Towards Autonomous Retail Stocking and Picking: Methods Enabling Robust Vacuum-Based Robotic Manipulation in Densely...

Area of Science:

• Robotics and Artificial Intelligence
• Automation in Retail Environments

Background:

• Increasing demand for automation in retail due to labor shortages and efficiency needs.
• Challenges in robotic manipulation within densely packed retail shelves with diverse object shapes.
• Limitations of current vacuum-based grasping technologies in adapting to various object geometries.

Purpose of the Study:

• To propose a framework for robotic grasping in retail settings.
• To develop an adaptive vacuum-based grasping solution for retail environments.
• To introduce a novel evaluation metric, grasp shear force resilience, for assessing robotic grasp stability.

Main Methods:

• Development of an adaptive vacuum-based grasping system.
• Introduction and application of the grasp shear force resilience metric.
• Testing the system's performance across a range of retail object shapes and sizes.

Main Results:

• Adaptive suction cups demonstrated successful handling of diverse object shapes and sizes.
• The proposed solution showed superior adaptability compared to some commercial alternatives.
• The grasp shear force resilience metric provided insights into object behavior under manipulation stresses.

Conclusions:

• Adaptive vacuum grasping is effective for retail automation challenges.
• The grasp shear force resilience metric offers valuable data for optimizing robotic grasping.
• The developed framework and solution enhance robotic picking and restocking capabilities in retail.