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Multi-group localization problem of service robots based on hybrid external localization algorithm with application to shopping mall environment

机译:基于混合外部定位算法应用于购物商场环境的多组定位问题

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摘要

Intellectualization of life is a general tendency due to the proliferation of technology and science. Based on this concept, this paper presents multi-group localization algorithms and detection algorithms for multi-group service robot system (MGSR). Shopping cart problem is considered as an exemplary multi-group service robot system. The MGSR is designed to provide users with co-service by multiple carts and allows multiple users operation simultaneously. In MGSR, a cart carrying personal belongings of the user follows the user automatically and provides real-time position information to the user. To fulfill estimating the location of MGSR, hybrid external localization algorithm based on combination of QR location information and ZigBee location estimate is proposed. To detect and track a cart by another cart with LRF, we define cart features in LRF data and employ a support vector data description method. Recognition of user-cart groups in MGSR is realized by ZigBee blind nodes on the cart. We verified the feasibility of the proposed algorithms for MGSR through three experiment trials.
机译:由于技术和科学的扩散,生活的智力化是一种普遍的趋势。基于这一概念,本文介绍了多组服务机器人系统(MGSR)的多组定位算法和检测算法。购物车问题被视为示例性多组服务机器人系统。 MGSR旨在为用户提供多次推车的共同服务,并允许多个用户同时操作。在MGSR中,携带用户个人物品的购物车自动跟随用户并向用户提供实时位置信息。为了满足估计MGSR的位置,提出了基于QR位置信息和ZigBee位置估计的组合的混合外部定位算法。要通过带LRF的另一台推车检测和跟踪推车,我们在LRF数据中定义了推车功能并采用支持向量数据描述方法。通过购物车上的ZigBee盲节点实现了MGSR中的用户购物车组的认识。我们通过三项实验试验验证了MGSR算法的可行性。

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