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Teleoperated Omni-directional Dual Arm Mobile Manipulation Robotic System with Shared Control for Retail Store

Rolif Lima, Somdeb Saha, Nijil George, Vismay Vakharia, Shubham Parab, Sahil Gaonkar, Vighnesh Vatsal, Kaushik Das

TL;DR

This study introduces an omni-directional dual-arm mobile robot specifically tailored for use in retail environments, and proposes a tele-operation method that enables shared control between the robot and a human operator.

Abstract

The swiftly expanding retail sector is increasingly adopting autonomous mobile robots empowered by artificial intelligence and machine learning algorithms to gain an edge in the competitive market. However, these autonomous robots encounter challenges in adapting to the dynamic nature of retail products, often struggling to operate autonomously in novel situations. In this study, we introduce an omni-directional dual-arm mobile robot specifically tailored for use in retail environments. Additionally, we propose a tele-operation method that enables shared control between the robot and a human operator. This approach utilizes a Virtual Reality (VR) motion capture system to capture the operator's commands, which are then transmitted to the robot located remotely in a retail setting. Furthermore, the robot is equipped with heterogeneous grippers on both manipulators, facilitating the handling of a wide range of items. We validate the efficacy of the proposed system through testing in a mockup of retail environment, demonstrating its ability to manipulate various commonly encountered retail items using both single and dual-arm coordinated manipulation techniques.

Teleoperated Omni-directional Dual Arm Mobile Manipulation Robotic System with Shared Control for Retail Store

TL;DR

This study introduces an omni-directional dual-arm mobile robot specifically tailored for use in retail environments, and proposes a tele-operation method that enables shared control between the robot and a human operator.

Abstract

The swiftly expanding retail sector is increasingly adopting autonomous mobile robots empowered by artificial intelligence and machine learning algorithms to gain an edge in the competitive market. However, these autonomous robots encounter challenges in adapting to the dynamic nature of retail products, often struggling to operate autonomously in novel situations. In this study, we introduce an omni-directional dual-arm mobile robot specifically tailored for use in retail environments. Additionally, we propose a tele-operation method that enables shared control between the robot and a human operator. This approach utilizes a Virtual Reality (VR) motion capture system to capture the operator's commands, which are then transmitted to the robot located remotely in a retail setting. Furthermore, the robot is equipped with heterogeneous grippers on both manipulators, facilitating the handling of a wide range of items. We validate the efficacy of the proposed system through testing in a mockup of retail environment, demonstrating its ability to manipulate various commonly encountered retail items using both single and dual-arm coordinated manipulation techniques.
Paper Structure (17 sections, 10 equations, 14 figures)

This paper contains 17 sections, 10 equations, 14 figures.

Figures (14)

  • Figure 1: (Left) Operator wearing head-mounted display and carrying two controllers. (Right) GriffinX: An in-house built omni-directional mobile robot with a dual-arm collaborative manipulators
  • Figure 2: System architecture
  • Figure 3: CAD model and the 3D printer Gripper
  • Figure 4: Schematic Diagram of 4-Mecanum Wheel Omni-directional Drive
  • Figure 5: CAD model of mock retail store
  • ...and 9 more figures