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

arXiv: https://arxiv.org/abs/2503.19699 · HTML: https://mlsanity.com/paper/2503.19699/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 24 references that are in the map.
- First posted on arXiv 25 Mar 2025.

## Builds on (most cited first, 1 of 1)

- [Model Predictive Control for Micro Aerial Vehicles: A Survey](https://mlsanity.com/paper/2011.11104/) (2020)

## Abstract (arXiv)

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.