Yevgeniy Lischuk

About

Yevgeniy Lischuk’s research lies at the intersection of robotics, trajectory planning, and artificial intelligence, with a particular focus on enhancing the precision and efficiency of multilink robotic systems. His most cited work, “Generating Constant Screw Axis Trajectories With Quintic Time Scaling For End-Effector Using Artificial Neural Network And Machine Learning” (2021), introduces a novel framework that leverages neural networks and machine learning algorithms to generate smooth, constant screw axis trajectories for robotic end-effectors. By integrating quintic time scaling, Lischuk’s approach ensures continuous velocity and acceleration profiles, significantly improving motion control in complex robotic tasks. This contribution has garnered 6 citations, reflecting its growing relevance in the robotics and AI communities. Lischuk’s work is notable for bridging classical kinematics with modern data-driven methods, offering a scalable solution for real-time trajectory generation. His research holds promise for applications in industrial automation, surgical robotics, and autonomous systems, where precise and adaptive motion is critical. Through his innovative fusion of machine learning and robotic control, Lischuk is advancing the capabilities of intelligent robotic systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Generating Constant Screw Axis Trajectories With Quintic Time Scaling For End-Effector Using Artificial Neural Network And Machine Learning

   6 citations · 2021

Key Collaborators

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