المفاهيم الأساسية
An iterative planning framework for multi-agent systems with hybrid state spaces enables continual improvement of solutions while efficiently using computational resources.
الملخص
The content introduces an iterative planning framework for multi-agent systems, focusing on energy-aware UAV-UGV cooperative task site assignments. It presents theoretical guarantees for recursive feasibility and continual solution improvement. The proposed method integrates multiple solvers to optimize plans iteratively, reducing sub-optimality.
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Introduction
- Recent advances in robotics enable complex planning problems.
- Applications include route planning for UAV-UGV teams with energy constraints.
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Motivation
- Cooperative planning tasks are NP-hard and become more complex with energy dynamics.
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Contributions
- Iterative planning framework integrating multiple solvers.
- Theoretical analysis of recursive feasibility.
- Application in energy-aware UAV-UGV cooperative task site assignments.
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Related Work
- Solutions for multi-agent planning and routing tasks exist but adapting them to generalized task site assignment is challenging.
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Preliminaries
- Definitions of transition systems, trajectories, implementations, key states, key transitions, and specifications are provided.
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Planning Problem in Transition Systems
- Formulation of the optimization problem to find a plan satisfying a given task site assignment.
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Solution Framework
- Proposal of an iterative planning framework using multiple solvers to optimize plans iteratively.
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Iterative Planning
- Algorithm 1 outlines the iterative planning process with multiple solvers and sampled transition systems.
الإحصائيات
When integrating different solvers for iterative planning, we establish theoretical guarantees for recursive feasibility.
The proposed method enables continual improvement of solutions to reduce sub-optimality while efficiently using allocated computational resources.