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About

Maksat Kalimoldayev is a robotics researcher whose work bridges mechanical design, intelligent control, and machine learning. His primary focus lies in the kinematics and development of serial/parallel hybrid robots—a field where he has made notable contributions to improving robot versatility, precision, and workspace. His most cited paper, “Design and kinematics of serial/parallel hybrid robot” (2017, 14 citations), addresses the growing industrial demand for multifunctional robots by combining the strengths of both serial and parallel architectures. Kalimoldayev has also advanced parallel manipulator design through his work on the “RoboMech” class (2016, 9 citations), and explored integrated machine learning modules for verbal robots (2019, 3 citations). His early efforts in mobile robotics include trajectory planning and object recognition (2016, 2 citations), while his recent work on gripper parameters for spherical and cylindrical objects (2023) demonstrates ongoing practical applications in manipulation. With a career spanning foundational kinematics to modern AI integration, Kalimoldayev’s research continues to shape the development of more capable, intelligent robotic systems for industrial and service sectors.