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
Top Papers
- 1
- 2Aesthetic guideline driven photography by robots14 citations · 2011
- 3
- 4MAMA: multi-agent management of crowds to avoid stampedes in long queues5 citations · 2013
- 5Wheeled Robots playing Chain Catch: Strategies and Evaluation4 citations · 2016
- 6Loosely Coupled Payload Transport System with Robot Replacement4 citations · 2019
- 7
- 8Motion Planning for Multi-Mobile-Manipulator Payload Transport Systems2 citations · 2019
- 9Managing Multi Robotic Agents to Avoid Congestion and Stampedes2 citations · 2015
- 10Crowd Congestion and Stampede Management through Multi Robotic Agents2 citations · 2015