Tanistha Nayak

Veer Surendra Sai University of Technology

Papers

Total Citations

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About

Tanistha Nayak’s research lies at the intersection of robotics, artificial intelligence, and intelligent control systems, with a focus on autonomous navigation and path planning. Her most-cited work, “Controlling Wall Following Robot Navigation Based on Gravitational Search and Feed Forward Neural Network” (22 citations), introduces a novel neural network training algorithm that leverages gravitational search to enhance a robot’s ability to autonomously follow walls—a critical challenge in mobile robotics. This builds on her earlier paper, “Neural Network Approach to Control Wall-Following Robot Navigation” (16 citations), which pioneered the use of neural network-based controllers for efficient, source-to-destination navigation. Nayak also contributed a novel algorithm for intelligent robot path planning (6 citations), proposing a system where a robot, equipped with an antenna, can determine optimal routes between start and end positions. Her work demonstrates how bio-inspired optimization techniques can improve robotic autonomy, offering practical solutions for real-world navigation tasks. Through these contributions, Nayak has established herself as a researcher advancing the frontiers of intelligent robotics, with her methods inspiring further exploration in adaptive control and autonomous systems.