qRobot: A Quantum computing approach in mobile robot order picking and batching problem solver optimization

TL;DR

This paper introduces a quantum computing approach for optimizing order picking and batching in warehouse robotics, utilizing a quantum algorithm on a Raspberry Pi 4 to reduce travel distance and improve efficiency.

Contribution

It develops a quantum combinatorial optimization algorithm integrated with robotics, enabling hybrid quantum-classical processing for warehouse order picking.

Findings

Quantum algorithm reduces travel distance in simulated warehouse scenarios.

Hybrid quantum-classical processing enables scalable optimization.

Proof of concept demonstrated on Raspberry Pi 4 with cloud-based quantum resources.

Abstract

This article aims to bring quantum computing to robotics. A quantum algorithm is developed to minimize the distance travelled in warehouses and distribution centres where order picking is applied. For this, a proof of concept is proposed through a Raspberry Pi 4, generating a quantum combinatorial optimization algorithm that saves the distance travelled and the batch of orders to be made. In case of computational need, the robot will be able to parallelize part of the operations in hybrid computing (quantum + classical), accessing CPUs and QPUs distributed in a public or private cloud. Before this, we must develop a stable environment (ARM64) inside the robot (Raspberry) to run gradient operations and other quantum algorithms on IBMQ, Amazon Braket, D'wave and Pennylane locally or remotely. The proof of concept will run in such quantum environments above.