# Control of automated guided vehicles without collision by quantum annealer and digital devices

arXiv: https://arxiv.org/abs/1812.01532 · HTML: https://mlsanity.com/paper/1812.01532/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2018.

## Built on by (most cited first, 13 of 13)

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- [Probabilistic Sampling of Balanced K-Means using Adiabatic Quantum Computing](https://mlsanity.com/paper/2310.12153/) (2023)
- [Assessment of image generation by quantum annealer](https://mlsanity.com/paper/2103.08373/) (2021)
- [Enhancing In-vehicle Multiple Object Tracking Systems with Embeddable Ising Machines](https://mlsanity.com/paper/2410.14093/) (2024)
- [Relaxation-assisted reverse annealing on nonnegative/binary matrix factorization](https://mlsanity.com/paper/2501.02114/) (2025)
- [Solving Large Break Minimization Problems in a Mirrored Double Round-robin Tournament Using Quantum Annealing](https://mlsanity.com/paper/2110.07239/) (2021)
- [Computing Graph Edit Distance with Algorithms on Quantum Devices](https://mlsanity.com/paper/2111.10183/) (2021)
- [Efficient correlation-based discretization of continuous variables for annealing machines](https://mlsanity.com/paper/2301.07244/) (2023)
- [L0 Regularization of Field-Aware Factorization Machine through Ising Model](https://mlsanity.com/paper/2403.01718/) (2024)
- [Data-Driven Discrete Geofence Design Using Binary Quadratic Programming](https://mlsanity.com/paper/2509.24679/) (2025)

## Abstract (arXiv)

We formulate an optimization problem to control a large number of automated guided vehicles in a plant without collision. The formulation consists of binary variables. A quadratic cost function over these variables enables us to utilize certain solvers on digital computers and recently developed purpose-specific hardware such as D-Wave 2000Q and the Fujitsu digital annealer. In the present study, we consider an actual plant in Japan, in which vehicles run, and assess efficiency of our formulation for optimizing the vehicles via several solvers. We confirm that our formulation can be a powerful approach for performing smooth control while avoiding collisions between vehicles, as compared to a conventional method. In addition, comparative experiments performed using several solvers reveal that D-Wave 2000Q can be useful as a rapid solver for generating a plan for controlling the vehicles in a short time although it deals only with a small number of vehicles, while a digital computer can rapidly solve the corresponding optimization problem even with a large number of binary variables.