Takashi Tanaka
Papers
8
Total Citations
139
H-Index
5
About
Takashi Tanaka is a robotics and autonomous systems researcher whose work spans three decades of innovation in robot motion planning, stochastic control, and uncertainty-aware navigation. He first gained significant recognition with his 2002 contributions to 3-D path planning for unmanned aerial vehicles (UAVs), where he pioneered the use of octree data structures combined with artificial potential fields to enable efficient, collision-free navigation in dynamic environments — work that has accumulated over 86 citations and remains a foundational reference in the field. More recently, Tanaka has advanced the frontier of probabilistically robust robot navigation, developing novel frameworks that account for real-world uncertainty and sensing limitations. His work on Gaussian Belief Space path planning introduces elegant geometric approaches for generating navigation paths that minimize sensing demands, while his rationally inattentive path-planning methodology — integrated with the widely-used RRT* algorithm — proposes principled metrics for planning under stochastic disturbances. His research on chance-constrained stochastic optimal control further bridges information theory and control theory using Hamilton-Jacobi-Bellman formulations. Across his career, Tanaka's contributions offer both practical algorithms and rigorous theoretical foundations, making his work essential reading for researchers tackling autonomous navigation in uncertain, real-world environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2Real-time path planning in a dynamic 3-D environment16 citations · 2002
- 3Gaussian Belief Space Path Planning for Minimum Sensing Navigation13 citations · 2022
- 4Rationally Inattentive Path-Planning via RRT10 citations · 2021
- 5
- 6
- 7Rationally Inattentive Path-Planning via RRT*2 citations · 2020
- 8Gaussian Belief Space Path Planning for Minimum Sensing Navigation2 citations · 2021