Srinivas Tennety
Papers
1
Total Citations
11
H-Index
1
About
Dr. Srinivas Tennety is a robotics researcher whose work focuses on intelligent path planning and autonomous navigation in unknown environments. His most-cited contribution, "Support Vector Machines Based Mobile Robot Path Planning in an Unknown Environment" (2009, 11 citations), pioneered the application of support vector machines (SVM)—traditionally used as maximum margin classifiers—to generate non-linear decision boundaries for robot trajectory planning. By leveraging SVM’s ability to separate data classes, Tennety demonstrated how robots could dynamically compute safe, obstacle-free paths without prior environmental maps. His Player/Stage simulation studies validated the approach across diverse case studies, offering a computationally efficient alternative to conventional methods like potential fields or grid-based planners. This work bridges machine learning and mobile robotics, providing a foundation for adaptive navigation systems in unstructured settings. Tennety’s research is particularly valuable for students and engineers developing autonomous vehicles, service robots, or exploration drones, as it illustrates how classification algorithms can be repurposed for real-time spatial reasoning. His contributions highlight the growing synergy between pattern recognition and robotic control, inspiring further investigation into SVM-based decision-making for motion planning under uncertainty.
Research Focus
Key Achievements
Top Papers
- 1