Memory-Efficient 2D/3D Shape Assembly of Robot Swarms
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866918350005731328 |
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| author | Yue, Shuoyu Li, Pengpeng Xu, Yang Ze, Kunrui Long, Xingjian Cao, Huazi Sun, Guibin |
| author_facet | Yue, Shuoyu Li, Pengpeng Xu, Yang Ze, Kunrui Long, Xingjian Cao, Huazi Sun, Guibin |
| contents | Mean-shift-based approaches have recently emerged as a representative class of methods for robot swarm shape assembly. They rely on image-based target-shape representations to compute local density gradients and perform mean-shift exploration, which constitute their core mechanism. However, such representations incur substantial memory overhead, especially for high-resolution or 3D shapes. To address this limitation, we propose a memory-efficient tree representation that hierarchically encodes user-specified shapes in both 2D and 3D. Based on this representation, we design a behavior-based distributed controller for assignment-free shape assembly. Comparative 2D and 3D simulations against a state-of-the-art mean-shift algorithm show one to two orders of magnitude lower memory usage and two to four times faster shape entry. Physical experiments with 6 to 7 UAVs further validate real-world practicality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_26518 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Memory-Efficient 2D/3D Shape Assembly of Robot Swarms Yue, Shuoyu Li, Pengpeng Xu, Yang Ze, Kunrui Long, Xingjian Cao, Huazi Sun, Guibin Robotics Mean-shift-based approaches have recently emerged as a representative class of methods for robot swarm shape assembly. They rely on image-based target-shape representations to compute local density gradients and perform mean-shift exploration, which constitute their core mechanism. However, such representations incur substantial memory overhead, especially for high-resolution or 3D shapes. To address this limitation, we propose a memory-efficient tree representation that hierarchically encodes user-specified shapes in both 2D and 3D. Based on this representation, we design a behavior-based distributed controller for assignment-free shape assembly. Comparative 2D and 3D simulations against a state-of-the-art mean-shift algorithm show one to two orders of magnitude lower memory usage and two to four times faster shape entry. Physical experiments with 6 to 7 UAVs further validate real-world practicality. |
| title | Memory-Efficient 2D/3D Shape Assembly of Robot Swarms |
| topic | Robotics |
| url | https://arxiv.org/abs/2509.26518 |