Papers
6
Total Citations
611
H-Index
5
About
Nathan Michael is a leading roboticist whose research focuses on the distributed coordination, control, and autonomy of multi-robot systems, with a particular emphasis on aerial and ground robots. His major contributions lie in developing vision-based control laws for motion coordination, formation control, and cooperative manipulation. His seminal 2009 paper on "Vision-Based, Distributed Control Laws for Motion Coordination of Nonholonomic Robots" has garnered 228 citations, while his work on "Vision-Based Localization for Leader–Follower Formation Control" has been cited 181 times, establishing foundational methods for decentralized robot teams. Michael also pioneered the use of interpolated implicit functions for controlling robot swarms (98 citations) and addressed the challenging problem of cooperative manipulation and transportation with aerial robots (56 citations). His innovative approach to multi-robot caging and manipulation through composition of vector fields (43 citations) further demonstrates his impact. Michael's work on persistent surveillance with teams of micro aerial vehicles (MAVs) showcases his commitment to real-world applications. His research has profoundly influenced the fields of swarm robotics, distributed control, and aerial manipulation, making him a key figure in advancing autonomous multi-agent systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Vision-Based Localization for Leader–Follower Formation Control181 citations · 2009
- 3Controlling Swarms of Robots Using Interpolated Implicit Functions98 citations · 2006
- 4Cooperative manipulation and transportation with aerial robots56 citations · 2009
- 5Composition of Vector Fields for Multi-Robot Manipulation via Caging43 citations · 2007
- 6Persistent surveillance with a team of MAVs5 citations · 2011