How can I optimize drone delivery using decentralized AI?
Ready, Bid, Go! On-Demand Delivery Using Fleets of Drones with Unknown, Heterogeneous Energy Storage Constraints
April 14, 2025
https://arxiv.org/pdf/2504.08585This paper explores decentralized control of a drone delivery fleet with unknown and varied battery health. Drones bid on delivery tasks based on their current charge, parcel weight, and delivery distance, learning over time which tasks they can realistically complete. Surprisingly, prioritizing "least confident" bidders (those closest to their capability limits) improves overall efficiency. The research also introduces a forecasting element where drones can reserve future deliveries, improving early order prioritization.
Key points for LLM-based multi-agent systems:
- Decentralized control and bidding: Individual LLMs could manage each drone, bidding autonomously on tasks and refining their bidding strategies through experience.
- Capability learning: LLMs can dynamically learn their limitations (analogous to battery health) related to task complexity, processing time, or resource constraints.
- Forecasting: LLMs can predict future capabilities and reserve tasks accordingly, enhancing scheduling and resource allocation in multi-agent scenarios.
- Heterogeneity: This research underscores the importance of handling agents with different capabilities, which is a common challenge in LLM-based systems.