Papers
55
Total Citations
1,247
H-Index
20
About
Matthew Garratt is a prominent researcher whose work spans autonomous robotics, unmanned aerial vehicles (UAVs), and multi-agent systems, with a particular emphasis on perception, navigation, and intelligent control. His highly cited 2022 survey on monocular depth estimation (160 citations) has become an essential reference for researchers tackling real-time scene understanding in autonomous systems, while his 2017 review of intelligent flight control systems (139 citations) established a comprehensive benchmark for adaptive autopilot design in small UAVs. Garratt's contributions to visual-inertial navigation (94 citations) have advanced sensor fusion techniques critical for micro aerial vehicles operating in GPS-denied environments. His work also extends to bio-inspired guidance, drawing on insect vision to develop robust navigation strategies for both ground and airborne platforms. More recently, Garratt has made significant strides in swarm robotics and multi-robot coordination, developing frontier-led swarming algorithms for dynamic environment coverage and fuzzy self-tuning formation controllers resilient to real-world disturbances like wind gusts. Across his career, his research has garnered hundreds of citations, cementing his influence across computer vision, aerial robotics, and autonomous systems communities worldwide.
Research Focus
Key Achievements
Top Papers
- 1Towards Real-Time Monocular Depth Estimation for Robotics: A Survey160 citations · 2022
- 2State-of-the-Art Intelligent Flight Control Systems in Unmanned Aerial Vehicles139 citations · 2017
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- 7Swarm Robotics: A Survey from a Multi-Tasking Perspective50 citations · 2023
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- 10Frontier-led swarming: Robust multi-robot coverage of unknown environments33 citations · 2022