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About this paper
Hoang-Dung Bui
Hoang-Dung Bui's survey presents a comprehensive overview of Multi-Robot Motion Planning (MRMP), introducing a new taxonomy to classify problems and algorithms based on robot models, environment types, communication, and planner types, while also outlining current challenges and future research directions.
Key insights
- The complexity of Multi-Robot Motion Planning (MRMP) heavily depends on factors like robot models (points vs. dynamic geometry), environment knowledge (known vs. unknown), and communication paradigms (centralized vs. decentralized).
- A fundamental trade-off exists between optimality, completeness, scalability, and efficiency across various MRMP algorithmic approaches (search-based, sampling-based, machine learning-guided).
- Current algorithms often prioritize either global optimality (centralized) or scalability and robustness (decentralized), with significant open challenges remaining in real-time adaptability, heterogeneity, and unknown environments.
Summary by alphaXiv.
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