Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Exploiting Intrinsic Kinematic Null Space for Supernumerary Robotic Limbs Control

Tommaso Lisini Baldi, Nicole D'Aurizio, Sergio Gurgone, Daniele Borzelli, Andrea D'Avella, Domenico Prattichizzo

TL;DR

The paper tackles intuitive control of supernumerary robotic limbs by exploiting the human body's intrinsic kinematic redundancy through Intrinsic Kinematic Null Space (IKNS) control. A data-driven pipeline identifies the task-specific IKNS via kinematic chain selection, joint-velocity analysis, PCA, workspace clustering, and interpolation to map human motion to an extra DoF. Virtual experiments show IKNS can track target control signals with accuracy comparable to a gamepad, and real-world pick-and-place tasks demonstrate IKNS feasibility for daily activities, albeit with learning-related variability. The results indicate IKNS as a practical approach for SRLs, with potential to extend to more DoFs and enhance assistive capabilities for people with disabilities.

Abstract

Supernumerary robotic limbs (SRLs) gained increasing interest in the last years for their applicability as healthcare and assistive technologies. These devices can either support or augment human sensorimotor capabilities, allowing users to complete tasks that are more complex than those feasible for their natural limbs. However, for a successful coordination between natural and artificial limbs, intuitiveness of interaction and perception of autonomy are key enabling features, especially for people suffering from motor disorders and impairments. The development of suitable human-robot interfaces is thus fundamental to foster the adoption of SRLs. With this work, we describe how to control an extra degree of freedom by taking advantage of what we defined the Intrinsic Kinematic Null Space, i.e. the redundancy of the human kinematic chain involved in the ongoing task. Obtained results demonstrated that the proposed control strategy is effective for performing complex tasks with a supernumerary robotic finger, and that practice improves users' control ability.

Exploiting Intrinsic Kinematic Null Space for Supernumerary Robotic Limbs Control

TL;DR

The paper tackles intuitive control of supernumerary robotic limbs by exploiting the human body's intrinsic kinematic redundancy through Intrinsic Kinematic Null Space (IKNS) control. A data-driven pipeline identifies the task-specific IKNS via kinematic chain selection, joint-velocity analysis, PCA, workspace clustering, and interpolation to map human motion to an extra DoF. Virtual experiments show IKNS can track target control signals with accuracy comparable to a gamepad, and real-world pick-and-place tasks demonstrate IKNS feasibility for daily activities, albeit with learning-related variability. The results indicate IKNS as a practical approach for SRLs, with potential to extend to more DoFs and enhance assistive capabilities for people with disabilities.

Abstract

Supernumerary robotic limbs (SRLs) gained increasing interest in the last years for their applicability as healthcare and assistive technologies. These devices can either support or augment human sensorimotor capabilities, allowing users to complete tasks that are more complex than those feasible for their natural limbs. However, for a successful coordination between natural and artificial limbs, intuitiveness of interaction and perception of autonomy are key enabling features, especially for people suffering from motor disorders and impairments. The development of suitable human-robot interfaces is thus fundamental to foster the adoption of SRLs. With this work, we describe how to control an extra degree of freedom by taking advantage of what we defined the Intrinsic Kinematic Null Space, i.e. the redundancy of the human kinematic chain involved in the ongoing task. Obtained results demonstrated that the proposed control strategy is effective for performing complex tasks with a supernumerary robotic finger, and that practice improves users' control ability.

Paper Structure

This paper contains 18 sections, 1 equation, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Intrinsic Kinematic Null Space Control
  3. Definition
  4. Identification
  5. Kinematic chain identification
  6. Joints motion analysis
  7. Principal Component Analysis
  8. Workspace clustering
  9. Workspace interpolation
  10. Experimental Campaign
  11. Calibration
  12. Virtual Environment
  13. Metrics of interest
  14. Results
  15. Real Environment
  16. ...and 3 more sections

Figures (4)

  • Figure 1: A subject exploiting her Intrinsic Kinematic Null Space for controlling the supernumerary robotic finger.
  • Figure 2: From data collection to interpolation volume estimation in a representative trial. In (a), the trajectory depicted by the marker attached to the user's right hand. In (b), data assigned to clusters (depicted with grey spheres) and considered for the PCA. The interpolation volume is depicted in light red.
  • Figure 3: Results of the experiment conducted in virtual environment. Upper panels: desired trajectories (blue line) together with the trajectory of the pointer controlled by the user in a representative trial (red line). Lower panels: mean absolute error (expressed as percentage of Maximum Control Value) computed among all the participants for each desired trajectory. It is worth noticing that the average absolute error among all the trials is lower than 4.5 for a control value ranging from 0 to 100. Lower panels make evident that the absolute error is lower than 4 when the desired profile is smoothly changing, while reaches greater values when the user is requested to follow rapid changes.
  • Figure 4: Real Environment Experiment. Subjects were asked to pick (a), lift (b), place (c), and release (d) all the objects correctly, being as fast as possible and using only their right (impaired) arm.