Goutham Mallapragada
Papers
10
Total Citations
80
H-Index
5
About
Goutham Mallapragada’s research lies at the intersection of robotics, control theory, and formal languages, with a focus on autonomous navigation and behavior identification. His most significant contribution is the development of the ν☆ (nu-star) path planning algorithm, which reframes robot path planning as an optimization problem over probabilistic finite state automata using a renormalized measure of regular languages. This language-measure-theoretic approach, detailed in his most-cited paper (22 citations), enables robots to plan optimal paths while dynamically adapting to changing environments. Mallapragada also pioneered the use of Symbolic Dynamic Filtering (SDF) for automated behavior recognition in mobile robots, allowing for signature detection and behavior classification without extensive domain knowledge. His work extends to multi-agent coordination, including simulation test beds for unmanned rotorcraft and ground vehicles, and real-time dynamic planning algorithms like All-Pair Dynamic Planning (APDP). By applying discrete-event supervisory control and probabilistic language measures to robotics, Mallapragada has created a rigorous mathematical framework for intelligent navigation, behavior analysis, and autonomous decision-making in complex, uncertain environments.
Research Focus
Key Achievements
Top Papers
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- 9Language-measure-based supervisory control of a mobile robot3 citations · 2005
- 10Tracking Mobile Targets Using Wireless Sensor Networks2 citations · 2010