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Hybrid Force Motion Control with Estimated Surface Normal for Manufacturing Applications

Ehsan Nasiri, Long Wang

TL;DR

This work tackles the challenge of robust, high-precision force-controlled manipulation on uncertain surfaces by presenting a hybrid force-motion framework that incorporates online surface normal estimation. The core idea is to estimate the surface normal direction from force sensing and velocity commands while compensating for friction, enabling accurate probing and contact-tracking in manufacturing tasks. The authors implement the method in ROS2 and validate it on a 7-DoF manipulator with a force/torque sensor, demonstrating improved position-force tracking on linear, sinusoidal, and dome-shaped surfaces with an average gain of about 5%. The approach offers practical impact for precision manufacturing applications such as thermoplastic tape replacement, where surface geometry and friction can otherwise degrade performance.

Abstract

This paper proposes a hybrid force-motion framework that utilizes real-time surface normal updates. The surface normal is estimated via a novel method that leverages force sensing measurements and velocity commands to compensate the friction bias. This approach is critical for robust execution of precision force-controlled tasks in manufacturing, such as thermoplastic tape replacement that traces surfaces or paths on a workpiece subject to uncertainties deviated from the model. We formulated the proposed method and implemented the framework in ROS2 environment. The approach was validated using kinematic simulations and a hardware platform. Specifically, we demonstrated the approach on a 7-DoF manipulator equipped with a force/torque sensor at the end-effector.

Hybrid Force Motion Control with Estimated Surface Normal for Manufacturing Applications

TL;DR

This work tackles the challenge of robust, high-precision force-controlled manipulation on uncertain surfaces by presenting a hybrid force-motion framework that incorporates online surface normal estimation. The core idea is to estimate the surface normal direction from force sensing and velocity commands while compensating for friction, enabling accurate probing and contact-tracking in manufacturing tasks. The authors implement the method in ROS2 and validate it on a 7-DoF manipulator with a force/torque sensor, demonstrating improved position-force tracking on linear, sinusoidal, and dome-shaped surfaces with an average gain of about 5%. The approach offers practical impact for precision manufacturing applications such as thermoplastic tape replacement, where surface geometry and friction can otherwise degrade performance.

Abstract

This paper proposes a hybrid force-motion framework that utilizes real-time surface normal updates. The surface normal is estimated via a novel method that leverages force sensing measurements and velocity commands to compensate the friction bias. This approach is critical for robust execution of precision force-controlled tasks in manufacturing, such as thermoplastic tape replacement that traces surfaces or paths on a workpiece subject to uncertainties deviated from the model. We formulated the proposed method and implemented the framework in ROS2 environment. The approach was validated using kinematic simulations and a hardware platform. Specifically, we demonstrated the approach on a 7-DoF manipulator equipped with a force/torque sensor at the end-effector.
Paper Structure (8 sections, 41 equations, 8 figures, 1 table)

This paper contains 8 sections, 41 equations, 8 figures, 1 table.

Table of Contents

  1. Introduction
  2. Modeling of the Robotic System
  3. Kinematic Modeling
  4. Hybrid Force-Motion Control
  5. Estimation of Surface Normal Using Online-Estimated Friction
  6. Optimizing Probe Orientation
  7. Simulation and Experimental Validation
  8. Conclusion

Figures (8)

  • Figure 1: The Robotic System Framework: A 7-DoF Manipulator Equipped with an F/T Sensor and Probe at the End-Effector, Including Workpieces.
  • Figure 2: Hybrid Force-Motion Controller
  • Figure 3: Estimation of Surface Normal Force Compensating for Environmental Friction.
  • Figure 4: Optimizing Probe Orientation: Utilizing Estimated Surface Normal and End-Effector Directions.
  • Figure 5: Tracking Hybrid Motion-Force Errors of the Probe.
  • ...and 3 more figures