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An End-to-End Computationally Lightweight Vision-Based Grasping System for Grocery Items.

Thanavin Mansakul1, Gilbert Tang1, Phil Webb1

  • 1Centre for Robotics and Assembly, Faculty of Engineering and Applied Sciences, Cranfield University, Bedford MK43 0AL, UK.

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Summary
This summary is machine-generated.

This study introduces an efficient vision-based grasping framework for mobile manipulators. The system achieves fast and accurate grasp detection, enabling practical robotic applications with high success rates.

Keywords:
end-to-end grasp detectionlightweight computationmachine visionmobile manipulatorobject detectionobject pose estimationvision-based grasping system

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Area of Science:

  • Robotics
  • Computer Vision
  • Machine Learning

Background:

  • Vision-based grasping for mobile manipulators faces challenges in perception, efficiency, and deployment.
  • Existing methods often lack computational efficiency for real-time applications.

Purpose of the Study:

  • To develop a computationally lightweight, end-to-end grasp detection framework for mobile manipulators.
  • To enable accurate manipulation by mapping image coordinates to the robot frame.

Main Methods:

  • Integrated object detection, pose estimation, and grasp point prediction.
  • Developed a coordinate transformation model from image to robot frame.
  • Created a benchmark and dataset for pick-and-pack grocery tasks.

Main Results:

  • Achieved average execution time under 5 seconds on an edge device.
  • Demonstrated 100% success rate on Level 1 and 96% on Level 2 of the benchmark.
  • Reported an average compute-to-speed ratio of 0.0130, indicating energy efficiency.

Conclusions:

  • The proposed framework is a practical, robust, and efficient solution for vision-based grasping.
  • It is suitable for lightweight robotic applications in real-world environments.
  • The system addresses key challenges in mobile manipulator perception and efficiency.