Christian Hartl-Nesic
Papers
15
Total Citations
154
H-Index
6
About
Christian Hartl-Nesic is a robotics researcher whose work centers on motion planning, trajectory optimization, and kinematic control for industrial robot manipulators. His research addresses some of the most challenging computational problems in modern robotics, particularly the real-time solution of inverse kinematics for redundant manipulators and robust path planning for complex manufacturing processes. Among his most influential contributions is a machine learning-based framework for solving analytical inverse kinematics in real time, which has garnered 37 citations since 2023, alongside an optimization-based path planning framework for industrial manufacturing that has accumulated 34 citations in the same period. These works directly tackle the growing demands of flexible, individualized production environments. Hartl-Nesic has also made significant strides in singularity avoidance, model predictive control for trajectory optimization, and optimal robot base placement—each addressing practical bottlenecks in deploying robots on factory floors. Beyond pure motion planning, his research extends to learning from demonstration for surface interaction tasks and innovative approaches for applying adhesive tapes on freeform 3D surfaces, reflecting a broad interest in contact-rich manipulation. With a consistently growing citation record across tightly focused, application-driven publications, Hartl-Nesic is emerging as a notable voice in industrial robotics and intelligent motion control research.
Research Focus
Key Achievements
Top Papers
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- 4Optimal TCP and Robot Base Placement for a Set of Complex Continuous Paths15 citations · 2021
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- 9Collaborative Synchronization of a 7-Axis Robot5 citations · 2019
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