Manish Bansal
Papers
1
Total Citations
7
H-Index
1
About
Dr. Manish Bansal is a leading researcher at the intersection of stochastic optimization and autonomous systems, with a primary focus on developing robust decision-making frameworks for unmanned ground vehicles (UGVs) and robotics under uncertainty. His most-cited work, "Two-stage stochastic programming approach for path planning problems under travel time and availability uncertainties" (2019, 7 citations), introduces a pioneering mathematical framework that addresses critical challenges in homeland security and collaborative multi-vehicle operations. By modeling travel time variability and vehicle availability as stochastic elements, Bansal’s approach enables UGVs to adaptively plan reliable paths in dynamic, unpredictable environments—a breakthrough that bridges theoretical optimization with real-world deployment in sensing, robotics, and wireless networks. His contributions are particularly notable for advancing the practical utility of autonomous aerial, ground, and underwater vehicles in high-stakes applications. With a growing citation footprint, Bansal’s work is shaping how engineers and researchers design resilient autonomous systems, offering a rigorous yet accessible methodology for students and practitioners tackling path planning under uncertainty. His research continues to influence the next generation of intelligent, uncertainty-aware robotics.
Research Focus
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Top Papers
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