Papers
8
Total Citations
92
H-Index
6
About
Argtim Tika is a robotics and control systems researcher whose work centers on model predictive control (MPC), cooperative robot manipulation, and autonomous trajectory planning. With a growing body of highly cited publications, Tika has made significant contributions to the challenge of coordinating multiple robotic arms operating within shared, confined workspaces — a critical problem in modern industrial automation. His most influential work, "Optimal Scheduling and Model Predictive Control for Trajectory Planning of Cooperative Robot Manipulators" (2020, 33 citations), introduced a hierarchical control framework enabling collision-free pick-and-place operations between cooperative robot arms. This work, alongside related publications on synchronous minimum-time manipulation and distributed MPC, established Tika as a leading voice in multi-robot coordination. His research on dynamic parameter estimation (19 citations) further demonstrates his rigorous approach to optimizing trajectory-based data collection for accurate robot modeling. More recently, Tika has extended his expertise to mobile robotics, developing predictive path-following controllers and exploring deep learning approaches — including a Convolutional Vision Transformer for omnidirectional robots — signaling a forward-looking integration of machine learning with classical control theory. Collectively, his work has accumulated over 90 citations, reflecting meaningful and growing influence within the robotics research community.
Research Focus
Key Achievements
Top Papers
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- 2Dynamic Parameter Estimation Utilizing Optimized Trajectories19 citations · 2020
- 3Predictive Control of Cooperative Robots Sharing Common Workspace10 citations · 2023
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