Shin Ando

Yaskawa Electric (Japan), Gunma University

Papers

Total Citations

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About

Shin Ando’s research lies at the intersection of robotics, anomaly detection, and behavior analysis, with a focus on making autonomous systems safer and more interpretable. His most cited work, “A fast collision-free path planning method for a general robot manipulator” (2004, 11 citations), introduced a practical hybrid approach combining global and serial local search to rapidly generate collision-free paths—a foundational contribution to real-time robot motion planning. Building on this, Ando pioneered anomaly detection in robotic behaviors with “ACE: Anomaly Clustering Ensemble for Multi-perspective Anomaly Detection in Robot Behaviors” (2011, 10 citations), which addressed the critical challenge of selecting temporal parameters in time-series subsequence analysis. This work enabled robots to autonomously identify irregular behaviors without manual tuning. Extending his impact to data-driven social analysis, “Role-Behavior Analysis from Trajectory Data by Cross-Domain Learning” (2011, 7 citations) demonstrated how trajectory data could reveal latent roles and behavioral patterns—a novel cross-domain learning framework. Ando’s contributions are notable for bridging theoretical rigor with practical deployment, earning him recognition for advancing both robotic autonomy and data mining methodologies. His work continues to influence researchers in intelligent systems and behavior informatics.