Sujay Chakraborty
Papers
1
Total Citations
3
H-Index
1
About
Sujay Chakraborty is a rising researcher at the intersection of artificial intelligence, robotics, and computational intelligence. His primary contributions lie in developing adaptive algorithms for autonomous navigation and decision-making, with a particular focus on path planning for mobile robots. Chakraborty’s most cited work introduces an innovative hybrid framework that synergizes deep reinforcement learning with a neuro-fuzzy inference system, enabling mobile robots to compute collision-free, efficient paths from start to goal in dynamic environments. This approach addresses a critical challenge in mobile robotics—autonomous navigation—by combining the learning capabilities of deep reinforcement learning with the reasoning power of fuzzy logic. With over 3 citations to his leading paper, Chakraborty’s research demonstrates early but meaningful impact in the field. His work is especially relevant for students and researchers interested in intelligent systems, autonomous vehicles, and adaptive control. By bridging theoretical advances in reinforcement learning with practical robotic applications, Chakraborty is contributing to the next generation of self-navigating machines capable of operating independently in complex, real-world settings.
Research Focus
Key Achievements
Top Papers
- 1