Dhadkan Shrestha
Papers
1
Total Citations
1
H-Index
1
About
Dhadkan Shrestha is a researcher at the forefront of autonomous systems and evolutionary robotics, with a focused expertise in neuroevolutionary algorithms for real-world navigation. Her most-cited work, "Reinforced NEAT Algorithms for Autonomous Rover Navigation in Multi-Room Dynamic Scenario" (2025, 1 citation), introduces a pioneering application of NeuroEvolution of Augmenting Topologies (NEAT) to enable rovers to autonomously navigate complex, multi-room environments. By simulating three- and four-room scenarios, Shrestha demonstrates how these algorithms can adapt to dynamic obstacles, offering transformative potential for critical fields such as wildfire management and search-and-rescue missions. Her contributions bridge the gap between theoretical neuroevolution and practical robotics, showcasing how evolving neural networks can replace traditional path-planning methods in unpredictable settings. While her citation count is nascent, the novelty of her work—integrating reinforcement learning principles with NEAT for multi-room traversal—positions her as an emerging voice in autonomous navigation. Shrestha’s research not only advances rover autonomy but also lays groundwork for deploying intelligent, self-adaptive robots in disaster response, where rapid, reliable navigation can save lives. Her approach signals a shift toward more resilient, biologically inspired AI systems.
Research Focus
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Top Papers
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