Research on Formation Reorganization Optimization and Trajectory Planning for Robotic Swan Light Performance on Water Surface

Zhanchong Zhou; Huailin Luo; Tao Zhang

doi:10.54097/2d2d5114

Authors

Zhanchong Zhou
Huailin Luo
Tao Zhang

DOI:

https://doi.org/10.54097/2d2d5114

Keywords:

Collective Performance, Trajectory Planning, Formation Reorganization Allocation, Distributed Model Predictive Control

Abstract

This paper proposes a method for a robotic swan light performance on the water surface, conducting an in-depth study on the reorganization and trajectory planning of the formation. In response to the reorganization needs of the performance formation faced by the water surface robot group, this scheme takes into account the performance requirements of individual robots and the collaborative effect of the entire group, realizing optimal aesthetic adjustment of the formation reorganization allocation. At the same time, the scheme also includes a trajectory planning method, which ensures the smooth and continuous motion of the swan water surface robots on the water through the distributed model predictive control (DMPC) framework, while avoiding potential collisions, enhance the lighting effects of the swan water surface performance by the robot swarm.

Downloads

Download data is not yet available.

References

[1] Gao Shunli. "How to Expand 'Night Scenery' and 'Night Tours' into the Night-time Economy" [J]. China Economic Weekly, 2024, (01): 108-110.

[2] Du Haiyi. Preliminary Exploration of the Development Model of Night Scene Economy in Cultural Tourism [J]. Modern Marketing (End of the Month Edition), 2020, (10): 22-1256/ f.2020.10.087.

[3] Zhu Lei. Research on the Product Design of Intelligent Landscape Lighting for Bochi Mountain Park under the Night Economy [D]. Guangdong University of Technology, 2021. 00119.

[4] Du Haiyi. Preliminary Exploration of the Night Scene Economy Development Model in Cultural Tourism [J]. Modern Marketing (End of the Month Issue), 2020, (10): 22-1256/ f.2020.10.087.

[5] Light Up 2020! New Year's Eve Light Shows Across the Globe" [J]. Journal of Lighting Engineering, 2020, 31(01): 92.

[6] Shu Y.-P., Liu C., Meng Y.-Z. Fault-Tolerant Collision Avoidance and Obstacle Avoidance Control for Quadrotor UAV Formation Based on DMPC [J]. Journal of Control and Detection, 2024, 7(01): 21-31.

[7] Wang Yingchun. Unveiling the Secrets Behind Drone Performances [N]. Hainan Daily,2022-02-21(B11).2022. 001025.

[8] Chen Xi, Chen Leiyin. A Study on the Integration of Interactive Light Art Installations into Public Spaces [J]. Screen Printing, 2023, (18): 99-101. 2023.18.029.

[9] Tian Yuezong. A Study on Fault Diagnosis Method for Electrical Equipment in Thermal Power Plants Based on PCA [J]. Popular Electricity, 2024, 39(07): 46-48.

[10] Wang Qiuping. Analysis of Influencing Parameters in the Hot Rolling Process Based on Principal Component Analysis [J]. Shanxi Metallurgy, 2024, 47(06): 39-41.

[11] Zhang Yalin. Research on the Customization of the Best Tourist Route in Scenic Spots Based on the K-Means Clustering Algorithm [J]. Computer Programming Skills and Maintenance, 2023, (11): 65-68. 2023.11.018.

[12] Zhou Rongrong, Chen Dong, Liu Siyuan. Research on the Optimization Model of Fresh Food Transportation Path Based on K-Means Clustering Algorithm [J]. Journal of Agricultural Big Data, 2022, 4(01): 89-97.

[13] Hao Wenkang, Chen Qifeng. Energy-saving Control of UAV Formation Based on Fuzzy Constrained Distributed Model Predictive Control [J]. Systems Engineering and Electronics, 2024, 46(03): 1021-1030.

[14] Wei Ruixuan, Lv Minghai, Ru Changjian, et al. Research on Collision Avoidance Control for UAV Formation Reconstruction Based on DE-DMPC [J]. Systems Engineering and Electronics, 2014, 36(12): 2473-2478.

[15] Li Bo, Qu Yuan, Xu Jing. Research on Online Trajectory Planning for Multiple UAVs Based on DMPC-PSO in Complex Dynamic Environment [J]. Journal of Inner Mongolia Normal University (Natural Science Edition), 2018, 47(06): 491-498.

[16] Wang P, Ding B.A synthesis approach of distributed-model predictive control for homogeneous multi-agent system with collision avoidance[J]. International Journal of Control, 2014, 87(1): 52-63.

[17] P. Ru, “Nonlinear model predictive control for cooperative control and estimation,” Ph.D. dissertation, 2017.