Optimal Path Planning and Cost Minimization for a Drone Delivery System Via Model Predictive Control

Contributing authors: Jamal.Al-Karaki@zu.ac.ae; Abstract In this study, we formulate the drone delivery problem as a control problem and solve it using Model Predictive Control. Two experiments are performed: The first is on a less challenging grid world environment with lower dimensionality, and the second is with a higher dimensionality and added complexity. The MPC method was benchmarked against three popular Multi-Agent Reinforcement Learning (MARL): Independent Q -Learning (IQL), Joint Action Learners (JAL), and Value-Decomposition Networks (VDN). It was shown that the MPC method solved the problem quicker and required fewer optimal numbers of drones to achieve a minimized cost and navigate the optimal path. Keywords: Model Predictive Control (MPC), Drone Delivery System, Applications of Multi-Agent Reinforcement Learning (MARL) 1 Introduction The rapid evolution of e-commerce and the increasing demand for faster, more efficient delivery systems have ushered in a new era in logistics and the shopping experience, which has huge effects on traditional brick-and-mortar shopping centers and malls that have globally reported a decrease in walk-in retail customers since the COVID-19 1 arXiv:2503.19699v1