Optimal Path Planning and Cost Minimization for a Drone Delivery System Via Model Predictive Control
arXiv:2503.19699
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 -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.
15 pages, 5 figures, Submitted to the 2025 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications