Location of Intelligent Carts Using RFID

Abstract

This chapter addresses optimizing distributed robotic control of systems using an example of an intelligent cart system designed to be used in common airports. This framework provides novel control methods using mobile software agents. In airport terminals, luggage carts used by traveler are taken from a depot but are left after use at arbitrary points. It would be desirable that carts be able to draw themselves together automatically after being used so that manual collection becomes less laborious. In order to avoid excessive energy consumption by the carts, we employ mobile software agents and RFID (Radio Frequency Identification) tags to identify the location of carts scattered in a field and then cause them to autonomously determine their moving behavior using a clustering method based on the ant colony optimization (ACO) algorithm. When we pass through terminals of an airport, we often see carts scattered in the walkway and employees manually collecting them one by one. It is a laborious task and not a fascinating job. It would be much easier if carts were roughly gathered in any way before the laborers begin to collect them. Multiple robot systems have made rapid progress in various fields, and the core technologies of multiple robot systems are now easily available (Kambayashi & Takimoto, 2005). Employing such technologies, it is possible to give each cart minimum intelligence, making each cart an autonomous robot. We realize that for such a system cost is a significant issue and we address one of those costs, the power source. A big, powerful battery is heavy and expensive; therefore such intelligent cart systems with small batteries are desirable to save energy (Takimoto, Mizuno, Kurio & Kambayashi, 2007; Nagata, Takimoto & Kambayashi, 2009; Oikawa, Mizutani, Takimoto & Kambayashi, 2010; Abe, Takimoto & Kambayashi, 2011). Travelers pick up carts at designated points and leave them in arbitrary places. It is desirable that intelligent carts (intelligent robots) draw themselves together automatically. A simple implementation would be to give each cart a designated assembly point to which it automatically returns when free. That solution is easy to implement, but some carts would have to travel a long way back to their own assembly point, even though they are located close to other assembly points. That strategy consumes unnecessary energy. To improve efficiency, we employ mobile software agents to locate carts scattered in a field, e.g. an airport, and enable them to determine their moving behavior autonomously using a clustering algorithm based on ant colony optimization (ACO). ACO is a swarm intelligencebased method and a multi-agent system that exploits artificial stigmergy for the solution of combinatorial optimization problems. Preliminary experiments yield a favorable result. Ant