# Coordinated Multi-Drone Last-mile Delivery: Learning Strategies for Energy-aware and Timely Operations

arXiv: https://arxiv.org/abs/2509.15830 · HTML: https://mlsanity.com/paper/2509.15830/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 7 of its 43 references that are in the map.
- First posted on arXiv 19 Sep 2025.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 7 of 7)

- [Vehicle Routing Problems for Drone Delivery](https://mlsanity.com/paper/1608.02305/) (2016)
- [Logistics in the Sky: A Two-phase Optimization Approach for the Drone Package Pickup and Delivery System](https://mlsanity.com/paper/2204.01335/) (2022)
- [Locating and Platforms and Scheduling a Fleet of Drones for Emergency Delivery of Perishable Items](https://mlsanity.com/paper/2107.13672/) (2021)
- [Zero-Emission Delivery for Logistics and Transportation: Challenges, Research Issues, and Opportunities](https://mlsanity.com/paper/2205.15606/) (2022)
- [3, 2, 1, Drones Go! A Testbed to Take off UAV Swarm Intelligence for Distributed Sensing](https://mlsanity.com/paper/2208.05914/) (2022)
- [Large-scale Package Deliveries with Unmanned Aerial Vehicles using Collective Learning](https://mlsanity.com/paper/2311.13489/) (2023)
- [M-SET: Multi-Drone Swarm Intelligence Experimentation with Collision Avoidance Realism](https://mlsanity.com/paper/2406.10916/) (2024)

## Abstract (arXiv)

Drones have recently emerged as a faster, safer, and cost-efficient way for last-mile deliveries of parcels, particularly for urgent medical deliveries highlighted during the pandemic. This paper addresses a new challenge of multi-parcel delivery with a swarm of energy-aware drones, accounting for time-sensitive customer requirements. Each drone plans an optimal multi-parcel route within its battery-restricted flight range to minimize delivery delays and reduce energy consumption. The problem is tackled by decomposing it into three sub-problems: (1) optimizing depot locations and service areas using K-means clustering; (2) determining the optimal flight range for drones through reinforcement learning; and (3) planning and selecting multi-parcel delivery routes via a new optimized plan selection approach. To integrate these solutions and enhance long-term efficiency, we propose a novel algorithm leveraging actor-critic-based multi-agent deep reinforcement learning. Extensive experimentation using realistic delivery datasets demonstrate an exceptional performance of the proposed algorithm. We provide new insights into economic efficiency (minimize energy consumption), rapid operations (reduce delivery delays and overall execution time), and strategic guidance on depot deployment for practical logistics applications.