Anil Nelakanti
Papers
1
Total Citations
10
H-Index
1
About
Anil Nelakanti is a researcher whose work lies at the intersection of robotics, computer vision, and optimization. His primary focus is on developing practical, constraint-aware algorithms for autonomous systems, particularly in the domains of visual servoing and navigation. His most notable contribution, "Path Planning for Visual Servoing and Navigation Using Convex Optimization," addresses a critical bottleneck in deploying vision-based robots: ensuring that real-world physical and sensory limitations—such as a camera's limited field of view—are respected during motion planning. By framing these challenges as convex optimization problems, Nelakanti provides a mathematically rigorous yet computationally efficient framework that bridges the gap between theoretical control and practical deployment. This work has garnered 10 citations, reflecting its value to researchers tackling similar problems in constrained robotic environments. Nelakanti’s research is particularly relevant for students and engineers seeking to integrate visual feedback into autonomous systems without sacrificing safety or performance. His contributions underscore a commitment to making vision-based robotics more reliable and deployable in real-world settings.
Research Focus
Key Achievements
Top Papers
- 1PATH PLANNING FOR VISUAL SERVOING AND NAVIGATION USING CONVEX OPTIMIZATION10 citations · 2015