Implementation of a Localization-Oriented Hri for Walking Robots in the Robocup Environment

Abstract

Received AcceptedThis paper presents the design and implementation of a human-robot interface capable of evaluating robot localization performance and maintaining full control of robot behaviors in the RoboCup domain. The system consists of legged robots, behavior modules, an overhead visual tracking system, and a graphic user interface. A human-robot communication framework is designed for executing cooperative and competitive processing tasks between users and robots by using object oriented and modularised software architecture, operability, and functionality. Some experimental results are presented to show the performance of the proposed system based on simulated and real-time information.

Visual Localisation of Mobile Devices in an Indoor Environment Under Network Delay Conditions

“…The motion capture or motion tracking is the process in which a movement is recorded and translated into a digital model. Its application area includes the military, entertainment, sports, medical applications, and the validation of computer vision and robotics [16].…”

“…The Vicon System [18] is a commercial solution for motion capture, and can be used such as a tracking system for recording robot positioning data [16]. It is based on the simultaneous recording by 8 CMOS video cameras of small reflective balls (markers) which are attached to the mobile robot or to the mobile device.…”

Visual Localisation of Quadruped Walking Robots

“…In this figure is shown a simulated localisation process of FM grid starting from absolute uncertainty of robot pose (a) and some initial uncertainty (b) and (c). Through to an approximated (d) and finally to an acceptable robot pose estimation obtained from simulated environment explained in (Samperio & Hu, 2008).…”

“…In general, visual localisation of legged robots can be achieved by using artificial and natural landmarks. The landmark modelling problem has been already investigated by using predefined landmark matching and real-time landmark learning strategies as in (Samperio & Hu, 2010). Also, by following the pre-attentive and attentive stages of previously presented work of (Quoc et al, 2004), we propose a landmark model for describing the environment with "interesting" features as in (Luke et al, 2005), and to measure the quality of landmark description and selection over time as shown in (Watman et al, 2004).…”