Takahito Ishiwata
Papers
2
Total Citations
16
H-Index
2
About
Takahito Ishiwata’s research centers on autonomous mobile robotics, with a particular focus on probabilistic surveillance and patrolling in unknown indoor environments. His key contributions lie in developing Bayesian learning frameworks that enable single robots to intelligently allocate their monitoring efforts without prior knowledge of intruder locations or numbers. In his most cited works—each garnering 8 citations—Ishiwata addresses the fundamental challenge of maximizing intruder detection rates when a robot must patrol multiple rooms under uncertainty. His 2015 paper on Bayesian learning for multiple intruders introduced a method for the robot to dynamically update its belief about where intruders are likely to appear, thereby improving patrol efficiency over time. Similarly, his work on probabilistic surveillance formalized how a mobile robot can balance exploration and exploitation to detect unknown intruders. These contributions are notable for advancing practical decision-making algorithms in robotics, bridging the gap between theoretical probability models and real-world autonomous navigation. Ishiwata’s research is particularly valuable for students and engineers working on security robotics, autonomous inspection, and adaptive sensor planning in unstructured environments.
Research Focus
Key Achievements
Top Papers
- 1Patrolling robot based on Bayesian learning for multiple intruders8 citations · 2015
- 2Probabilistic surveillance by mobile robot for unknown intruders8 citations · 2015