RESUMO
Environment perception is essential for autonomous mobile robots in human-robot coexisting outdoor environments. One of the important tasks for such intelligent robots is to autonomously detect the traversable region in an unstructured 3D real world. The main drawback of most existing methods is that of high computational complexity. Hence, this paper proposes a binocular vision-based, real-time solution for detecting traversable region in the outdoors. In the proposed method, an appearance model based on multivariate Gaussian is quickly constructed from a sample region in the left image adaptively determined by the vanishing point and dominant borders. Then, a fast, self-supervised segmentation scheme is proposed to classify the traversable and non-traversable regions. The proposed method is evaluated on public datasets as well as a real mobile robot. Implementation on the mobile robot has shown its ability in the real-time navigation applications.
RESUMO
In this paper, a novel method for respiratory monitoring is presented. The method is based on Trichel pulses (TPs) using a simple field ionization sensor which consists of a needle electrode and a plate electrode. Experiments have been conducted to demonstrate that different respiratory patterns, including normal, ultra-fast, deep breaths, and apnea could be easily monitored in real time by detecting the changes in the TP frequency. The vital capacity could also be assessed by calculating the variation of TP frequency. It is found that the operation principle of the proposed sensor is based on the effects of breath airflow and the atomized water in exhaled air on the TP frequency by changing the ionization process and the dynamics of charged particles in the short gap. The influences of applied voltage and ambient parameters have also been investigated.