Tenda Okimoto
Papers
2
Total Citations
29
H-Index
2
About
Tenda Okimoto is a leading researcher in distributed artificial intelligence and multi-agent systems, with a particular focus on collision avoidance algorithms and robust team formation. Her most impactful work, the Distributed Stochastic Search Algorithm (DSSA+), addresses the critical challenge of ship collision avoidance in complex maritime environments. This algorithm allows vessels to dynamically coordinate both course and speed changes to minimize collision risk, achieving 21 citations and establishing itself as a state-of-the-art solution in the field. Okimoto’s research extends to multi-robot systems, where she developed mission-oriented robust multi-team formation strategies for robot rescue simulation, demonstrating her commitment to real-world applications in emergency response. Her work is notable for its practical impact on autonomous navigation and cooperative robotics, bridging theoretical optimization with tangible safety improvements. With contributions that directly enhance the efficiency and reliability of distributed decision-making, Okimoto continues to influence how autonomous agents collaborate in high-stakes, dynamic environments.
Research Focus
Key Achievements
Top Papers
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- 2