Research on Multi-Objective Trajectory Planning for Industrial Robots Based on Machine Learning

Authors

  • Kexuan Shen Texas A&M University, College Station, Texas, US Author

DOI:

https://doi.org/10.71222/2jcjjz62

Keywords:

industrial robot, machine learning, multi-objective optimization, trajectory planning, intelligent manufacturing

Abstract

This study proposes a machine learning-based method to address the multi-objective trajectory planning problem for industrial robots. First, the kinematic model of the industrial robot is constructed, and the multi-objective trajectory planning problem is analyzed. Then, a machine learning-based trajectory planning framework is designed, including key steps such as feature engineering, model selection, and training. Subsequently, a multi-objective optimization algorithm is proposed to balance multiple objectives in trajectory planning. Finally, the effectiveness of the proposed method is validated through simulation experiments and practical application cases. The results show that this method significantly improves the efficiency and accuracy of industrial robot trajectory planning, providing a new solution for the field of intelligent manufacturing.

References

1. O. Ciprian, V. Silviu, M. Muguras, et al., "End-To-End Computer Vision Framework: An Open-Source Platform for Research and Education," Sensors (Basel, Switzerland), vol. 21, no. 11, pp. 3691-3692, 2021, doi: 10.3390/s21113691.

2. W. Zhang, C. Lin, T. Liu, et al., "Multiple Extended Target Tracking Algorithm Based on Spatio-Temporal Correlation," Appl. Sci., vol. 14, no. 6, p. 20, 2024,doi: 10.3390/app14062367.

3. Y. Huang, H. Huang, M. Niu, et al., "UAV Complex-Scene Single-Target Tracking Based on Improved Re-Detection Staple Algorithm," Remote Sens., vol. 16, no. 10, pp. 12-14, 2024,doi: 10.3390/rs16101768.

4. F. Shamsfakhr, D. Macii, L. Palopoli, et al., "A multi-target detection and position tracking algorithm based on mmWave-FMCW radar data," Measurement, vol. 234114797, 2024,doi: 10.1016/j.measurement.2024.114797.

5. J. Huang, J. Xie, H. Zhang, et al., "A Novel Tracking Algorithm Based on Waveform Selection for Maneuvering Targets in Clutter," Circuits Syst. Signal Process., vol. 43, no. 5, pp. 3160-3179, 2024,doi: 10.1007/s00034-024-02601-9.

6. S. Yi, "Artificial Intelligence (AI)-Robotics Started When Human Capability Reached Limit, Human Creativity Begin Again When the Capability of AI-Robotics Reaches a Plateau," Neurospine, vol. 21, no. 1, pp. 3-5, 2024,doi: 10.14245/ns.2448234.117.

7. Q. Song, Q. Zhao, "Recent Advances in Robotics and Intelligent Robots Applications," Appl. Sci., vol. 14, no. 10, p. 16, 2024, doi: 10.3390/app14104279.

8. S. Yin, Q. Liang, "Research on Multi-target Tracking Algorithm Based on Track Segment Association," J. Phys. Conf. Ser., vol. 2747, no. 1, pp. 8-11, 2024,doi: 10.1088/1742-6596/2747/1/012017.

9. Y. Huo, B. Chen, J. Zhang, et al., "UAV Target Tracking Algorithm Based on Illumination Adaptation and Future Awareness in Low Illumination Scenes," Int. J. Pattern Recognit. Artif. Intell., vol. 38, no. 03, pp. 13-15, 2024, doi: 10.1142/S0218001424550036.

10. W. Xu, J. Xiao, D. Xu, et al., "An Adaptive IMM Algorithm for a PD Radar with Improved Maneuvering Target Tracking Performance," Remote Sens., vol. 16, no. 6, p. 11, 2024,doi: 10.3390/rs16061051.

Downloads

Published

22 January 2025

Issue

Section

Article

How to Cite

Shen, K. (2025). Research on Multi-Objective Trajectory Planning for Industrial Robots Based on Machine Learning. Journal of Computer, Signal, and System Research, 2(1), 28-37. https://doi.org/10.71222/2jcjjz62