Angular and Lateral Acceleration Characterisation of MasterMover Vehicular Dynamics in Tesco Culverhouse Warehouse, UK

¹ Department of Engineering, Manchester Metropolitan University, M1 5GD Manchester, U.K.
² Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa.
³ Center for Future Technologies, University of Chichester, Bognor Regis, PO21 1HR, U.K.
⁴ Imperial College London, 2 South Kensington Campus, Exhibition Road, London, SW7 2AZ, UK

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

Autonomous Mobile Robots (AMRs) are increasingly deployed in large retail warehouses, including those operated by Tesco, Aldi, and Lidl, to improve material handling and replenishment efficiency. However, dense warehouse environments characterised by narrow aisles, mixed payload conditions, and frequent interaction with personnel and equipment present significant challenges for stable and efficient autonomous guided vehicles (AGV) operation. This paper presents a Connected Autonomous MasterMover (CAM) framework for coordinated control of coupled angular and lateral dynamics in warehouse AGV systems. We integrated a kinematic vehicle model with a coordinated motion control architecture to improve trajectory tracking, steering stability, and manoeuvring performance under constrained warehouse conditions. Unlike conventional decoupled control systems, the CAM enables coordinated motion regulation and adaptive response during complex navigation tasks. Results demonstrate that the proposed system improves trajectory-tracking accuracy by up to 43%, enhances angular-response smoothness by up to 41%, reduces lateral oscillations by up to 52%, and increases stability margins by up to 39% under high-complexity operating conditions. These improvements result in smoother motion behaviour, enhanced operational stability, and improved navigation reliability in dense warehouse environments. Overall, the CAM provides a scalable and adaptive solution for intelligent intralogistics systems, supporting safer, more efficient, and coordinated autonomous material handling in smart logistics infrastructure.