Benjamin E. Hargis
Papers
1
Total Citations
6
H-Index
1
About
Benjamin E. Hargis is a researcher focused on advancing robotics and automation, with a particular emphasis on solving complex kinematic challenges in serial manipulators. His key research area lies in the application of artificial neural networks (ANNs) to address inverse kinematics problems, especially for manipulators with non-spherical wrists—a configuration that poses significant computational difficulties. Hargis’s major contribution, detailed in his most-cited work from 2018, involves training an ANN to accurately determine the configuration parameters of a 6-DOF serial manipulator, enabling precise positioning and orientation control. This innovative approach has garnered 6 citations, reflecting its value in the robotics community. By leveraging machine learning to bypass traditional analytical methods, Hargis has opened new pathways for efficient robotic control in industrial and research settings. His work is particularly notable for tackling a practical limitation of many robotic arms, offering a scalable solution that enhances adaptability. For students and researchers exploring the intersection of neural networks and robotics, Hargis’s study serves as a foundational reference, demonstrating how AI can streamline complex mechanical computations.
Research Focus
Key Achievements
Top Papers
- 1