TacMMs: Tactile Mobile Manipulators for Warehouse Automation

TL;DR

This paper introduces Tactile Mobile Manipulators (TacMMs), integrating tactile sensors with mobile robots to improve cooperative load-lifting efficiency in warehouse automation, outperforming vision-based methods especially with uneven loads.

Contribution

The paper presents a novel multi-robot system with tactile sensing for enhanced physical interaction and load handling in warehouse tasks, demonstrating significant performance improvements.

Findings

TacMMs achieved a 66% success rate, outperforming the 34% of vision-based methods.

Tactile perception benefits increase with uneven load distribution.

The approach shows promise for scaling to multiple robots in warehouse automation.

Abstract

Multi-robot platforms are playing an increasingly important role in warehouse automation for efficient goods transport. This paper proposes a novel customization of a multi-robot system, called Tactile Mobile Manipulators (TacMMs). Each TacMM integrates a soft optical tactile sensor and a mobile robot with a load-lifting mechanism, enabling cooperative transportation in tasks requiring coordinated physical interaction. More specifically, we mount the TacTip (biomimetic optical tactile sensor) on the Distributed Organisation and Transport System (DOTS) mobile robot. The tactile information then helps the mobile robots adjust the relative robot-object pose, thereby increasing the efficiency of load-lifting tasks. This study compares the performance of using two TacMMs with tactile perception with traditional vision-based pose adjustment for load-lifting. The results show that the average success rate of the TacMMs (66%) is improved over a purely visual-based method (34%), with a larger improvement when the mass of the load was non-uniformly distributed. Although this initial study considers two TacMMs, we expect the benefits of tactile perception to extend to multiple mobile robots. Website: https://sites.google.com/view/tacmms