Papers
31
Total Citations
851
H-Index
16
About
Graeme Best is a robotics researcher whose work spans multi-robot coordination, active perception, and autonomous planning. He is perhaps best known for developing Dec-MCTS (Decentralized Monte Carlo Tree Search), a landmark algorithm enabling teams of robots to independently optimize their actions while reasoning over joint-action spaces — a contribution that has garnered over 186 citations and spawned multiple follow-on works including planning-aware communication strategies and region-of-interest reconstruction systems. His research consistently tackles the challenge of making robot teams smarter and more efficient, from coordinating heterogeneous marine vehicles to autonomously detect and track ocean fronts, to leading resilient subterranean exploration missions with mixed aerial and ground robot teams. Best has also contributed foundational work in Bayesian intent inference for trajectory prediction and self-organizing map approaches to multi-robot path planning, demonstrating a breadth that bridges probabilistic reasoning, field robotics, and real-world deployment. With over 600 citations across his most recognized publications and successful demonstrations in demanding environments — underground, underwater, and in agriculture — Best has established himself as a significant voice in the field of autonomous multi-robot systems.
Research Focus
Key Achievements
Top Papers
- 1Dec-MCTS: Decentralized planning for multi-robot active perception186 citations · 2018
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- 3Online planning for multi-robot active perception with self-organising maps58 citations · 2017
- 4Planning-Aware Communication for Decentralised Multi-Robot Coordination56 citations · 2018
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- 8Multi-Robot Region-of-Interest Reconstruction with Dec-MCTS37 citations · 2019
- 9Decentralised Monte Carlo Tree Search for Active Perception34 citations · 2020
- 10Terrain classification using a hexapod robot32 citations · 2013