actuation for data-driven control

Hiroshi Atsuta, Hisashi Ishihara, Minoru Asada

Abstract

Pneumatically-actuated anthropomorphic robots with high degrees of freedom (DOF) offer significant potential for physical human-robot interaction. However, precise control of pneumatic actuators is challenging due to their inherent nonlinearities. This paper presents the development of a compact 13-DOF upper-body humanoid robot. To assess the feasibility of an effective controller, we first investigate its key dynamic properties, such as actuation time delays, and confirm that the system exhibits highly reproducible behavior. Leveraging this reproducibility, we implement a preliminary data-driven controller for a 4-DOF arm subsystem based on a multilayer perceptron with explicit time delay compensation. The network was trained on random movement data to generate pressure commands for tracking arbitrary trajectories. Comparative evaluations with a traditional PID controller demonstrate superior trajectory tracking performance, highlighting the potential of data-driven approaches for controlling complex, high-DOF pneumatic robots.

Exploring the dynamic properties and motion reproducibility of a small upper-body humanoid robot with 13-DOF pneumatic actuation for data-driven control

Abstract

Pneumatically-actuated anthropomorphic robots with high degrees of freedom (DOF) offer significant potential for physical human-robot interaction. However, precise control of pneumatic actuators is challenging due to their inherent nonlinearities. This paper presents the development of a compact 13-DOF upper-body humanoid robot. To assess the feasibility of an effective controller, we first investigate its key dynamic properties, such as actuation time delays, and confirm that the system exhibits highly reproducible behavior. Leveraging this reproducibility, we implement a preliminary data-driven controller for a 4-DOF arm subsystem based on a multilayer perceptron with explicit time delay compensation. The network was trained on random movement data to generate pressure commands for tracking arbitrary trajectories. Comparative evaluations with a traditional PID controller demonstrate superior trajectory tracking performance, highlighting the potential of data-driven approaches for controlling complex, high-DOF pneumatic robots.
Paper Structure (35 sections, 3 equations, 21 figures, 3 tables)

This paper contains 35 sections, 3 equations, 21 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Robot System
  3. Mechanical System
  4. Drive, Sensory, and Control Systems
  5. Posture Variation
  6. Dynamic Properties and Motion Reproducibility
  7. Experimental Setup
  8. Tested Joints and Postures
  9. Notation
  10. Experiment I-A: Measuring Transmission Time Delay
  11. Method
  12. Results
  13. Experiment I-B: Measuring Minimum Pressure Command
  14. Method
  15. Results
  16. ...and 20 more sections

Figures (21)

  • Figure 1: The 13-DOF pneumatically-actuated upper-body humanoid robot developed in this study. The main photo shows the front view while the inset on the lower left shows the robot's front-right side with a different pose.
  • Figure 2: Kinematic diagram of the 13-DOF upper body. Joints 2, 3, 8, and 9 in the chest and scapula are driven by air cylinders A2, A3, A8, and A9, while the others driven by rotary actuators.
  • Figure 3: Actuator mechanisms. (a) Assembly drawing of a vane-type rotary actuator. (b) Assembly drawing of an air cylinder. (c) Internal schematic of the rotary actuator. (d) Internal schematic of the air cylinder.
  • Figure 4: Hardware overview of the developed robot: (a) front view and (b) rear view, with key components labeled.
  • Figure 5: Overview of the experimental control system. A host computer manages valve commands and sensor data acquisition, allowing a client computer to interface with the robot over a local network.
  • ...and 16 more figures