The Development of an Autonomous Library Assistant Service Robot

Abstract

In modern society, service robots are becoming increasingly integrated into the lives of ordinary people. This is primarily due to the fact that the world is becoming an aged society (a society in which 10% of the population is over 60 years of age). Service robots may provide support to this increasing pool of aged individuals in a variety of forms, such as social interaction robots (Bruce et al., 2001; Breazeal 2002; Fong et al., 2003), task manipulation in rehabilitation robotics (Casals et al., 1993; Bolmsjo et al., 1995; Dario et al., 1995) and through assistive functionality such as nurse bots, tour guide robots etc (Evans 1994; Thrun et al., 2000; Graf et al., 2004). This chapter describes the development of an autonomous service robotic assistant known as “LUCAS”: Limerick University Computerized Assistive System, whose functionality includes the assistance of individuals within a library environment. The robot is described in this role through environment interaction, user interaction and integrated functionality. The robot acts as a guide for users within the library to locate user specific textbooks. A complete autonomous system has been implemented, which allows for simple user interaction to initiate functionality and is described specifically in terms of its implemented localization system and its human–robot interaction system. When evaluating the overall success of a service robot, three important factors need to be considered: 1. A successful service robot must have complete autonomous capabilities. 2. It must initiate meaningful social interaction with the user and 3. It must be successful in its task. To address these issues, factors 1 and 3 are grouped together and described with respect to the localization algorithm implemented for the application. The goal of the proposed localization system is to implement a low cost accurate navigation system to be applied to a real world environment. Due to cost constraints, the sensors used were limited to odometry, sonar and monocular vision. The implementation of the three sensor models ensures that a two dimensional constraint is provided for the position of the robot as well as the orientation. The localization system described here implements a fused mixture of existing localization techniques, incorporating landmark based recognition, applied to a unique setting. In classical approaches to landmark based pose determination, two distinguished interrelated problems are identified. The first is the correspondence problem, which is concerned with finding pairs of corresponding landmark and image features. The