Mohak Shah
Papers
2
Total Citations
16
H-Index
2
About
Dr. Mohak Shah is a leading researcher in autonomous robotics, with a primary focus on developing intelligent systems for area coverage in unknown and dynamic environments. His work sits at the intersection of deep reinforcement learning, path planning, and multi-robot coordination, addressing the critical challenge of enabling robots to navigate and cover spaces without prior maps. His most cited paper, "Deep Reinforcement Learning Based Online Area Covering Autonomous Robot" (2021, 9 citations), introduces a novel framework that allows a robot to learn optimal coverage strategies in real-time, adapting to changing obstacles and room geometry. This work is complemented by his study "Online Area Covering Robot in Unknown Dynamic Environments" (2021, 7 citations), which further explores the limitations of traditional universal algorithms and proposes adaptive solutions. Dr. Shah’s contributions are particularly impactful for the deployment of service robots in residential and commercial settings, where robustness to unpredictable layouts is essential. His research bridges the gap between theoretical reinforcement learning and practical robotic applications, paving the way for more autonomous and efficient cleaning, inspection, and surveillance systems.
Research Focus
Key Achievements
Top Papers
- 1Deep Reinforcement Learning Based Online Area Covering Autonomous Robot9 citations · 2021
- 2Online Area Covering Robot in Unknown Dynamic Environments7 citations · 2021