High accuracy mobile robot positioning using external large volume metrology instruments

A method of accurately controlling the position of a mobile robot using an external large volume metrology (LVM) instrument is presented in this article. By utilising an LVM instrument such as a laser tracker or indoor GPS (iGPS) in mobile robot navigation, many of the most difficult problems in mobile robot navigation can be simplified or avoided. Using the real-time position information from the laser tracker, a very simple navigation algorithm, and a low cost robot, 5mm repeatability was achieved over a volume of 30m radius. A surface digitisation scan of a wind turbine blade section was also demonstrated, illustrating possible applications of the method for manufacturing processes. Further, iGPS guidance of a small KUKA omni-directional robot has been demonstrated, and a full scale prototype system is being developed in cooperation with KUKA Robotics, UK. © 2011 Taylor & Francis.

High accuracy mobile robot positioning using an external large volume metrology instrument

A method of accurately controlling the position of a mobile robot using an external Large Volume Metrology (LVM) instrument is presented in this paper. Utilizing a LVM instrument such as the laser tracker in mobile robot navigation, many of the most difficult problems in mobile robot navigation can be simplified or avoided. Using the real- Time position information from the laser tracker, a very simple navigation algorithm, and a low cost robot, 5mm repeatability was achieved over a volume of 30m radius. A surface digitization scan of a wind turbine blade section was also demonstrated, illustrating possible applications of the method for manufacturing processes. © Springer-Verlag Berlin Heidelberg 2010.

Mobile note taking:investigating the efficacy of mobile text entry

When designing interaction techniques for mobile devices we must ensure users are able to safely navigate through their physical environment while interacting with their mobile device. Non-speech audio has proven effective at improving interaction on mobile devices by allowing users to maintain visual focus on environmental navigation while presenting information to them via their audio channel. The research described here builds on this to create an audio-enhanced single-stroke-based text entry facility that demands as little visual resource as possible. An evaluation of the system demonstrated that users were more aware of their errors when dynamically guided by audio-feedback. The study also highlighted the effect of handwriting style and mobility on text entry; designers of handwriting recognizers and of applications involving mobile note taking can use this fundamental knowledge to further develop their systems to better support the mobility of mobile text entry.

Dynamic Bayesian network for semantic place classification in mobile robotics

In this paper, the problem of semantic place categorization in mobile robotics is addressed by considering a time-based probabilistic approach called dynamic Bayesian mixture model (DBMM), which is an improved variation of the dynamic Bayesian network. More specifically, multi-class semantic classification is performed by a DBMM composed of a mixture of heterogeneous base classifiers, using geometrical features computed from 2D laserscanner data, where the sensor is mounted on-board a moving robot operating indoors. Besides its capability to combine different probabilistic classifiers, the DBMM approach also incorporates time-based (dynamic) inferences in the form of previous class-conditional probabilities and priors. Extensive experiments were carried out on publicly available benchmark datasets, highlighting the influence of the number of time-slices and the effect of additive smoothing on the classification performance of the proposed approach. Reported results, under different scenarios and conditions, show the effectiveness and competitive performance of the DBMM.