Method of collaborative detection of autonomous transport vehicles based on laser rangefinder data

To master changing performance demands, autonomous transport vehicles are deployed to make inhouse material flow applications more flexible. The socalled cellular transport system consists of a multitude of small scale transport vehicles which shall be able to form a swarm. Therefore the vehicles need to detect each other, exchange information amongst each other and sense their environment. By provision of peripherally acquired information of other transport entities, more convenient decisions can be made in terms of navigation and collision avoidance. This paper is a contribution to collective utilization of sensor data in the swarm of cellular transport vehicles.

Feasibility of Using EMG for Early Detection of the Facial Nerve During Robotic Direct Cochlear Access

HYPOTHESIS Facial nerve monitoring can be used synchronous with a high-precision robotic tool as a functional warning to prevent of a collision of the drill bit with the facial nerve during direct cochlear access (DCA). BACKGROUND Minimally invasive direct cochlear access (DCA) aims to eliminate the need for a mastoidectomy by drilling a small tunnel through the facial recess to the cochlea with the aid of stereotactic tool guidance. Because the procedure is performed in a blind manner, structures such as the facial nerve are at risk. Neuromonitoring is a commonly used tool to help surgeons identify the facial nerve (FN) during routine surgical procedures in the mastoid. Recently, neuromonitoring technology was integrated into a commercially available drill system enabling real-time monitoring of the FN. The objective of this study was to determine if this drilling system could be used to warn of an impending collision with the FN during robot-assisted DCA. MATERIALS AND METHODS The sheep was chosen as a suitable model for this study because of its similarity to the human ear anatomy. The same surgical workflow applicable to human patients was performed in the animal model. Bone screws, serving as reference fiducials, were placed in the skull near the ear canal. The sheep head was imaged using a computed tomographic scanner and segmentation of FN, mastoid, and other relevant structures as well as planning of drilling trajectories was carried out using a dedicated software tool. During the actual procedure, a surgical drill system was connected to a nerve monitor and guided by a custom built robot system. As the planned trajectories were drilled, stimulation and EMG response signals were recorded. A postoperative analysis was achieved after each surgery to determine the actual drilled positions. RESULTS Using the calibrated pose synchronized with the EMG signals, the precise relationship between distance to FN and EMG with 3 different stimulation intensities could be determined for 11 different tunnels drilled in 3 different subjects. CONCLUSION From the results, it was determined that the current implementation of the neuromonitoring system lacks sensitivity and repeatability necessary to be used as a warning device in robotic DCA. We hypothesize that this is primarily because of the stimulation pattern achieved using a noninsulated drill as a stimulating probe. Further work is necessary to determine whether specific changes to the design can improve the sensitivity and specificity.

Appendix A. Whole-rock major, trace and rare earth element analyses of the Ulubey volcanics, Appendix B. Analytical standards and detection limits for analyses

Collisional and post-collisional volcanic rocks in the Ulubey (Ordu) area at the western edge of the Eastern Pontide Tertiary Volcanic Province (EPTVP) in NE Turkey are divided into four suites; Middle Eocene (49.4-44.6 Ma) aged Andesite-Trachyandesite (AT), Trachyandesite-Trachydacite-Rhyolite (TTR), Trachydacite-Dacite (TD) suites, and Middle Miocene (15.1 Ma) aged Trachybasalt (TB) suite. Local stratigraphy in the Ulubey area starts with shallow marine environment sediments of the Paleocene-Eocene time and then continues extensively with sub-aerial andesitic to rhyolitic and rare basaltic volcanism during Eocene and Miocene time, respectively. Petrographically, the volcanic rocks are composed primarily of andesites/trachyandesites, with minor trachydacites/rhyolites, basalts/trachybasalts and pyroclastics, and show porphyric, hyalo-microlitic porphyric and rarely glomeroporphyric, intersertal, intergranular, fluidal and sieve textures. The Ulubey (Ordu) volcanic rocks indicate magma evolution from tholeiitic-alkaline to calc-alkaline with medium-K contents. Primitive mantle normalized trace element and chondrite normalized rare earth element (REE) patterns show that the volcanic rocks have moderate light rare earth element (LREE)/heavy rare earth element (HREE) ratios relative to E-Type MORB and depletion in Nb, Ta and Ti. High Th/Yb ratios indicate parental magma(s) derived from an enriched source formed by mixing of slab and asthenospheric melts previously modified by fluids and sediments from a subduction zone. All of the volcanic rocks share similar incompatible element ratios (e.g., La/Sm, Zr/Nb, La/Nb) and chondrite-normalized REE patterns, indicating that the basic to acidic rocks originated from the same source. The volcanic rocks were produced by the slab dehydration-induced melting of an existing metasomatized mantle source, and the fluids from the slab dehydration introduced significant large ion lithophile element (LILE) and LREE to the source, masking its inherent HFSE-enriched characteristics. The initial 87Sr/86Sr (0.7044-0.7050) and eNd (-0.3 to +3.4) ratios of the volcanics suggest that they originated from an enriched lithospheric mantle source with low Sm/Nd ratios. Integration of the geochemical, petrological and isotopical with regional and local geological data suggest that the Tertiary volcanic rocks from the Ulubey (Ordu) area were derived from an enriched mantle, which had been previously metasomatized by fluids derived from subducted slab during Eocene to Miocene in collisional and post-collisional extension-related geodynamic setting following Late Mesozoic continental collision between the Eurasian plate and the Tauride-Anatolide platform.

