Algorithmic Modified Denavit–Hartenberg Modeling for Robotic Manipulators Using Line Geometry
Source abstract
Abstract
This paper proposes a kinematic modeling method for robotic manipulators by extracting the modified Denavit–Hartenberg (MDH) parameters using line geometry. For single-branched manipulators, various joint axes can be represented as lines using Plücker coordinates. The forward kinematics is derived by performing the product of matrices which are the exponential maps lifted from two kinds of exponential coordinates using the MDH parameters. For extracting MDH parameters, line geometry systematically analyzes the following: (1) the closest point between a point and line, (2) the closest distance and twist angle between two lines, (3) the common perpendicular line and its intersection points, and (4) classifies line relationships into collinear, distant parallel, intersected, and skewed cases. For each case, five parameters including twist angle, closest distance, common perpendicular direction vector, and both feet on a common perpendicular line are sequentially computed as results of the line geometry block. Finally, the aforementioned line geometry blocks are utilized to extract the four MDH parameters according to their definitions. The effectiveness of the proposed algorithm is verified by four examples including a typical Selective Compliance Assembly Robot Arm (SCARA) robot and three different commercial manipulators.
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Research context
OpenAlex topics: Robotic Mechanisms and Dynamics · Advanced Numerical Analysis Techniques · Dynamics and Control of Mechanical Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Electrical and Electronic Engineering, Hanyang University, Ansan 15588, Republic of Korea
Department of Robotics, Hanyang University, Ansan 15588, Republic of Korea
Funding
Institute of Civil-Military Technology Cooperation funded by the Defense Acquisition Program Administration and the Ministry of Trade, Industry, and Energy of the Korean government
References (32)
Show source-provided references
1. Featherstone, R. (1984). Robot Dynamics Algorithms. [Ph.D. Thesis, University of Edinburgh].
2. Robots and screw theory: Applications of kinematics and statics to robotics10.1115/1.1775805 ↗
3. Murray, R.M., Li, Z., and Sastry, S.S. (2017). A Mathematical Introduction to Robotic Manipulation, CRC Press.10.1201/9781315136370 ↗
4. Lynch, K.M., and Park, F.C. (2017). Modern Robotics, Cambridge University Press.10.1017/9781316661239 ↗
5. Improving the absolute positioning accuracy of robot manipulators10.1002/rob.4620020406 ↗
6. A kinematic notation for lower-pair mechanisms based on matrices10.1115/1.4011045 ↗
7. Hartenberg, R., and Danavit, J. (1964). Kinematic Synthesis of Linkages, McGraw-Hill.
8. The near-minimum-time control of open-loop articulated kinematic chains10.1115/1.3426492 ↗
9. Featherstone, R. (1982). A Program for Simulating Robot Dynamics, Department of Artificial Intelligence, University of Edinburgh. Working Paper 155.
10. Craig, J.J. (2006). Introduction to Robotics, Pearson Educacion.
11. Lipkin, H. (2005, January 24–28). A note on Denavit-Hartenberg notation in robotics. Proceedings of the International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Long Beach, CA, USA.
12. Waldron, K.J., and Schmiedeler, J. (2008). Chapter 1 Kinematics, Handbook of Robotics, Springer.10.1007/978-3-540-30301-5_2 ↗
13. A Simple and Systematic Approach to Assigning Denavit–Hartenberg Parameters10.1109/TRO.2007.896765 ↗
14. Lin, Y., Zhao, H., Ye, C., and Ding, H. (2017, January 5–8). A computationally efficient and robust kinematic calibration model for industrial robots with kinematic parallelogram. Proceedings of the 2017 IEEE International Conference on Robotics and Biomimetics (ROBIO), Macau, China.10.1109/ROBIO.2017.8324602 ↗
15. Faria, C., Vilaça, J.L., Monteiro, S., Erlhagen, W., and Bicho, E. (2019, January 14–17). Automatic Denavit-Hartenberg Parameter Identification for Serial Manipulators. Proceedings of the IECON 2019—45th Annual Conference of the IEEE Industrial Electronics Society, Lisbon, Portugal.10.1109/IECON.2019.8927455 ↗
16. Rajeevlochana, C.G., Saha, S.K., and Kumar, S. (June, January 29). Automatic Extraction of DH Parameters of Serial Manipulators using Line Geometry. Proceedings of the 2nd Joint International Conference on Multibody System Dynamics, Stuttgart, Germany.
17. POE-based kinematic calibration for serial robots using left-invariant error representation and decomposed iterative method10.1016/j.robot.2024.104896 ↗
18. An integrated kinematic calibration and dynamic identification method with only static measurements for serial robot10.1109/TMECH.2023.3241302 ↗
19. Kinematic-parameter identification for serial-robot calibration based on POE formula10.1109/TRO.2010.2047529 ↗
20. POE-based robot kinematic calibration using axis configuration space and the adjoint error model10.1109/TRO.2016.2593042 ↗
21. Lie-group methods10.1017/S0962492900002154 ↗
22. Bullo, F., and Murray, R.M. (1995). Proportional Derivative (PD) Control on the Euclidean Group, California Institute of Technology. CDS Technical Report 95-010.
23. Hodge, W.V.D., and Pedoe, D. (1994). Methods of Algebraic Geometry, Cambridge University Press.10.1017/CBO9780511623899 ↗
24. Mason, M.T. (2001). Mechanics of Robotic Manipulation, MIT Press.10.7551/mitpress/4527.001.0001 ↗
25. Jia, Y.B. (2020). Plücker coordinates for lines in the space. Problem Solver Techniques for Applied Computer Science, Com-S-477/577 Course Handout, Iowa State University.
26. Sung, M. (2025, April 27). Algorithmic MDH. Available online: https://github.com/MinchangSung0223/AlgorithmicMDH.
27. Coumans, E., and Bai, Y. (2025, April 27). Pybullet, a Python Module for Physics Simulation for Games, Robotics and Machine Learning. Available online: http://pybullet.org.
28. (2023, November 27). ROS.org. URDF. Available online: http://wiki.ros.org/urdf.
29. Neuromeka (2023, November 27). Indy7 URDF. Available online: https://github.com/neuromeka-robotics/indy-ros.
30. Tadese, M., Pico, N., Seo, S., and Moon, H. (2022). A Two-Step Method for Dynamic Parameter Identification of Indy7 Collaborative Robot Manipulator. Sensors, 22.10.3390/s22249708 ↗
31. KUKA (2023, November 27). KUKA lbr Iiwa URDF. Available online: https://github.com/rtkg/lbr_iiwa.
32. Universal Robotics (2023, November 27). UR5 URDF. Available online: https://github.com/ros-industrial/universal_robot.