Vikram Raju
Papers
2
Total Citations
8
H-Index
2
About
Vikram Raju is a researcher whose work bridges computational geometry, artificial intelligence, and real-time decision-making under adversarial constraints. His primary research focuses on online evasive path planning—a domain where an autonomous agent must navigate to multiple targets while actively avoiding pursuers. Raju’s major contributions include developing model-based approximate λ-policy iteration and cell decomposition approaches for this pursuit-evasion problem, demonstrated through the challenging, dynamic environment of the video game Ms. Pac-Man. His 2011 papers, each garnering 4 citations, present foundational algorithms that optimize paths in real-time, balancing target visitation with evasion. This work has direct relevance to robotics, particularly in applications like autonomous surveillance, search-and-rescue, and drone navigation in hostile environments. Raju’s research stands out for its novel integration of reinforcement learning principles with geometric decomposition, offering a scalable framework for agents operating under uncertainty and active opposition. His contributions provide a valuable stepping stone for students and researchers exploring adversarial path planning, showcasing how classic game environments can serve as rigorous testbeds for practical robotic algorithms.
Research Focus
Key Achievements
Top Papers
- 1
- 2