Vision-based vehicle detection and tracking with a mobile camera using a statistical framework

En esta tesis se aborda la detección y el seguimiento automático de vehículos mediante técnicas de visión artificial con una cámara monocular embarcada. Este problema ha suscitado un gran interés por parte de la industria automovilística y de la comunidad científica ya que supone el primer paso en aras de la ayuda a la conducción, la prevención de accidentes y, en última instancia, la conducción automática. A pesar de que se le ha dedicado mucho esfuerzo en los últimos años, de momento no se ha encontrado ninguna solución completamente satisfactoria y por lo tanto continúa siendo un tema de investigación abierto. Los principales problemas que plantean la detección y seguimiento mediante visión artificial son la gran variabilidad entre vehículos, un fondo que cambia dinámicamente debido al movimiento de la cámara, y la necesidad de operar en tiempo real. En este contexto, esta tesis propone un marco unificado para la detección y seguimiento de vehículos que afronta los problemas descritos mediante un enfoque estadístico. El marco se compone de tres grandes bloques, i.e., generación de hipótesis, verificación de hipótesis, y seguimiento de vehículos, que se llevan a cabo de manera secuencial. No obstante, se potencia el intercambio de información entre los diferentes bloques con objeto de obtener el máximo grado posible de adaptación a cambios en el entorno y de reducir el coste computacional. Para abordar la primera tarea de generación de hipótesis, se proponen dos métodos complementarios basados respectivamente en el análisis de la apariencia y la geometría de la escena. Para ello resulta especialmente interesante el uso de un dominio transformado en el que se elimina la perspectiva de la imagen original, puesto que este dominio permite una búsqueda rápida dentro de la imagen y por tanto una generación eficiente de hipótesis de localización de los vehículos. Los candidatos finales se obtienen por medio de un marco colaborativo entre el dominio original y el dominio transformado. Para la verificación de hipótesis se adopta un método de aprendizaje supervisado. Así, se evalúan algunos de los métodos de extracción de características más populares y se proponen nuevos descriptores con arreglo al conocimiento de la apariencia de los vehículos. Para evaluar la efectividad en la tarea de clasificación de estos descriptores, y dado que no existen bases de datos públicas que se adapten al problema descrito, se ha generado una nueva base de datos sobre la que se han realizado pruebas masivas. Finalmente, se presenta una metodología para la fusión de los diferentes clasificadores y se plantea una discusión sobre las combinaciones que ofrecen los mejores resultados. El núcleo del marco propuesto está constituido por un método Bayesiano de seguimiento basado en filtros de partículas. Se plantean contribuciones en los tres elementos fundamentales de estos filtros: el algoritmo de inferencia, el modelo dinámico y el modelo de observación. En concreto, se propone el uso de un método de muestreo basado en MCMC que evita el elevado coste computacional de los filtros de partículas tradicionales y por consiguiente permite que el modelado conjunto de múltiples vehículos sea computacionalmente viable. Por otra parte, el dominio transformado mencionado anteriormente permite la definición de un modelo dinámico de velocidad constante ya que se preserva el movimiento suave de los vehículos en autopistas. Por último, se propone un modelo de observación que integra diferentes características. En particular, además de la apariencia de los vehículos, el modelo tiene en cuenta también toda la información recibida de los bloques de procesamiento previos. El método propuesto se ejecuta en tiempo real en un ordenador de propósito general y da unos resultados sobresalientes en comparación con los métodos tradicionales. ABSTRACT This thesis addresses on-road vehicle detection and tracking with a monocular vision system. This problem has attracted the attention of the automotive industry and the research community as it is the first step for driver assistance and collision avoidance systems and for eventual autonomous driving. Although many effort has been devoted to address it in recent years, no satisfactory solution has yet been devised and thus it is an active research issue. The main challenges for vision-based vehicle detection and tracking are the high variability among vehicles, the dynamically changing background due to camera motion and the real-time processing requirement. In this thesis, a unified approach using statistical methods is presented for vehicle detection and tracking that tackles these issues. The approach is divided into three primary tasks, i.e., vehicle hypothesis generation, hypothesis verification, and vehicle tracking, which are performed sequentially. Nevertheless, the exchange of information between processing blocks is fostered so that the maximum degree of adaptation to changes in the environment can be achieved and the computational cost is alleviated. Two complementary strategies are proposed to address the first task, i.e., hypothesis generation, based respectively on appearance and geometry analysis. To this end, the use of a rectified domain in which the perspective is removed from the original image is especially interesting, as it allows for fast image scanning and coarse hypothesis generation. The final vehicle candidates are produced using a collaborative framework between the original and the rectified domains. A supervised classification strategy is adopted for the verification of the hypothesized vehicle locations. In particular, state-of-the-art methods for feature extraction are evaluated and new descriptors are proposed by exploiting the knowledge on vehicle appearance. Due to the lack of appropriate public databases, a new database is generated and the classification performance of the descriptors is extensively tested on it. Finally, a methodology for the fusion of the different classifiers is presented and the best combinations are discussed. The core of the proposed approach is a Bayesian tracking framework using particle filters. Contributions are made on its three key elements: the inference algorithm, the dynamic model and the observation model. In particular, the use of a Markov chain Monte Carlo method is proposed for sampling, which circumvents the exponential complexity increase of traditional particle filters thus making joint multiple vehicle tracking affordable. On the other hand, the aforementioned rectified domain allows for the definition of a constant-velocity dynamic model since it preserves the smooth motion of vehicles in highways. Finally, a multiple-cue observation model is proposed that not only accounts for vehicle appearance but also integrates the available information from the analysis in the previous blocks. The proposed approach is proven to run near real-time in a general purpose PC and to deliver outstanding results compared to traditional methods.

