Papers
10
Total Citations
139
H-Index
7
About
Ingo Ahrns is a leading researcher in autonomous robotic systems for extreme environments, with a career spanning space exploration, deep-sea robotics, and intelligent control. His work focuses on spacecraft pose estimation, multi-robot cooperation, and neural network-based perception. Ahrns made significant contributions to uncooperative spacecraft rendezvous, developing CNN-based monocular pose estimation systems validated on ground testbeds—his 2022 paper on this topic has garnered 43 citations. He also pioneered adaptive neural network Kalman filters for robust pose estimation in space (14 citations). Earlier, Ahrns led the LUNARES project, demonstrating heterogeneous reconfigurable robots for lunar crater exploration and sample return (30 citations). His foundational work includes space-variant dynamic neural fields for visual attention (11 citations) and neural fuzzy control for collision avoidance. Notably, he contributed to the PERIOD project on in-space manufacturing, assembly, and refueling technologies (7 citations), and developed the MANSIO-VIATOR deep-sea crawler system (8 citations), highlighting his cross-domain expertise. With over 140 total citations, Ahrns’ research bridges simulation and real-world deployment, advancing autonomous operations in space and deep sea.
Research Focus
Key Achievements
Top Papers
- 1
- 2LUNARES: lunar crater exploration with heterogeneous multi robot systems30 citations · 2010
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- 5Space-variant dynamic neural fields for visual attention11 citations · 2003
- 6A New Deep-Sea Crawler System - MANSIO-VIATOR8 citations · 2018
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- 8
- 9Neural Fuzzy Techniques in Sonar-Based Collision Avoidance4 citations · 1998
- 10Cooperative Docking Procedures for a Lunar Mission2 citations · 2010