论文标题
分布式本地化,而无需直接通信,灵感来自统计力学
Distributed Localization without Direct Communication Inspired by Statistical Mechanics
论文作者
论文摘要
Distributed localization is essential in many robotic collective tasks such as shape formation and self-assembly.Inspired by the statistical mechanics of energy transition, this paper presents a fully distributed localization algorithm named as virtual particle exchange (VPE) localization algorithm, where each robot repetitively exchanges virtual particles (VPs) with neighbors and eventually obtains its relative position from the virtual particle (VP) amount it owns.使用自定义设计的硬件和协议,VPE本地化算法允许机器人仅使用传感器读数来实现本地化,从而避免与邻居进行直接通信并保持匿名。此外,VPE定位算法会自动确定群中心,从而消除了固定信标的需求,以体现坐标的起源。理论分析证明,VPE定位算法始终可以收敛到相同的结果,而不论初始状态如何,并且渐近时间和记忆复杂性较低。进行了多达10000个机器人的广泛定位模拟,并进行了52个LowCost机器人的实验,从而验证VPE定位算法是否可扩展,准确且鲁棒,对传感器噪声。基于VPE本地化算法,在使用52个机器人的模拟和实验中进一步实现了形状形成,这说明该算法可以直接应用于支持群体协作任务。
Distributed localization is essential in many robotic collective tasks such as shape formation and self-assembly.Inspired by the statistical mechanics of energy transition, this paper presents a fully distributed localization algorithm named as virtual particle exchange (VPE) localization algorithm, where each robot repetitively exchanges virtual particles (VPs) with neighbors and eventually obtains its relative position from the virtual particle (VP) amount it owns. Using custom-designed hardware and protocol, VPE localization algorithm allows robots to achieve localization using sensor readings only, avoiding direct communication with neighbors and keeping anonymity. Moreover, VPE localization algorithm determines the swarm center automatically, thereby eliminating the requirement of fixed beacons to embody the origin of coordinates. Theoretical analysis proves that the VPE localization algorithm can always converge to the same result regardless of initial state and has low asymptotic time and memory complexity. Extensive localization simulations with up to 10000 robots and experiments with 52 lowcost robots are carried out, which verify that VPE localization algorithm is scalable, accurate and robust to sensor noises. Based on the VPE localization algorithm, shape formations are further achieved in both simulations and experiments with 52 robots, illustrating that the algorithm can be directly applied to support swarm collaborative tasks.