Arpita Sinha
Papers
19
Total Citations
160
H-Index
8
About
Arpita Sinha is a leading researcher in multi-agent robotics, specializing in decentralized coordination, trajectory planning, and autonomous navigation. Her work addresses critical challenges in multi-robot systems, including patrolling, graph exploration, and collision avoidance. Her most cited paper, "A deep reinforcement learning approach for multi-agent mobile robot patrolling" (2022, 27 citations), advances intelligent decision-making for robot teams. She is also known for pioneering the use of Lissajous curves in trajectory planning, enabling efficient coverage and target detection in complex environments. Her 2016 paper on collision-free trajectory planning using these curves uniquely addresses multiple surveillance objectives simultaneously, including complete area coverage and rogue element detection. With over 130 total citations across her top ten papers, Sinha’s contributions have practical implications for search-and-rescue, environmental monitoring, and security. Notable achievements include her work on target tracking with range-only information and obstacle avoidance in dynamic settings, demonstrating her ability to solve real-world problems with elegant, implementable algorithms. Her research continues to inspire students and engineers in robotics and autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 3Application of Lissajous curves in trajectory planning of multiple agents14 citations · 2019
- 4Conditions for target tracking with range-only information14 citations · 2015
- 5Generalizing Multi-agent Graph Exploration Techniques11 citations · 2020
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- 9A novel obstacle avoidance control algorithm in a dynamic environment7 citations · 2013
- 10