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arXiv · 2509.26518

Memory-Efficient 2D/3D Shape Assembly of Robot Swarms

Shuoyu Yue·Pengpeng Li·Yang Xu·Kunrui Ze·Xingjian Long·Huazi Cao·Guibin Sun
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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.

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Shuoyu Yue, Pengpeng Li, Yang Xu, Kunrui Ze, Xingjian Long, Huazi Cao, Guibin Sun. 2025-09-30. Memory-Efficient 2D/3D Shape Assembly of Robot Swarms. https://arxiv.org/abs/2509.26518

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