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This paper presents an iterative planning framework for multi-agent systemswith hybrid state spaces. The framework uses transition systems tomathematically represent planning tasks and employs multiple solvers toiteratively improve the plan until computation resources are exhausted. Whenintegrating different solvers for iterative planning, we establish theoreticalguarantees on the mathematical framework to ensure recursive feasibility. Theproposed framework enables continual improvement of solution optimality,efficiently using allocated computation resources. The proposed method isvalidated by applying it to an energy-aware UGV-UAV cooperative task siteassignment. The results demonstrate the continual solution improvement whilepreserving real-time implementation ability compared to algorithms proposed inthe literature.

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