Takashi Kusaka
Papers
4
Total Citations
27
H-Index
3
About
Takashi Kusaka’s research lies at the intersection of computational efficiency, control engineering, and sensor fusion, with a focus on making complex multi-degree-of-freedom (DoF) systems more tractable for real-world applications. His most cited work, “Fast and Accurate Approximation Methods for Trigonometric and Arctangent Calculations for Low-Performance Computers” (2022, 11 citations), addresses a critical bottleneck in signal processing by enabling high-precision approximations on resource-constrained hardware—a vital contribution for embedded and low-power robotics. Kusaka is perhaps best known for pioneering the **Partial Lagrangian method**, introduced in two highly cited papers (2022, 2025), which revolutionizes the analysis of multi-DoF systems by allowing analytical extraction of motion-induced torque components without the computational overhead of traditional Lagrange or Newton-Euler approaches. By coupling this method with automatic differentiation, he has opened new pathways for efficient inverse dynamics and system reconstruction. His work on **stateful rotor quaternion continuity** and fast 9-axis sensor fusion (2022, 6 citations) further demonstrates his impact on 3D attitude measurement, a cornerstone of modern drones and wearable devices. With a growing citation footprint and a clear trajectory toward practical, computationally lean solutions, Kusaka is a rising voice in making advanced robotics and control theory accessible to low-performance platforms.
Research Focus
Key Achievements
Top Papers
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