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

TL;DR

This paper introduces an iterative planning framework for multi-agent systems with hybrid state spaces, focusing on energy-aware UAV-UGV task assignments, ensuring continual solution improvement with real-time capabilities.

Contribution

It presents a novel iterative planning approach with theoretical guarantees for multi-agent systems, enabling continual solution enhancement while maintaining real-time performance.

Findings

Demonstrates continual solution improvement in energy-aware UAV-UGV task assignment.

Ensures recursive feasibility with theoretical guarantees.

Achieves real-time implementation compared to existing algorithms.

Abstract

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.