Path planning of intelligent tennis ball picking robot integrating twin network target tracking algorithm
Abstract Intelligent tennis picking robots can effectively improve the efficiency of tennis training and competition, and reduce manual labor intensity. However, the real-time tracking of targets in existing intelligent robot path planning is poor and susceptible to becoming entrenched in local opti...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-04865-w |
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| author | Zegang Wang |
| author_facet | Zegang Wang |
| author_sort | Zegang Wang |
| collection | DOAJ |
| description | Abstract Intelligent tennis picking robots can effectively improve the efficiency of tennis training and competition, and reduce manual labor intensity. However, the real-time tracking of targets in existing intelligent robot path planning is poor and susceptible to becoming entrenched in local optimal solutions. Therefore, this study proposes an intelligent tennis ball picking robot path planning method that integrates a twin network object tracking algorithm. In terms of target tracking, a hybrid attention mechanism is introduced, which utilizes a transformer structure to achieve hierarchical feature fusion. In terms of path planning, this study combines an improved rapidly-exploring random trees with an artificial potential field ant colony algorithm to enhance the obstacle avoidance capability of robot path planning. Among them, the hybrid attention mechanism enhances local feature extraction and reduces the influence of occlusion by combining grouped convolution transformation and spatially gated embedding. Additionally, the Transformer structure improves tracking accuracy by capturing the global context relationship. In path planning, the improved bidirectional rapidly-exploring random tree algorithm is enhanced through sector constraint sampling to improve search efficiency. The artificial potential field ant colony algorithm optimizes the obstacle avoidance ability and path smoothness. The results showed that in the training dataset, the accuracy of the proposed target tracking algorithm was as high as 0.981, which was 5.40–25.56% higher than existing algorithms such as SiamFC, MORT, SiamRPN, MeMOT, and FROG MOT. In both test datasets, the expected average overlap values were 0.405 and 0.437. The path planning length and time of the proposed method were 42.07 m and 56.12 s, significantly lower than other methods. This indicates that the research method can provide accurate target position information for robots, optimize path planning, and improve the efficiency of picking up tennis balls. This method provides an effective solution for target tracking and path planning of intelligent tennis ball picking robots in complex environments and has important practical application value. |
| format | Article |
| id | doaj-art-1a24b4022f4045efb3bf46540cd82f08 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-1a24b4022f4045efb3bf46540cd82f082025-08-20T03:45:35ZengNature PortfolioScientific Reports2045-23222025-07-0115111710.1038/s41598-025-04865-wPath planning of intelligent tennis ball picking robot integrating twin network target tracking algorithmZegang Wang0School of Physical Education, Xichang UniversityAbstract Intelligent tennis picking robots can effectively improve the efficiency of tennis training and competition, and reduce manual labor intensity. However, the real-time tracking of targets in existing intelligent robot path planning is poor and susceptible to becoming entrenched in local optimal solutions. Therefore, this study proposes an intelligent tennis ball picking robot path planning method that integrates a twin network object tracking algorithm. In terms of target tracking, a hybrid attention mechanism is introduced, which utilizes a transformer structure to achieve hierarchical feature fusion. In terms of path planning, this study combines an improved rapidly-exploring random trees with an artificial potential field ant colony algorithm to enhance the obstacle avoidance capability of robot path planning. Among them, the hybrid attention mechanism enhances local feature extraction and reduces the influence of occlusion by combining grouped convolution transformation and spatially gated embedding. Additionally, the Transformer structure improves tracking accuracy by capturing the global context relationship. In path planning, the improved bidirectional rapidly-exploring random tree algorithm is enhanced through sector constraint sampling to improve search efficiency. The artificial potential field ant colony algorithm optimizes the obstacle avoidance ability and path smoothness. The results showed that in the training dataset, the accuracy of the proposed target tracking algorithm was as high as 0.981, which was 5.40–25.56% higher than existing algorithms such as SiamFC, MORT, SiamRPN, MeMOT, and FROG MOT. In both test datasets, the expected average overlap values were 0.405 and 0.437. The path planning length and time of the proposed method were 42.07 m and 56.12 s, significantly lower than other methods. This indicates that the research method can provide accurate target position information for robots, optimize path planning, and improve the efficiency of picking up tennis balls. This method provides an effective solution for target tracking and path planning of intelligent tennis ball picking robots in complex environments and has important practical application value.https://doi.org/10.1038/s41598-025-04865-wTwin networkTarget tracking algorithmIntelligent robotsTennisPath planning |
| spellingShingle | Zegang Wang Path planning of intelligent tennis ball picking robot integrating twin network target tracking algorithm Scientific Reports Twin network Target tracking algorithm Intelligent robots Tennis Path planning |
| title | Path planning of intelligent tennis ball picking robot integrating twin network target tracking algorithm |
| title_full | Path planning of intelligent tennis ball picking robot integrating twin network target tracking algorithm |
| title_fullStr | Path planning of intelligent tennis ball picking robot integrating twin network target tracking algorithm |
| title_full_unstemmed | Path planning of intelligent tennis ball picking robot integrating twin network target tracking algorithm |
| title_short | Path planning of intelligent tennis ball picking robot integrating twin network target tracking algorithm |
| title_sort | path planning of intelligent tennis ball picking robot integrating twin network target tracking algorithm |
| topic | Twin network Target tracking algorithm Intelligent robots Tennis Path planning |
| url | https://doi.org/10.1038/s41598-025-04865-w |
| work_keys_str_mv | AT zegangwang pathplanningofintelligenttennisballpickingrobotintegratingtwinnetworktargettrackingalgorithm |