Shubham Shubham
Papers
1
Total Citations
5
H-Index
1
About
Shubham Shubham is a researcher at the forefront of bio-inspired robotics and autonomous navigation, with a particular focus on indoor path planning and spatial cognition. His most-cited work, "Bio Inspired Approaches for Indoor Path Navigation and Spatial Map Formation by Analysing Depth Data" (2022), addresses a critical bottleneck in mobile robotics: the trade-off between computational efficiency and robust path exploration in complex indoor environments. By integrating biological principles—such as insect-inspired visual odometry and neural spatial mapping—with depth sensor data, Shubham proposes novel frameworks that outperform traditional graph-theoretic optimization methods. His research demonstrates how bio-mimetic strategies can enable robots to form accurate spatial maps and navigate safely without heavy computational overhead, a key challenge for real-time deployment. With 5 citations to date, this paper has already influenced subsequent work in swarm robotics and assistive navigation systems. Shubham’s contributions are particularly relevant for applications in autonomous warehouses, healthcare robotics, and smart home assistants, where reliable indoor navigation remains a persistent hurdle. His work exemplifies how interdisciplinary approaches—merging biology, computer vision, and control theory—can yield practical, scalable solutions for next-generation autonomous systems.
Research Focus
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Top Papers
- 1