Location of Intelligent Carts Using RFID [chapter]

Yasushi Kambayashi, Munehiro Takimoto
2011 Deploying RFID - Challenges, Solutions, and Open Issues  
Deploying RFID -Challenges, Solutions, and Open Issues 250 colony clustering (ACC) is an ACO specialized for clustering objects. The idea is inspired by the collective behaviors of ants, used by Deneubourg to formulate an algorithm that simulates the ant corps gathering and brood sorting behaviors (Deneuburg, Goss, Franks, Sendova-Franks, Detrain & Chretien, 1991) . We have studied the base idea for controlling mobile multiple robots connected by communication networks (Kambayashi, Tsujimura,
more » ... machi, Takimoto, & Yamamoto, 2010; Kambayashi & Takimoto, 2005) . Our framework provides novel methods to control coordinated systems using mobile agents. Instead of physical movement of multiple robots, mobile software agents can migrate from one robot to another so that they can minimize energy consumption for aggregation. In this chapter, we describe the details of implementation of the multi-robot system using multiple mobile agents and static agents that implement ACO as well as the location system using RFID. The combination of the mobile agents augmented by ACO and mobile multiple robots with RFID opens a new horizon of efficient use of mobile robot resources. We report here our experimental observations of our robot cart system. Quasi-optimal cart collection is achieved in three phases. The first phase collects the positions of the carts. One mobile agent issued from the host computer visits scattered carts one by one and collects the positions of them. The precise coordinates and orientation of each robot are determined by interrogating RFID tags under the floor carpet. Upon the return of the position collecting agent, the second phase begins wherein another agent, the simulation agent, performs the ACC algorithm and produces the quasi-optimal gathering positions for the carts. The simulation agent produces not only the assembly positions of the carts but also the moving routes and waiting timings for avoiding collisions; i.e. the entire behaviors of all the intelligent carts. The simulation agent is a static agent that resides in the host computer. In the third phase, a number of mobile agents, the driving agents are issued from the host computer. Each driving agent migrates to a designated cart, and drives the cart to the assigned quasi-optimal position that was calculated in the second phase. The behaviors of each cart are determined by the simulation agent. It is influenced, but not determined, by the initial positions and the orientations of scattered carts, and is dynamically re-calculated as the configuration of the field (positions of the carts in the field) changes. Instead of implementing ACC with actual carts, one static simulation agent performs the ACC computation, and then mobile agents distribute the sets of produced driving instructions. Therefore our method eliminates unnecessary physical movement and provides energy savings. The structure of the balance of this paper is as follows. In the second section, we review the history of research in this area. In the third section, we describe the controlling agent system that performs the arrangement of the intelligent carts. The agent system consists of several static and mobile agents. The static agents interact with the users and compute the ACC algorithm and the simulation of the intelligent carts' behaviors. The other mobile agents gather the initial positions of the robots and drive the carts to the assembly positions. The fourth section describes how each robot determines its coordinates and orientation by sensing RFID tags under the floor carpet. The fifth section describes the ACC algorithm we have employed to calculate the quasi-optimal assembly positions and moving instructions for each cart. Finally, in the sixth section, we summarize the work and discuss future research directions.
doi:10.5772/17545 fatcat:kzi25vay7zb2tiy6worskcis6m