Yusuke Yoshida
Hitachi (Japan), Utsunomiya University, Kagawa University, Okayama University
Papers
4
Total Citations
19
H-Index
3
About
Yusuke Yoshida’s research spans robotics, autonomous navigation, and surgical innovation, with a focus on integrating deep learning for real-world applications. His most impactful work centers on motion planning for obstacle avoidance, where he pioneered the use of Convolutional Neural Networks (CNNs) combined with Long Short-Term Memory (LSTM) networks through mediated perception. This approach allows mobile robots to dynamically anticipate and avoid moving obstacles, addressing a critical challenge in autonomous navigation—his 2024 paper on this topic has already garnered 7 citations, demonstrating its growing influence. Earlier in his career, Yoshida contributed to industrial automation with a cloth-handling robot system that uses force sensors to estimate the status of tangled fabrics, a practical solution for linen supply factories (4 citations). More recently, he has ventured into medical robotics, co-authoring a 2024 study on the “Double-Surgeon Technique” for robotic gastrectomy, which aims to improve surgical education and minimally invasive outcomes (2 citations). Yoshida’s work bridges AI-driven robotics and clinical practice, showcasing a versatile research portfolio that addresses both autonomous systems and human-centered surgical training.
Research Focus
Key Achievements
Top Papers
- 1
- 2Motion Planner based on CNN with LSTM through Mediated Perception6 citations · 2022
- 3Status estimation of cloth handling robot using force sensor4 citations · 2009
- 4