自由形态机器人自组装结构中的接触驱动定位
Contact-Driven Localization in a Freeform Robotic Self-Assembled Structure
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中文总结 AI 辅助
针对群体机器人定位难题，提出仅用二元接触信息的虚拟力框架，实现无外部基础设施的模块化机器人自组装定位，仿真验证其可支持自由形态结构的精确组装。
中文摘要 AI 辅助
精确的定位仍是群体机器人领域的关键挑战，尤其对于必须识别相对位置以形成多样结构的自重构系统而言。现有多数方法依赖外部跟踪基础设施或高成本传感器，这限制了其在非结构化环境中的可扩展性与部署能力。本文提出一种面向模块化机器人的新型接触驱动定位方法，仅利用二元接触信息（两个机器人是否物理连接）进行本地通信。为利用这些接触线索，我们引入虚拟力框架，其中机器人通过迭代优化自身位姿，向已对接连接的邻居吸引、向未连接的邻居排斥。该方法无需外部基础设施，仅依赖最小的机载传感。仿真结果显示，在塔状结构与悬臂结构的组装过程中，该方法可实现有效定位，支持精确、可扩展的自由形态自组装。
英文摘要
Accurate localization remains a key challenge in swarm robotics, particularly for self-reconfigurable systems that must identify relative positions to form diverse structures. Most existing approaches rely on external tracking infrastructure or high-cost sensors, which limit scalability and deployment in unstructured environments. In this paper, we propose a novel contact-driven localization method for modular robots that leverages only local communication through binary contact information (whether two robots are physically connected or not). To exploit these contact cues, we introduce a virtual-force framework in which robots iteratively refine their poses attracting toward dock-connected neighbors and repelling from non-connected ones. The method requires no external infrastructure and relies only on minimal onboard sensing. Simulations show effective localization during the assembly of towers and cantilevers, enabling accurate, scalable, free-form self-assembly.
发表机构
- New Jersey Institute of Technology（新泽西理工学院）
机构由 AI 辅助整理，请以论文原文为准。
