Percepcija u inteligentnim prostorima: kombinirana primjena distribuiranih i robotskih senzora

This work considers the joint use of robot onboard sensors and a network of sensors distributed in the environment for tracking the position of the robot and other objects. This is motivated by our research on Intelligent Spaces, which combine the use of distributed sensors with mobile robots to provide various services to users. Here we analyze the distributed sensing using the extended information filter and computation issues that arise due to correlations between estimates. In turn we show how the correlations can be resolved with the use of Covariance Intersection at a cost of conservative estimates, and analyze two special cases where the issues related to correlations can be reduced.Ovaj rad razmatra kombiniranu primjenu senzora na mobilnim robotima i mreže senzora distribuiranih u prostoru za praćenje položaja robota i ostalih objekata. Rad je dio istraživanja o "inteligentnim prostorima", gdje se koriste distribuirani senzori i mobilni roboti sa svrhom pružanja različitih usluga korisnicima prostora. Analizirana je upotreba proširenog informacijskog filtra za distribuiranu percepciju te računski problem uzrokovan korelacijama u procesu estimacije. Potom je objašnjeno rješenje problema korelacija korištenjem metode presjeka kovarijanci (Covariance Intersection), koje međutim daje konzervativne rezultate, te je dana analiza dva specijalna slučaja kod kojih je moguće ublažiti utjecaj korelacija

Data-Driven Imitation Learning for a Shopkeeper Robot with Periodically Changing Product Information

Data-driven imitation learning enables service robots to learn social interaction behaviors, but these systems cannot adapt after training to changes in the environment, such as changing products in a store. To solve this, a novel learning system that uses neural attention and approximate string matching to copy information from a product information database to its output is proposed. A camera shop interaction dataset was simulated for training/testing. The proposed system was found to outperform a baseline and a previous state of the art in an offline, human-judged evaluation

Pedestrian Group Behaviour Analysis under Different Density Conditions

AbstractWe recently introduced a potential to describe pedestrian interaction in walking groups. The potential was used to derive the spatial distribution and velocity of small groups under scarce density conditions and its predictions are in good agreement with observations. In the present work we apply the same method to a new data set regarding pedestrians moving in an indoor facility under different density conditions. To describe the variation of the group structure with changing density we introduce an “effective potential” term that assesses the average effect of the external environment on the group dynamics

Using a Rotating 3D LiDAR on a Mobile Robot for Estimation of Person’s Body Angle and Gender

We studied the use of a rotating multi-layer 3D Light Detection And Ranging (LiDAR) sensor (specifically the Velodyne HDL-32E) mounted on a social robot for the estimation of features of people around the robot. While LiDARs are often used for robot self-localization and people tracking, we were interested in the possibility of using them to estimate the people’s features (states or attributes), which are important in human–robot interaction. In particular, we tested the estimation of the person’s body orientation and their gender. As collecting data in the real world and labeling them is laborious and time consuming, we also looked into other ways for obtaining data for training the estimators: using simulations, or using LiDAR data collected in the lab. We trained convolutional neural network-based estimators and tested their performance on actual LiDAR measurements of people in a public space. The results show that with a rotating 3D LiDAR a usable estimate of the body angle can indeed be achieved (mean absolute error 33.5 ° ), and that using simulated data for training the estimators is effective. For estimating gender, the results are satisfactory (accuracy above 80%) when the person is close enough; however, simulated data do not work well and training needs to be done on actual people measurements

Estimation of Component Concentration in Chemical Mixtures

U ovom su radu analizirane metode estimacije koncentracije kemijskih komponenata u smjesama. Estimacija se obavljala na osnovi infracrvenog spektra smjese dobivenog mjerenjem pomoću FTIR spektra. U radu je dan pregled teorije povezane s FTIR spektrometrijom. Prikazane su osnovne linearne metode koje se koriste u estimaciji. Dan je opis metoda odabira broja komponenata modela. Osim toga, prikazane su metode preprocesiranja spektra. Prikazana je i jednostavna nelinearne metoda estimacije. Algoritmi estimacije ispitani su najprije na simuliranim podacima, a zatim na stvarnim podacima dobivenim mjerenjem. Na kraju dan je komentar dobivenih rezultata, te je dan zakljuèak o pogodnosti korištenja prikazanih metoda kod opisanog problema.In this thesis, methods for estimation of chemical component concentrations in mixtures were analysed. The concentrations are estimated from infrared spectra of the mixture obtained from the FTIR spectrometer. An overview of the theory connected with FTIR spectroscopy was given. Basic linear estimation methods were described. A description of methods for choosing the number of model components was given. Moreover, spectra preprocessing methods were described. A simple nonlinear estimation method was also described. The described methods were tested; first on simulated data, then on measured real data. A comment of obtained results was given at the end. Also, a conclusion on the applicability of the described methods on the research problem was given

bibDEK00873576016

U ovom su radu analizirane metode estimacije koncentracije kemijskih komponenata u smjesama. Estimacija se obavljala na osnovi infracrvenog spektra smjese dobivenog mjerenjem pomoću FTIR spektra. U radu je dan pregled teorije povezane s FTIR spektrometrijom. Prikazane su osnovne linearne metode koje se koriste u estimaciji. Dan je opis metoda odabira broja komponenata modela. Osim toga, prikazane su metode preprocesiranja spektra. Prikazana je i jednostavna nelinearne metoda estimacije. Algoritmi estimacije ispitani su najprije na simuliranim podacima, a zatim na stvarnim podacima dobivenim mjerenjem. Na kraju dan je komentar dobivenih rezultata, te je dan zakljuèak o pogodnosti korištenja prikazanih metoda kod opisanog problema.In this thesis, methods for estimation of chemical component concentrations in mixtures were analysed. The concentrations are estimated from infrared spectra of the mixture obtained from the FTIR spectrometer. An overview of the theory connected with FTIR spectroscopy was given. Basic linear estimation methods were described. A description of methods for choosing the number of model components was given. Moreover, spectra preprocessing methods were described. A simple nonlinear estimation method was also described. The described methods were tested; first on simulated data, then on measured real data. A comment of obtained results was given at the end. Also, a conclusion on the applicability of the described methods on the research problem was given

Recognition of Rare Low-Moral Actions Using Depth Data

Detecting and recognizing low-moral actions in public spaces is important. But low-moral actions are rare, so in order to learn to recognize a new low-moral action in general we need to rely on a limited number of samples. In order to study the recognition of actions from a comparatively small dataset, in this work we introduced a new dataset of human actions consisting in large part of low-moral behaviors. In addition, we used this dataset to test the performance of a number of classifiers, which used either depth data or extracted skeletons. The results show that both depth data and skeleton based classifiers were able to achieve similar classification accuracy on this dataset (Top-1: around 55%, Top-5: around 90%). Also, using transfer learning in both cases improved the performance

Pdf of the <i>x</i> observable according to purpose.

<p>Blue and circles: leisure oriented dyads. Red and squares: work oriented dyads.</p

Pdf of the <i>y</i> observable according to purpose.

<p>Blue and circles: leisure oriented dyads. Red and squares: work oriented dyads.</p

Group observables.

<p>Group observables.</p