About

Robert Fitch is a robotics researcher whose work spans multi-robot systems, active perception, field robotics, and autonomous planning. His most significant contributions include the development of Dec-MCTS, a decentralized Monte Carlo tree search algorithm enabling multi-robot active perception that has garnered 186 citations, and an Active SLAM framework combining model predictive control with area coverage and uncertainty reduction (118 citations). Fitch has made notable strides in agricultural robotics, with his vision-based obstacle detection and navigation system for broad-acre farming attracting 152 citations, demonstrating real-world impact beyond the laboratory. His pioneering work in robotic ecology — using autonomous aerial vehicles to track radio-tagged wildlife — bridges robotics and conservation science, with related papers earning 89 and 79 citations respectively. Earlier in his career, Fitch established foundational contributions in self-reconfiguring modular robots, addressing locomotion scalability, distributed control, and heterogeneous reconfiguration planning. His research consistently addresses the challenge of making autonomous robots practical and intelligent in complex, dynamic environments, blending algorithmic innovation with meaningful real-world deployment across ecology, agriculture, and multi-agent coordination.

Research Focus

Key Achievements

25
H-Index
86
Papers
2,102
Total Citations
24
Avg Citations/Paper
🏆 Most Cited Paper
Dec-MCTS: Decentralized planning for multi-robot active perception
186 citations · 2018
📈 Most Prolific Year: 2020 (9 Papers)
🤝 Key Collaborators: 117
🏛 Institutions: University of Technology Sydney, The University of Sydney, Australian Centre for Robotic Vision, Dartmouth College, Wright-Patterson Air Force Base, Dartmouth Hospital

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 42 days ago