Papers
17
Total Citations
386
H-Index
10
About
Paramsothy Jayakumar is a leading researcher in autonomous ground vehicle (AGV) mobility, terrain-vehicle interaction, and off-road robotics, whose work bridges computational simulation, machine learning, and real-world vehicle dynamics. His research has fundamentally advanced how engineers model and predict vehicle performance across complex and uncertain terrain environments. Jayakumar's most influential contribution, the R2-RRT* algorithm (92 citations), introduced reliability-based robust mission planning for off-road AGVs operating under uncertain terrain conditions — a critical advance for military and commercial autonomous systems. His extensive work on granular dynamics and terrain modeling, including comparisons of compliant versus rigid contact methods (51 citations) and physics-based simulation of light tracked vehicles, has provided the field with rigorous computational frameworks for understanding soil-vehicle interaction. His deformable soil modeling and geostatistical approaches to mobility prediction further established scalable methods for large-area terrain assessment. More recently, Jayakumar has embraced machine learning, developing efficient mobility map generation techniques and Convolutional Bayesian Kernel Inference for 3D semantic mapping, positioning his work at the frontier of trustworthy robotic perception. With over 350 cumulative citations, his research portfolio reflects sustained, high-impact contributions that directly inform the design and deployment of next-generation autonomous ground systems.
Research Focus
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Top Papers
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- 10Convolutional Bayesian Kernel Inference for 3D Semantic Mapping14 citations · 2023