Aidan F. Browne
Papers
6
Total Citations
45
H-Index
4
About
Aidan F. Browne is a researcher whose work sits at the intersection of robotics education, autonomous navigation, and sensor-based localization. His primary contributions focus on making autonomous robot control accessible to multidisciplinary student teams, as demonstrated in his most cited work, “A Versatile Approach for Teaching Autonomous Robot Control” (17 citations). Browne has advanced practical localization techniques for challenging environments, from extraterrestrial terrains to indoor spaces where GPS is unavailable. His 2017 paper on vector-based obstacle avoidance using LIDAR and mecanum drive (15 citations) showcases a key innovation: pairing omni-directional drive systems with affordable LIDAR sensors to simplify navigation. He has also explored particle filter approaches for wireless signal-based localization (4 citations) and developed a novel laser localization system through mathematical modeling. Browne’s work on a breadcrumb system for outdoor robots addresses persistent GPS accuracy limitations. His research is notable for its hands-on, educational focus—bridging theory and application in autonomous systems, with an emphasis on cost-effective, scalable solutions for real-world robotics challenges.
Research Focus
Key Achievements
Top Papers
- 1
- 2Vector-based robot obstacle avoidance using LIDAR and mecanum drive15 citations · 2017
- 3A survey on robot localization in extraterrestrial environments4 citations · 2016
- 4
- 5Modeling a novel laser localization system3 citations · 2015
- 6