Arun Kumar Sah
Papers
2
Total Citations
25
H-Index
2
About
Arun Kumar Sah is a researcher specializing in autonomous mobile robotics, with a primary focus on integrating deep reinforcement learning for intelligent navigation and obstacle avoidance. His work addresses the critical challenge of enabling wheeled mobile robots to operate without human intervention in dynamic environments. Sah’s most cited paper, "Application of Deep Q-Learning for Wheel Mobile Robot Navigation" (2017, 22 citations), pioneered the use of deep Q-learning to enhance path planning and real-time obstacle avoidance, demonstrating a significant step toward fully autonomous systems. He further refined this approach in his 2018 work, "Log-Based Reward Field Function for Deep-Q-Learning for Online Mobile Robot Navigation" (3 citations), where he introduced a novel reward function to improve online learning efficiency and robot adaptability. By bridging reinforcement learning with practical robotics, Sah’s contributions offer scalable solutions for industrial and service robotics, with his 2017 paper serving as a foundational reference for researchers exploring deep learning-driven autonomy. His work continues to inspire advances in self-navigating systems, emphasizing robust, real-time decision-making in complex terrains.
Research Focus
Key Achievements
Top Papers
- 1Application of Deep Q-Learning for Wheel Mobile Robot Navigation22 citations · 2017
- 2