Abstract
[Objective] To improve the motion efficiency and operational stability of food handling robots, a robot trajectory planning method is proposed using a hybrid-improved whale optimization algorithm. [Methods] Firstly, a "quintic-cubic-quintic" hybrid polynomial trajectory model is constructed to ensure smoothness during the start and stop phases. Secondly, a weighted comprehensive objective function is established, aiming to minimize both running time and the integral of Jerk, thereby quantifying the overall performance of the trajectory. Then, addressing the shortcomings of the standard whale algorithm, such as the high randomness of population initialization and the imbalance between global exploration and local development, the standard whale optimization algorithm is subjected to multi-strategy hybrid and deep improvement. This hybrid-improved whale optimization algorithm is subsequently utilized to optimize and solve the trajectory time parameters. Finally, comparative tests are conducted in two typical scenarios, "high-frequency sorting" and "liquid food transfer", using the latest enhanced algorithms as a benchmark. [Results] In the efficiency-prioritized scenario, the proposed method reduces running time by 3.7% and the overall performance metrics by 8.4%. In the stability-prioritized scenario, its advantages are even more pronounced, with the average Jerk decreases by 18.2% and overall performance metrics drops by 16.2%. [Conclusion] The proposed trajectory planning method effectively addresses the multi-objective balance between efficiency and smoothness in the trajectory planning of food handling robots.
Publication Date
1-13-2026
First Page
99
Last Page
107
DOI
10.13652/j.spjx.1003.5788.2025.60106
Recommended Citation
Lixia, HUANG; Wei, LI; Chuyu, YAN; and Mingqing, LIU
(2026)
"Trajectory planning for food handling robots based on hybrid-improved whale optimization algorithm,"
Food and Machinery: Vol. 41:
Iss.
12, Article 13.
DOI: 10.13652/j.spjx.1003.5788.2025.60106
Available at:
https://www.ifoodmm.cn/journal/vol41/iss12/13
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