Morteza Farrokhsiar
Papers
9
Total Citations
83
H-Index
5
About
Morteza Farrokhsiar’s research lies at the intersection of motion planning, control theory, and probabilistic robotics, with a particular focus on enabling autonomous systems to operate reliably under uncertainty. His most influential work introduces the *unscented model predictive control* (UMPC) framework, a novel approach that replaces traditional analytical linearization with statistical linearization for controlling constrained nonlinear systems. This method, detailed in his top-cited paper (29 citations), provides a robust, tube-based MPC scheme for integrated probing motion planning. Farrokhsiar has also made significant contributions to multi-robot coordination, developing an UMPC approach for the formation control of nonholonomic mobile robots in dynamic, unstructured environments (22 citations). On the perception side, he advanced visual SLAM by generalizing the Rao-Blackwellized particle filter to incorporate higher-order state variables and a modified undelayed initialization scheme, enabling more accurate 3D monocular vSLAM. His work on chance-constrained nonlinear systems and a teaching tool for probabilistic localization methods further underscores his dedication to both theoretical rigor and educational impact. With a publication record spanning from 2009 to 2015, Farrokhsiar’s contributions continue to inform modern approaches to safe, uncertainty-aware autonomous navigation.
Research Focus
Key Achievements
Top Papers
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- 3Unscented predictive motion planning of a nonholonomic system8 citations · 2011
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- 5A higher order Rao-Blackwellized particle filter for monocular vSLAM6 citations · 2010
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- 7Monocular vSLAM using a novel Rao-Blackwellized particle filter3 citations · 2010
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