Localisation of humans, objects and robots interacting on load-sensing floors

International audienceLocalisation, tracking and recognition of objects and humans are basic tasks that are of high value in applications of ambient intelligence. Sensing floors were introduced to address these tasks in a non-intrusive way. To recognize the humans moving on the floor, they are usually first localized, and then a set of gait features are extracted (stride length, cadence, pressure profile over a footstep). However, recognition generally fails when several people stand or walk together, preventing successful tracking. This paper presents a detection, tracking and recognition technique which uses objects' weight. It continues working even when tracking individual persons becomes impossible. Inspired by computer vision, this technique processes the floor pressure-image by segmenting the blobs containing objects, tracking them, and recognizing their contents through a mix of inference and combinatorial search. The result lists the probabilities of assignments of known objects to observed blobs. The concept was successfully evaluated in daily life activity scenarii, involving multi-object tracking and recognition on low resolution sensors, crossing of user trajectories, and weight ambiguity. This technique can be used to provide a probabilistic input for multi-modal object tracking and recognition systems

Design and development of triangular, spiral, and fractal antennas for radio frequency identification tags

This dissertation reports on the design and development of three compact, non-meandered microstrip patch antennas for ultra high frequency (UHF) radio frequency identification (RFID) applications. The monopole antennas considered in this work are an inset-fed triangular antenna, one arm Archimedes spiral antenna and a Half-Sierpinski fractal antenna. These antennas with small length to width ratios (\u3c 2/1), can be the preferred choice, in the tagging of small size consumer end products, over the ubiquitous meandered dipole antenna (length/width \u3e 5/1), which is often the antenna of choice, due to its high gain for UHF RFID applications. The lengths and widths of all three antennas are less than 5.5 cm. Earlier reports of planar antennas for RFID applications in the UHF range have lengths larger than 9 cm on one side or are developed on a rigid substrate. All three antennas have a surface area of about 30 cm2 and are designed for a flexible polyimide substrate. The new antennas satisfy the requirement of a voltage standing wave ratio (VSWR) \u3c 2 and exhibit a gain close to or greater than 0 dBi at the operation frequency of 915 MHz. All three antennas have a return-loss less than -10 dB at 915 MHz and a -10 dB bandwidth greater than 12 MHz. While the triangular and spiral antennas display peak gains of over 2 dBi, the fractal antenna has a gain close to 0 dBi (-0.64 dBi). The effect of ground geometry on the radiation performance of the antennas has been analyzed using ANSOFT Designer software. Slots, aligned to the top patch were introduced in the antenna ground plane to increase the gain of the antennas. The fabricated and tested antennas were then employed in the transmission-delay-line-based passive radio-frequency identification tag. The location of the antenna with respect to the transmission line on the tag was found to affect the radiation pattern of the antenna. A circular disc monopole antenna having a gain of 8.88 dBi and having a -10 dB bandwidth greater than 300 MHz was employed to transmit and receive the interrogating and back-scattered signals, respectively. The generation of bits, employing On-Off Keying (OOK) modulation technique was successfully demonstrated. The tag, fabricated with the triangular antenna is found to perform the best

Fusion de données hétérogènes pour la perception de l'homme par robot mobile

Ces travaux de thèse s'inscrivent dans le cadre du projet européen CommRob impliquant des partenaires académiques et industriels. Le but du projet est la conception d'un robot compagnon évoluant en milieu structuré, dynamique et fortement encombré par la présence d'autres agents partageant l'espace (autres robots, humains). Dans ce cadre, notre contribution porte plus spécifiquement sur la perception multimodale des usagers du robot (utilisateur et passants). La perception multimodale porte sur le développement et l'intégration de fonctions perceptuelles pour la détection, l'identification de personnes et l'analyse spatio-temporelle de leurs déplacements afin de communiquer avec le robot. La détection proximale des usagers du robot s'appuie sur une perception multimodale couplant des données hétérogènes issues de différents capteurs. Les humains détectés puis reconnus sont alors suivis dans le flot vidéo délivré par une caméra embarquée afin d'en interpréter leurs déplacements. Une première contribution réside dans la mise en place de fonctions de détection et d'identification de personnes depuis un robot mobile. Une deuxième contribution concerne l'analyse spatio-temporelle de ces percepts pour le suivi de l'utilisateur dans un premier temps, de l'ensemble des personnes situées aux alentours du robot dans un deuxième temps. Enfin, dans le sens des exigences de la robotique, la thèse comporte deux volets : un volet formel et algorithmique qui tire pertinence et validation d'un fort volet expérimental et intégratif. Ces développements s'appuient sur notre plateforme Rackham et celle mise en œuvre durant le projet CommRob.This work has been realized under the CommRob European project involving several academic and industrial partners. The goal of this project is to build a robot companion able to act in structured and dynamic environments cluttered by other agents (robots and humans). In this context, our contribution is related to multimodal perception of humans from the robot (users and passers-by). The multimodal perception induces the development and integration of perceptual functions able to detect, to identify the people and to track the motions in order to communicate with the robot. Proximal detection of the robot's users uses a multimodal perception framework based on heterogeneous data fusion from different sensors. The detected and identified users are then tracked in the video stream extracted from the embedded camera in order to interpret the human motions. The first contribution is related to the definition of perceptual functions for detecting and identifying humans from a mobile robot. The second contribution concerns the spatio-temporal analysis of these percepts for user tracking. Then, this work is extended to multi-target tracking dedicated to the passers by. Finally, as it is frequently done in robotics, our work contains two main topics: on one hand the approaches are formalized; on the other hand, these approaches are integrated and validated through live experiments. All the developments done during this thesis has been integrated on our platform Rackham and on the CommRob platform too

４Ｗ１Ｈ に着目し抽象化したロボットサービス開発方式の研究

芝浦工業大学201