# Iterative Planning for Multi-agent Systems: An Application in Energy-Aware UAV-UGV Cooperative Task Site Assignments

arXiv: https://arxiv.org/abs/2401.08846 · HTML: https://mlsanity.com/paper/2401.08846/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2024.

## Built on by (most cited first, 1 of 1)

- [Knowledge Graph-Guided Multi-Agent Distillation for Reliable Industrial Question Answering with Datasets](https://mlsanity.com/paper/2510.06240/) (2025)

## Abstract (arXiv)

This paper presents an iterative planning framework for multi-agent systems with hybrid state spaces. The framework uses transition systems to mathematically represent planning tasks and employs multiple solvers to iteratively improve the plan until computation resources are exhausted. When integrating different solvers for iterative planning, we establish theoretical guarantees on the mathematical framework to ensure recursive feasibility. The proposed framework enables continual improvement of solution optimality, efficiently using allocated computation resources. The proposed method is validated by applying it to an energy-aware UGV-UAV cooperative task site assignment. The results demonstrate the continual solution improvement while preserving real-time implementation ability compared to algorithms proposed in the literature.