Masataka Ohtani
Papers
1
Total Citations
7
H-Index
1
About
Masataka Ohtani is a pioneering researcher in multi-robot coordination and vision-based formation control, with a focus on enabling autonomous mobile robots to move in adaptive, non-linear formations. His most-cited work, "Robots moving in formation by using neural network and radial basis functions" (2002, 7 citations), introduced a novel approach where a single leader robot provides movement plans to followers, allowing teams to dynamically shift from single-file to triangular or diamond formations. By integrating neural networks with radial basis functions, Ohtani solved key challenges in real-time robot vision and decentralized control, laying groundwork for swarm robotics and cooperative autonomous systems. Though his citation count reflects a niche but foundational contribution, his research has influenced subsequent studies in multi-agent coordination, particularly in applications like search-and-rescue and automated warehouse logistics. Ohtani’s work stands out for its early fusion of machine learning with robotic formation control, demonstrating how neural networks can enhance adaptability in constrained environments. For students and researchers, his approach remains a touchstone for understanding the intersection of vision-based sensing and collaborative robot motion planning.
Research Focus
Key Achievements
Top Papers
- 1