P
Paramsothy Jayakumar
Researcher at United States Department of the Army
Publications - 159
Citations - 1775
Paramsothy Jayakumar is an academic researcher from United States Department of the Army. The author has contributed to research in topics: Finite element method & Terramechanics. The author has an hindex of 19, co-authored 148 publications receiving 1254 citations. Previous affiliations of Paramsothy Jayakumar include Michigan State University & University of Michigan.
Papers
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Combined Speed and Steering Control in High-Speed Autonomous Ground Vehicles for Obstacle Avoidance Using Model Predictive Control
TL;DR: Simulation results show that the proposed algorithm is capable of safely exploiting the dynamic limits of the vehicle while navigating the vehicle through sensed obstacles of different sizes and numbers and can significantly improve performance by allowing navigation of obstacle fields that would otherwise not be cleared with steering control alone.
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A study on model fidelity for model predictive control-based obstacle avoidance in high-speed autonomous ground vehicles
TL;DR: In this article, the authors investigated the level of model fidelity needed in order for a model predictive control (MPC)-based obstacle avoidance algorithm to be able to safely and quickly avoid obstacles.
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A high-fidelity approach for vehicle mobility simulation: Nonlinear finite element tires operating on granular material☆
Antonio M. Recuero,Radu Serban,Bryan Peterson,Hiroyuki Sugiyama,Paramsothy Jayakumar,Dan Negrut +5 more
TL;DR: In this paper, the authors focus on physics-based methodologies for wheeled vehicle mobility that factor in both tire flexibility and terrain deformation within a fully three-dimensional multibody system approach.
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A sequential calibration and validation framework for model uncertainty quantification and reduction
TL;DR: A sequential model calibration and validation (SeCAV) framework is proposed to improve the efficacy of both model parameter calibration and bias correction for the purpose of uncertainty quantification and reduction.
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A nonlinear model predictive control formulation for obstacle avoidance in high-speed autonomous ground vehicles in unstructured environments
TL;DR: In this paper, a nonlinear model predictive control (MPC) formulation for obstacle avoidance in high-speed, large-size autono-mous ground vehicles (AGVs) with high center of gravity (CoG) that operate in unstructured environments, such as military vehicles, is presented.