Autonomous robots are expected to replace dangerous, dirty, and demanding (3D) jobs. At a theme park, surveillance, cleaning, and guiding tasks can be regarded as 3D jobs. The present paper attempts to develop an autonomous tour guide robot system and co-experience system at a large theme park. For realizing such autonomous service robots used in our daily environment, localization is one of the most important and fundamental functions. A number of localization techniques, including simultaneous localization and mapping, have been proposed. Although a global navigation satellite system (GNSS) is most commonly used in outdoor environments, its accuracy is approximately 10 m, which is inadequate for navigation of an autonomous service robot. Therefore, a GNSS is usually used together with other localization techniques, such as map matching or camera-based localization. In the present study, we adopt the quasi-zenith satellite system (QZSS), which became available in and around Japan in November 2018, for the localization of an autonomous service robot. The QZSS provides high-accuracy position information using quasi-zenith satellites (QZSs) and has a localization error of less than 10 centimeters. In the present paper, we compare the positioning performance of the QZSS and the real-time kinematic GPS and verify the stability and the accuracy of the QZSS in an outdoor environment. In addition, we introduce a tour guide robot system using the QZSS and present the results of a guided tour experiment in a theme park. On the other hand, based on the tour guide system, we also develop a co-experience robot system in a theme park, which realizes the sharing of experiences using an immersive VR display and the 5th-generation mobile communication system (5G). The robot is equipped with a 360-degree video camera and real-time 4K video is transmitted to a remote operator using the large communication capacity of the 5G network. The experimental results at a theme park showed that the guided tour experiment was successful and that the co-experience system allowed sharing of the experience with high immersion by a remote operator.
All Science Journal Classification (ASJC) codes
- Modelling and Simulation
- Mechanical Engineering
- Control and Optimization
- Artificial Intelligence