Optimal Path Planning and Cost Minimization for a Drone Delivery System Via Model Predictive Control
Year: 2025
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Khan, Muhammad Al-Zafar, Al-Karaki, Jamal
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6947858605
Cites:
Cited by:
Related to: