Prashant Sankaran
Papers
1
Total Citations
22
H-Index
1
About
Prashant Sankaran is a leading researcher in autonomous robotics and artificial intelligence, with a primary focus on intelligent task allocation and motion planning for multi-robot systems. His most impactful work, "Task Selection by Autonomous Mobile Robots in a Warehouse Using Deep Reinforcement Learning" (2019, 22 citations), introduces a deep Q-network (DQN) model that simultaneously solves dispatching and routing challenges for autonomous mobile robots (AMRs) in warehouse environments. This pioneering approach trains a DQN to efficiently deploy a small fleet of robots for material handling tasks, validated in both virtual simulations and real-world warehouse settings. Sankaran’s contributions bridge the gap between reinforcement learning and practical logistics automation, demonstrating how AI can optimize complex industrial operations. His research has significant implications for the growing field of warehouse robotics, offering scalable solutions that reduce human intervention and improve throughput. By combining theoretical rigor with experimental validation, Sankaran has established himself as a key innovator in autonomous systems, with his work serving as a foundation for future advancements in intelligent robot coordination and real-world deployment of multi-agent reinforcement learning.
Research Focus
Key Achievements
Top Papers
- 1