Optimal Path Planning and Cost Minimization for a Drone Delivery System Via Model Predictive Control
This addresses cost and efficiency optimization for drone delivery systems, but it appears incremental as it applies an existing control method to a specific domain.
The study tackled drone delivery path planning by formulating it as a control problem and solving it with Model Predictive Control (MPC), showing that MPC solved the problem quicker and required fewer drones to minimize costs compared to Multi-Agent Reinforcement Learning methods.
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.