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Encoding Biomechanical Energy Margin into Passivity-based Synchronization for Networked Telerobotic Systems

Xingyuan Zhou, Peter Paik, S. Farokh Atashzar

TL;DR

This work introduces TBPS2, a biomechanics-aware two-port passivity-based stabilizer for networked telerobotic systems. By incorporating the human operator's biomechanical energy margin (EoP) into both the leader and follower stabilization ports, TBPS2 recovers position synchronization while reducing stabilizer activation and maintaining minimal L2 stability under unknown delays and non-passive environments. The authors provide a mathematical synthesis and stability proof, then validate the approach through grid simulations and systematic experiments, showing superior velocity/force tracking and near-zero position drift compared to state-of-the-art TDPA-based controllers. The results suggest TBPS2 relaxes strict passivity assumptions and offers practical benefits for applications like telerehabilitation and telesurgery, where energy margins in human biomechanics can be leveraged to enhance safety and transparency in haptic teleoperation.

Abstract

Maintaining system stability and accurate position tracking is imperative in networked robotic systems, particularly for haptics-enabled human-robot interaction. Recent literature has integrated human biomechanics into the stabilizers implemented for teleoperation, enhancing force preservation while guaranteeing convergence and safety. However, position desynchronization due to imperfect communication and non-passive behaviors remains a challenge. This paper proposes a two-port biomechanics-aware passivity-based synchronizer and stabilizer, referred to as TBPS2. This stabilizer optimizes position synchronization by leveraging human biomechanics while reducing the stabilizer's conservatism in its activation. We provide the mathematical design synthesis of the stabilizer and the proof of stability. We also conducted a series of grid simulations and systematic experiments, comparing their performance with that of state-of-the-art solutions under varying time delays and environmental conditions.

Encoding Biomechanical Energy Margin into Passivity-based Synchronization for Networked Telerobotic Systems

TL;DR

This work introduces TBPS2, a biomechanics-aware two-port passivity-based stabilizer for networked telerobotic systems. By incorporating the human operator's biomechanical energy margin (EoP) into both the leader and follower stabilization ports, TBPS2 recovers position synchronization while reducing stabilizer activation and maintaining minimal L2 stability under unknown delays and non-passive environments. The authors provide a mathematical synthesis and stability proof, then validate the approach through grid simulations and systematic experiments, showing superior velocity/force tracking and near-zero position drift compared to state-of-the-art TDPA-based controllers. The results suggest TBPS2 relaxes strict passivity assumptions and offers practical benefits for applications like telerehabilitation and telesurgery, where energy margins in human biomechanics can be leveraged to enhance safety and transparency in haptic teleoperation.

Abstract

Maintaining system stability and accurate position tracking is imperative in networked robotic systems, particularly for haptics-enabled human-robot interaction. Recent literature has integrated human biomechanics into the stabilizers implemented for teleoperation, enhancing force preservation while guaranteeing convergence and safety. However, position desynchronization due to imperfect communication and non-passive behaviors remains a challenge. This paper proposes a two-port biomechanics-aware passivity-based synchronizer and stabilizer, referred to as TBPS2. This stabilizer optimizes position synchronization by leveraging human biomechanics while reducing the stabilizer's conservatism in its activation. We provide the mathematical design synthesis of the stabilizer and the proof of stability. We also conducted a series of grid simulations and systematic experiments, comparing their performance with that of state-of-the-art solutions under varying time delays and environmental conditions.

Paper Structure

This paper contains 20 sections, 52 equations, 20 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background
  3. System Description
  4. Conventional Time Domain Passivity Control
  5. Estimation of EoP
  6. Controller Design
  7. Passivity Controller Algorithms
  8. Simulation results
  9. Resistive Environment:
  10. Spearman Correlation for Velocity Tracking
  11. Position Drift
  12. Spearman Correlation for Force Tracking
  13. Demonstration of Stabilizers in a Case Simulation
  14. Assistive Environment Verification:
  15. Assistive environment: Grid Simulation
  16. ...and 5 more sections

Figures (20)

  • Figure 1: Block diagram of a Leader-Follower telerobotic architecture with telerehabilitation scenario. The virtual environment is shared between the leader and follower side, where the white square and yellow circle represent leader operator and follower operator movements, respectively.
  • Figure 2: Overall schematic of a two-channel telerobotic interconnection.
  • Figure 3: Interconnection system consisting of 'j' subsystems
  • Figure 4: TBPS2 Stabilizer architecture.
  • Figure 5: Energy flow through the Communication Channel
  • ...and 15 more figures