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International Journal of Neural Network, 2026, 5(1); doi: 10.38007/NN.2026.050107.

Application and Performance Analysis of an S-curve Trajectory Planning Algorithm Based on Sine Functions in High-Speed Transfer Equipment

Author(s)

Bin Zhou1, Tiesheng Liu1, Zongwang Cai1, Shufeng Zhang2

Corresponding Author:
Shufeng Zhang
Affiliation(s)

1Jingwei Systemtechnik Ltd., Ningbo, Zhejiang, China

2Ningbo City College of Vocational Technology, Ningbo, Zhejiang, China

Abstract

To address the high demands for trajectory smoothness and transfer efficiency during the motion of high-speed, high-precision transfer equipment for flexible materials, traditional trajectory planning methods often face issues such as abrupt acceleration changes, significant impact, or computational complexity. Based on a project technical report, this paper proposes an S-curve trajectory planning algorithm based on sine functions. By designing a continuously smooth sine acceleration curve, the algorithm divides the motion process into five stages: acceleration-jerk, acceleration-deceleration, constant velocity, deceleration-jerk, and deceleration-deceleration, and establishes an accurate mathematical model. Through MATLAB simulation, this algorithm is compared and analyzed against cubic polynomial, quintic polynomial, and trapezoidal velocity planning algorithms. The results show that this algorithm achieves continuous and smooth transitions in acceleration while ensuring high transfer efficiency, effectively suppressing impact and vibration. It offers the optimal comprehensive performance and is suitable for high-speed transfer scenarios involving flexible materials.

Keywords

Trajectory planning, Sine function, S-curve, High-speed transfer, Flexible materials

Cite This Paper

Bin Zhou, Tiesheng Liu, Zongwang Cai, Shufeng Zhang. Application and Performance Analysis of an S-curve Trajectory Planning Algorithm Based on Sine Functions in High-Speed Transfer Equipment. International Journal of Neural Network (2026), Vol. 5, Issue 1: 64-70. https://doi.org/10.38007/NN.2026.050107.

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