Papers
12
Total Citations
197
H-Index
7
About
Faraz Kunwar is a robotics and autonomous systems researcher whose work has made significant contributions to mobile robot navigation, path planning, and dynamic obstacle avoidance. His research spans over two decades, addressing some of the most challenging problems in autonomous robotic motion, particularly in unpredictable, real-world environments. Kunwar's most impactful contribution — his work on the Guided Autowave Pulse Coupled Neural Network (GAPCNN) for real-time path planning (58 citations) — introduced a biologically inspired framework enabling robots to navigate complex environments efficiently. His early foundational work on rendezvous-guidance trajectory planning (44 citations) pioneered time-optimal methods for intercepting moving targets while avoiding dynamic obstacles, a problem with critical applications in surveillance, search-and-rescue, and autonomous vehicles. Beyond neural network approaches, Kunwar has explored cellular automata, hybrid global-local planners, probabilistic pursuit-evasion strategies, and, most recently, Z-number-based fuzzy logic navigation under uncertainty within ROS environments. This breadth reflects a consistent drive to make autonomous robots more adaptive and reliable in real-world conditions. With over 190 cumulative citations, his body of work remains a valued reference for researchers tackling the enduring challenges of intelligent robotic motion planning.
Research Focus
Key Achievements
Top Papers
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- 7Z-Number-Based Fuzzy Logic Approach for Mobile Robot Navigation7 citations · 2023
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- 9Probabilistic Search and Pursuit Evasion on a Graph4 citations · 2015
- 10Cellular Automata Based Real-Time Path-Planning for Mobile Robots3 citations · 2014