Evaluation of the multi-element capabilities of collision/reaction cell inductively coupled plasma - mass spectrometry in wine analysis

This work explores the multi-element capabilities of inductively coupled plasma - mass spectrometry with collision/reaction cell technology (CCT-ICP-MS) for the simultaneous determination of both spectrally interfered and non-interfered nuclides in wine samples using a single set of experimental conditions. The influence of the cell gas type (i.e. He, He+H2 and He+NH3), cell gas flow rate and sample pre-treatment (i.e. water dilution or acid digestion) on the background-equivalent concentration (BEC) of several nuclides covering the mass range from 7 to 238 u has been studied. Results obtained in this work show that, operating the collision/reaction cell with a compromise cell gas flow rate (i.e. 4 mL min−1) improves BEC values for interfered nuclides without a significant effect on the BECs for non-interfered nuclides, with the exception of the light elements Li and Be. Among the different cell gas mixtures tested, the use of He or He+H2 is preferred over He+NH3 because NH3 generates new spectral interferences. No significant influence of the sample pre-treatment methodology (i.e. dilution or digestion) on the multi-element capabilities of CCT-ICP-MS in the context of simultaneous analysis of interfered and non-interfered nuclides was observed. Nonetheless, sample dilution should be kept at minimum to ensure that light nuclides (e.g. Li and Be) could be quantified in wine. Finally, a direct 5-fold aqueous dilution is recommended for the simultaneous trace and ultra-trace determination of spectrally interfered and non-interfered elements in wine by means of CCT-ICP-MS. The use of the CCT is mandatory for interference-free ultra-trace determination of Ti and Cr. Only Be could not be determined when using the CCT due to a deteriorated limit of detection when compared to conventional ICP-MS.

Assessment of IVHS countermeasures for collision avoidance: rear-end crashes.

Mode of access: Internet.

Multisensorial obstacles detection and classification for ADAS and autonomous driving

A reliable perception of the real world is a key-feature for an autonomous vehicle and the Advanced Driver Assistance Systems (ADAS). Obstacles detection (OD) is one of the main components for the correct reconstruction of the dynamic world. Historical approaches based on stereo vision and other 3D perception technologies (e.g. LIDAR) have been adapted to the ADAS first and autonomous ground vehicles, after, providing excellent results. The obstacles detection is a very broad field and this domain counts a lot of works in the last years. In academic research, it has been clearly established the essential role of these systems to realize active safety systems for accident prevention, reflecting also the innovative systems introduced by industry. These systems need to accurately assess situational criticalities and simultaneously assess awareness of these criticalities by the driver; it requires that the obstacles detection algorithms must be reliable and accurate, providing: a real-time output, a stable and robust representation of the environment and an estimation independent from lighting and weather conditions. Initial systems relied on only one exteroceptive sensor (e.g. radar or laser for ACC and camera for LDW) in addition to proprioceptive sensors such as wheel speed and yaw rate sensors. But, current systems, such as ACC operating at the entire speed range or autonomous braking for collision avoidance, require the use of multiple sensors since individually they can not meet these requirements. It has led the community to move towards the use of a combination of them in order to exploit the benefits of each one. Pedestrians and vehicles detection are ones of the major thrusts in situational criticalities assessment, still remaining an active area of research. ADASs are the most prominent use case of pedestrians and vehicles detection. Vehicles should be equipped with sensing capabilities able to detect and act on objects in dangerous situations, where the driver would not be able to avoid a collision. A full ADAS or autonomous vehicle, with regard to pedestrians and vehicles, would not only include detection but also tracking, orientation, intent analysis, and collision prediction. The system detects obstacles using a probabilistic occupancy grid built from a multi-resolution disparity map. Obstacles classification is based on an AdaBoost SoftCascade trained on Aggregate Channel Features. A final stage of tracking and fusion guarantees stability and robustness to the result.