Papers

11

Total Citations

89

H-Index

4

About

Kamalakar Karlapalem’s research lies at the intersection of multi-robot systems, autonomous motion planning, and crowd management. His most impactful work addresses decentralized obstacle avoidance for multi-robot target tracking using model predictive control (MPC), a paper with 42 citations that formulates local motion planning as a quadratic program to handle dynamic environments. He has also pioneered aesthetic guideline-driven photography by robots, enabling autonomous capture of high-quality images through iterative repositioning. In the domain of deformable payload transport, Karlapalem developed algorithms for loosely coupled nonholonomic robots to navigate static and dynamic obstacles, and introduced robot replacement strategies to extend operational time in multi-robot transport systems. His notable contributions to crowd safety include multi-agent management frameworks to prevent stampedes in long queues and real-time detection of congestion-prone areas, with several papers on this topic. With a total of over 85 citations across his top ten papers, Karlapalem’s work demonstrates a consistent focus on practical, real-world applications—from robotic games like Chain Catch to traffic management for rigid payload transport. His research is characterized by hierarchical motion planning and decentralized control, making significant strides in autonomous multi-agent coordination.

Research Focus

Key Achievements

4
H-Index
11
Papers
89
Total Citations
8
Avg Citations/Paper
🏆 Most Cited Paper
Decentralized MPC based Obstacle Avoidance for Multi-Robot Target Tracking Scenarios
42 citations · 2018
📈 Most Prolific Year: 2019 (4 Papers)
🤝 Key Collaborators: 14
🏛 Institutions: Indian Institute of Technology Hyderabad, International Institute of Information Technology, Hyderabad

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 15 days ago