Vijay Arvindh
Papers
1
Total Citations
3
H-Index
1
About
Vijay Arvindh is a robotics researcher focused on advancing multi-robot coordination and autonomous navigation in complex, unstructured environments. His most-cited work, "Online Decentralized Receding Horizon Trajectory Optimization for Multi-Robot Systems" (2018), introduces a novel decentralized trajectory generation algorithm that enables multiple robots to plan and adapt their paths in real time without centralized control. This contribution addresses a critical bottleneck in multi-agent systems—trajectory generation remains largely confined to heavily controlled settings—by offering a scalable, online solution for dynamic and unpredictable environments. Though his citation count is modest, his work lays foundational groundwork for applications in search-and-rescue, environmental monitoring, and autonomous logistics. Arvindh’s research sits at the intersection of optimization, control theory, and distributed systems, with a clear vision of deploying multi-robot teams beyond the lab. His approach emphasizes robustness and adaptability, key traits for real-world deployment. As the field matures, his decentralized receding horizon framework stands to influence next-generation multi-robot coordination, making him a promising voice in autonomous systems research.
Research Focus
Key Achievements
Top Papers
- 1