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arXiv · 2503.19699

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

Muhammad Al-Zafar Khan·Jamal Al-Karaki
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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.

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Muhammad Al-Zafar Khan, Jamal Al-Karaki. 2025-03-25. Optimal Path Planning and Cost Minimization for a Drone Delivery System Via Model Predictive Control. https://arxiv.org/abs/2503.19699

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