Papers
2
Total Citations
4
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About
David Delouche is a robotics researcher focused on advancing the dynamic modeling and parameter identification of robotic systems. His primary research areas include robot dynamics, system identification, and control theory, with a particular emphasis on developing and comparing methodologies for extracting accurate dynamic parameters from robotic manipulators. In his most cited work, "Identification of the dynamic parameters of the planar robot model at 2 degrees of freedom using the least squares method," Delouche addresses the fundamental challenge of determining robot dynamic parameters by leveraging the inverse dynamic model, which is linear with respect to the parameters, and employing least-squares estimation techniques. His subsequent paper, "Comparative Analysis of DIDIM and IV Approaches using Double Least Squares Method," further advances the field by systematically comparing different identification approaches, including the Direct and Indirect Identification Method (DIDIM) and Instrumental Variables (IV) methods, to improve accuracy and robustness. Although his citation counts are currently modest, Delouche's work contributes to the critical infrastructure of robot control, enabling more precise modeling for applications in automation and robotics.
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