Energy Aware Path Planning and Guidance for Non- Holonomic Robots in a Manufacturing Workshop

Abstract

Automated Guided Vehicles (AGVs) are now a core part of modern manufacturing workshops, enabling fast, reliable, and flexible material movement while reducing dependence on manual handling. Despite their importance, most existing path planning approaches still focus mainly on minimizing distance or travel time. These strategies overlook a crucial factor, energy consumption, which directly affects operational cost, battery health, sustainability goals, and overall system efficiency. Addressing this limitation, this thesis presents an Energy Aware Path Planning (EAPP) framework specifically designed for single-load, non-holonomic AGVs used in structured workshop environments. The proposed framework models the workshop layout as an undirected graph, where nodes represent workstation points or intersections and edges represent feasible routes that comply with AGV kinematic and turning constraints. Unlike traditional planners, our approach integrates a physics-based energy model into the A* algorithm, allowing each edge to be evaluated not only by its geometric length but also by its expected energy usage, considering acceleration, deceleration, rolling resistance, turning angles, and standby power. This enables the AGV to prioritize paths with fewer turns, even when they are slightly longer, ultimately reducing total energy consumption. The method is implemented and validated using MATLAB simulations, where both distance-based A* and the proposed energy-aware A* are compared under realistic AGV parameters. The results show that the EAPP framework significantly lowers energy consumption and maintains competitive travel times, while still producing feasible and safe navigation paths within constrained workshop networks. By directly embedding energy considerations into the path planning process, this work contributes to more sustainable intralogistics and provides a practical, scalable solution suitable for real-time AGV navigation and industrial deployment.