{"ID":2862933,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.26518","arxiv_id":"2509.26518","title":"Memory-Efficient 2D/3D Shape Assembly of Robot Swarms","abstract":"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.","short_abstract":"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 s...","url_abs":"https://arxiv.org/abs/2509.26518","url_pdf":"https://arxiv.org/pdf/2509.26518v2","authors":"[\"Shuoyu Yue\",\"Pengpeng Li\",\"Yang Xu\",\"Kunrui Ze\",\"Xingjian Long\",\"Huazi Cao\",\"Guibin Sun\"]","published":"2025-09-30T16:54:59Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"LoRA\"]","has_code":false}