Daniel Henrique Braz de Sousa
Pontifícia Universidade Católica do Rio de Janeiro, Military Institute of Engineering
Papers
4
Total Citations
27
H-Index
3
About
Daniel Henrique Braz de Sousa is a robotics researcher whose work centers on the system identification and dynamic modeling of flexible robotic manipulators, with a particular focus on elastomer-based series elastic actuators (SEAs). His primary contributions lie in developing hybrid gray-box and black-box nonlinear models to characterize the complex nonlinearities introduced by compliant elements in robotic systems—a critical challenge for advancing safe human-robot collaboration. His most cited work, "Hybrid gray and black-box nonlinear system identification of an elastomer joint flexible robotic manipulator" (2023, 17 citations), demonstrates his ability to fuse physics-informed approaches with data-driven techniques for precise dynamic modeling. Through subsequent studies, including "Black-box Identification with Static Neural Networks" and "System Identification of an elastomeric series elastic actuator" (each with 4 citations), he has systematically explored neural network-based methods to capture actuator nonlinearities. His most recent contribution, "Physics-informed and black-box Identification of robotic actuator with a flexible joint" (2024, 2 citations), further integrates physical constraints into learning-based models. Collectively, his work addresses a fundamental bottleneck in collaborative robotics: obtaining accurate, tractable models that ensure both safety and performance in human-interactive systems.
Research Focus
Key Achievements
Top Papers
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