David Balderas
Papers
6
Total Citations
198
H-Index
5
About
David Balderas is a researcher at the forefront of robotics, intelligent control, and inclusive technology. His work primarily focuses on enhancing robotic systems through advanced control algorithms and artificial intelligence, with a significant emphasis on making these technologies accessible to all. Balderas’s most cited paper, “PD Control Compensation Based on a Cascade Neural Network Applied to a Robot Manipulator” (98 citations), addresses a critical challenge in industrial robotics: the degradation of PID controllers over time. By introducing a cascade neural network to compensate for this, he has improved the stability and bandwidth of robot manipulators, a contribution with direct implications for manufacturing precision. He has also explored deep learning for robotics, notably through his work on convolutional long short-term memory networks for image sequence prediction (55 citations), advancing how robots perceive and navigate dynamic environments. More recently, Balderas has turned his attention to social sustainability, co-authoring a systematic review on the lack of verified inclusive technology for workers with disabilities in Industry 4.0 (26 citations). This work highlights a critical gap in the integration of advanced manufacturing technologies, positioning him as a voice for equitable technological progress. His research, which also includes practical implementations like NAO robot maze navigation, demonstrates a rare blend of theoretical depth and real-world application, making him a compelling figure in modern robotics.
Research Focus
Key Achievements
Top Papers
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- 5Human Movement Control8 citations · 2016
- 6