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Multi-Robot Assembly Strategies and Metrics.

Jeremy A Marvel1, Roger Bostelman1, Joe Falco1

  • 1U.S. National Institute of Standards and Technology.

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Summary
This summary is machine-generated.

This survey explores multi-robot assembly for industry, focusing on fixtureless methods using robots and automated vehicles. It details assembly types, synchronization algorithms, and performance metrics for enhanced robotic collaboration.

Keywords:
Applied computing → Computer-aided manufacturingComputer systems organization → RoboticsComputing methodologies → Motion path planningCooperation and coordinationRobot assemblyassembly performance metricsmanufacturing roboticsmulti-robot coordination

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Area of Science:

  • Robotics and Automation
  • Industrial Engineering

Background:

  • Industrial assembly increasingly requires sophisticated automation.
  • Traditional assembly methods face limitations in flexibility and precision.
  • Multi-robot systems offer potential for advanced assembly solutions.

Purpose of the Study:

  • To survey multi-robot assembly applications and methods for industrial use.
  • To identify trends and insights in fixtureless multi-robot assembly.
  • To analyze robotic systems, synchronization algorithms, and performance metrics.

Main Methods:

  • Literature review of multi-robot assembly systems.
  • Focus on fixtureless assembly strategies.
  • Categorization of robotic systems (arms, hands, mobile platforms).
  • Analysis of motion synchronization algorithms.
  • Identification of quality and performance assessment metrics.

Main Results:

  • Overview of assembly types enabled by multi-robot collaboration.
  • Description of algorithms for synchronizing multiple robots in assembly.
  • Presentation of metrics for evaluating assembly quality and performance.
  • Identification of key trends in industrial multi-robot assembly.

Conclusions:

  • Multi-robot systems, particularly fixtureless approaches, are crucial for advanced industrial assembly.
  • Effective synchronization algorithms and robust performance metrics are essential for successful implementation.
  • This survey provides a foundational understanding for future research and development in the field.