Extended Reality for Enhanced Human-Robot Collaboration: A Human-in-the-Loop Approach

מושגי ליבה

Enhancing human-robot collaboration through extended reality and machine learning.

תקציר

The content explores the integration of extended reality (XR) and machine learning in human-robot collaboration. It proposes a framework for an autonomous manipulator that incorporates human-in-the-loop principles using XR technology. The paper discusses the challenges, technologies, and future outlook of XR in industrial human-robot interaction.

I. Introduction

• Transition from Industry 4.0 to Industry 5.0.
• Challenges in designing communication interfaces.
• Importance of combining robot precision with human intelligence.

II. Framework Conceptualization

A. Manipulator Task Generalization:

• Overview of ML-based manipulator autonomy.
  B. Human-in-the-Loop Component:
• Immersive demonstration for task illustration.
  C. Key Technologies:
• Extended Reality, Digital Twin, AI, Cloud Computing, Edge Computing.

III. Applications Review

A. Operator Support and Communication:

• Use of XR for operator support and safety.
  B. Safety Considerations:
• AR-based safety zones and obstacle detection.
  C. Teleoperation:
• MR interfaces for multi-arm systems.
  D. Robot Programming:
• AR-based trajectory modification and skill maintenance.

IV. Discussion

A. Mitigating Risks:

• Addressing physical and mental health concerns.
  B. Immersiveness:
• Enhancing task coordination through immersive environments.
  C. User-oriented Concepts:
• Considering social cues in human-machine communication.
  D. From Lab to Industry:
• Challenges in XR integration in industrial settings.

V. Conclusions

The study highlights the potential of XR in enhancing human-to-robot interaction within industrial contexts, emphasizing the importance of immersive technology for improved collaboration.

סטטיסטיקה

arXiv:2403.14597v1 [cs.RO] 21 Mar 2024

ציטוטים

"XR technologies offer a range of human-interaction interfaces tailored for both digital and physical environments."
"The goal is to conceptualize approaches for human involvement with autonomous ML-based manipulators."
"XR could serve as the foundational pillar for the future of autonomous robotics in Industry 5.0."

תובנות מפתח מזוקקות מ:

Extended Reality for Enhanced Human-Robot Collaboration

by Yehor Karpic... ב- arxiv.org 03-22-2024

https://arxiv.org/pdf/2403.14597.pdf

שאלות מעמיקות

How can XR address the challenges related to physical and mental health concerns when used in HRI tasks?

Extended Reality (XR) offers solutions to mitigate physical and mental health concerns in Human-Robot Interaction (HRI) tasks through various means:

Ergonomics: XR systems can be designed with ergonomic considerations to reduce muscle fatigue, visual fatigue, and other physical strains associated with prolonged use.
Training: Providing proper training on using XR devices can help users avoid issues like motion sickness or cybersickness, improving their overall experience.
Simulation: By creating virtual environments for training or task execution, XR eliminates risks of physical injury that may occur during real-world interactions with robots.
Feedback Mechanisms: Incorporating feedback mechanisms into XR interfaces can alert users about potential dangers or hazards in the environment, enhancing safety awareness.

How can XR interfaces be standardized across multiple manipulators while allowing customization for specific characteristics?

Standardizing XR interfaces across multiple manipulators involves defining common design principles and interaction patterns that are consistent across different systems:

Common Elements: Establishing a set of standard elements such as menus, buttons, gestures, or voice commands that are universally understood by operators interacting with various manipulators.
Modular Design: Creating modular components within the interface architecture allows for customization based on specific manipulator characteristics without compromising overall consistency.
User Profiles: Implementing user profiles within the XR system enables personalized settings for individual operators while maintaining a standardized interface layout for general usability.
API Integration: Developing Application Programming Interfaces (APIs) that facilitate communication between the XR interface and different manipulator systems ensures seamless integration while accommodating unique features.

What are the implications of transferring developed methodologies from lab experiments to real-world industrial settings?

Transferring developed methodologies from lab experiments to real-world industrial settings entails several implications:

Validation: The need for validation studies in actual industrial environments to ensure that the methodologies perform effectively under practical conditions.
Scalability: Adapting lab-developed techniques to large-scale operations may require adjustments in scalability factors such as processing power, data handling capabilities, and network infrastructure.
Integration: Seamless integration of new methodologies into existing industrial workflows without disrupting productivity is crucial for successful implementation.
Maintenance: Considering long-term maintenance requirements and sustainability aspects when transitioning from controlled lab setups to dynamic industrial contexts is essential for continued success.