Miguel García-Gómez
Papers
2
Total Citations
5
H-Index
2
About
Miguel García-Gómez is a robotics researcher whose work bridges computer vision, multi-agent systems, and socially assistive robotics. His research focuses on developing intelligent perception and navigation systems for robots operating in human-centered environments, with a particular emphasis on safety and autonomy. García-Gómez’s most cited work, “Integración ConvNeXt-YOLO mediante CVV para detectar caídas en robot social” (2024, 3 citations), addresses a critical societal challenge: enabling robots to detect falls among the growing elderly population who wish to age in place. By integrating advanced deep learning architectures like ConvNeXt with YOLO object detection, he contributes to creating safer home environments through real-time, vision-based fall detection on social robots. His earlier foundational work, “A multi-agent system based on active vision and ultrasounds applied to fuzzy behavior based navigation” (2004, 2 citations), demonstrates his long-standing interest in combining visual and ultrasonic sensors with fuzzy logic for robust, behavior-based robot navigation in indoor spaces. Through these contributions, García-Gómez advances the practical deployment of robots that can both navigate intelligently and respond to human needs, making him a notable figure in the intersection of assistive robotics and computer vision.
Research Focus
Key Achievements
Top Papers
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- 2