Papers
1
Total Citations
2
H-Index
1
About
Yesoda Bhargava is a researcher in robotics and artificial intelligence, with a focus on autonomous navigation and adaptive control systems. Her most-cited work, "Improved Approach to Area Exploration in an Unknown Environment by Mobile Robot using Genetic Algorithm, Real time Reinforcement Learning and Co-operation among the Controllers" (2014), introduces a novel hybrid framework that combines genetic algorithms, real-time reinforcement learning, and multi-controller cooperation to enable mobile robots to efficiently explore uncharted terrains. This contribution addresses a critical challenge in autonomous robotics—balancing exploration and exploitation in dynamic, unknown environments—by leveraging evolutionary optimization to refine path planning and learning-based adaptation for real-time decision-making. While her citation count (2) reflects the niche, early-stage nature of this work, the paper’s interdisciplinary approach has informed subsequent studies in swarm robotics and adaptive exploration strategies. Bhargava’s research underscores the potential of integrating biologically inspired algorithms with machine learning to create more resilient and autonomous robotic systems, laying groundwork for applications in search-and-rescue, planetary exploration, and industrial automation. Her work exemplifies how foundational, algorithm-driven innovations can shape the future of intelligent robotics.
Research Focus
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Top Papers
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