Vaibhav Deshmukh
Papers
4
Total Citations
21
H-Index
2
About
Vaibhav Deshmukh’s research lies at the intersection of swarm robotics, control theory, and trajectory optimization, with a particular focus on mean-field models for large-scale multi-agent systems. His most impactful work, “Mean-Field Stabilization of Markov Chain Models for Robotic Swarms” (2018, 15 citations), introduces computational approaches for synthesizing decentralized density-feedback laws that asymptotically stabilize target equilibrium distributions in robotic swarms—a foundational contribution to scalable swarm control. In related theoretical work, Deshmukh established global controllability and rational feedback properties for continuous-time Markov chains, advancing the mathematical underpinnings of mean-field controllability. His applied research includes dynamic trajectory planning for nonholonomic mobile robots intercepting moving targets, where he employed cubic Bézier curves for real-time path adaptation and energy estimation under velocity and continuity constraints. These studies, though smaller in citation count, demonstrate his ability to bridge theory and practice in autonomous navigation. Deshmukh’s contributions are particularly valuable for researchers working on decentralized coordination, stochastic control, and energy-efficient motion planning in robotics. His work continues to influence the design of scalable, provably stable swarm behaviors.
Research Focus
Key Achievements
Top Papers
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