Papers
1
Total Citations
2
H-Index
1
About
Shivam Yadav is a researcher in robotics and artificial intelligence, with a primary focus on autonomous navigation and reinforcement learning. His key contributions lie in developing intelligent path-planning strategies for mobile robots operating in unknown and dynamic terrains. In his most cited work, "A Q-Learning Strategy for Path Planning of Robots in Unknown Terrains" (2022), Yadav introduced a novel application of the Q-Learning algorithm to enable robots to autonomously learn collision-free, shortest-path routes without prior knowledge of the environment. This approach addresses a fundamental challenge in robotics—navigating uncertainty—by allowing the robot to iteratively improve its decision-making through trial and error. Although early in his career, with the paper garnering 2 citations, the work demonstrates a promising intersection of machine learning and practical robotics. Yadav’s research has implications for autonomous vehicles, warehouse logistics, and search-and-rescue operations, where adaptive navigation is critical. His focus on reinforcement learning for real-world constraints marks him as an emerging voice in the field, with potential for significant future impact as autonomous systems become more prevalent.
Research Focus
Key Achievements
Top Papers
- 1A Q-Learning Strategy for Path Planning of Robots in Unknown Terrains2 citations · 2022