Stochastic performance modeling and evaluation of obstacle detectability with imaging range sensors

Statistical modeling and evaluation of the performance of obstacle detection systems for Unmanned Ground Vehicles (UGVs) is essential for the design, evaluation, and comparison of sensor systems. In this report, we address this issue for imaging range sensors by dividing the evaluation problem into two levels: quality of the range data itself and quality of the obstacle detection algorithms applied to the range data. We review existing models of the quality of range data from stereo vision and AM-CW LADAR, then use these to derive a new model for the quality of a simple obstacle detection algorithm. This model predicts the probability of detecting obstacles and the probability of false alarms, as a function of the size and distance of the obstacle, the resolution of the sensor, and the level of noise in the range data. We evaluate these models experimentally using range data from stereo image pairs of a gravel road with known obstacles at several distances. The results show that the approach is a promising tool for predicting and evaluating the performance of obstacle detection with imaging range sensors

Unmanned and autonomous ground vehicle

Unmanned and Autonomous Ground Vehicle (UAGV) is a smart vehicle that capable of doing tasks without the need of human operator. The automated vehicle can work during off and on road navigation and also used in military operation such as detecting bombs, border patrol, carrying cargos, search, rescue etc reducing soldier’s exposure to danger, freeing them to perform other duties. This type of vehicle mainly uses sensors to observe the environment and automatically take decisions on its own in unpredictable situation and with unknown information or pass this information to the operator who control the UAGV through various communication when it requires support. This UAGV can send visual feedbacks to the operator at the ground station. An onboard sensor gives the complete environment of the vehicle as signals to the operator

Deep learning-based vessel detection from very high and medium resolution optical satellite images as component of maritime surveillance systems

This thesis presents an end-to-end multiclass vessel detection method from optical satellite images. The proposed workflow covers the complete processing chain and involves rapid image enhancement techniques, the fusion with automatic identification system (AIS) data, and the detection algorithm based on convolutional neural networks (CNN). The algorithms presented are implemented in the form of independent software processors and integrated in an automated processing chain as part of the Earth Observation Maritime Surveillance System (EO-MARISS).In der vorliegenden Arbeit wird eine Methode zur Detektion von Schiffen unterschiedlicher Klassen in optischen Satellitenbildern vorgestellt. Diese gliedert sich in drei aufeinanderfolgende Funktionen: i) die Bildbearbeitung zur Verbesserung der Bildeigenschaften, ii) die Datenfusion mit den Daten des Automatischen Identifikation Systems (AIS) und iii) dem auf „Convolutional Neural Network“ (CNN) basierenden Detektionsalgorithmus. Die vorgestellten Algorithmen wurden in Form eigenständiger Softwareprozessoren implementiert und als Teil des maritimen Erdbeobachtungssystems integriert

Fuzzy reactive piloting for continuous driving of long range autonomous planetary micro-rovers

Abstract — A complete piloting control subsystem for a highly autonomous long range rover will be defined in order to identify the key control functions needed to achieve contin-uous driving. This capability can maximize range and num-ber of interesting scientific sites visited during the limited life time of a planetary rover. To achieve continuous driving, a complete set of techniques have been employed: fuzzy based control, real-time artificial intelligence reasoning, fast and ro-bust rover position estimation based on odometry and angu-lar rate sensing, efficient stereo vision elevation maps based on grids, and fast reaction and planning for obstacle detec-tion and obstacle avoidance based on a simple IF-THEN ex-pert system with fuzzy reasoning. To quickly design and im-plement these techniques, graphical programming has been used to build a fully autonomous piloting system using jus

Roving vehicle motion control Final report

Roving vehicle motion control for unmanned planetary and lunar exploratio

In-situ monitoring of laser powder bed fusion applied to defect detection

Additive manufacturing technologies, particularly laser powder bed fusion (LPBF), have received much attention recently due to their numerous advantages over conventional manufacturing methods. However, the use of LPBF is still quite restricted, mainly due to two factors: its typically low productivity, which makes the technology less competitive in applications with moderate to high production volumes, and its limited reliability, particularly relevant for applications where high performance is required from the materials.The issue of low productivity is addressed in this thesis by adjusting the main LPBF process parameters. An equation for the build rate was formulated based on these parameters, determining their contributions and enabling strategies for build rate maximization. The changes in microstructure and defect populations associated with increasing productivity were determined.The reliability issue was explored by investigating defect formation, detectability and mitigation, since a major factor compromising reliability and materials’ performance is the presence of defects. Internal defects were deliberately created in LPBF-manufactured material to assess their detectability via in-situ monitoring. Two main routes of deliberate defect formation have been identified while preserving defect formation mechanisms; therefore, this thesis can be divided into two parts according to the approach employed to create defects.Defects are generated systematically if suboptimal process parameters are employed. The types, quantities, and sizes of defects in nickel-based alloy Hastelloy X resulting from varying processing conditions were thoroughly characterized. Analyzing data obtained from in-situ monitoring made it possible to distinguish virtually defect-free material from defective material.Defects are generated stochastically due to the redeposition of process by-products on the powder bed. With the aid of in-situ monitoring data, the presence of these defects can be inferred from the detection of the process by-products responsible for their formation. The comparison of data obtained in-situ with data obtained through ex-situ material characterization allowed determining how precisely detections corresponded to actual defects. The impact of these defects on the mechanical properties of Hastelloy X was assessed. A couple of in-process mitigation strategies were investigated, and their performances were evaluated. By establishing means to use LPBF process monitoring to distinguish high-quality from defective material and detect random, unavoidable defects, this thesis enables the prediction of LPBF material quality. It creates conditions necessary for the first-time-right production of defect-free material at increased build rates

A New High-Speed Foreign Fiber Detection System with Machine Vision

A new high-speed foreign fiber detection system with machine vision is proposed for removing foreign fibers from raw cotton using optimal hardware components and appropriate algorithms designing. Starting from a specialized lens of 3-charged couple device (CCD) camera, the system applied digital signal processor (DSP) and field-programmable gate array (FPGA) on image acquisition and processing illuminated by ultraviolet light, so as to identify transparent objects such as polyethylene and polypropylene fabric from cotton tuft flow by virtue of the fluorescent effect, until all foreign fibers that have been blown away safely by compressed air quality can be achieved. An image segmentation algorithm based on fast wavelet transform is proposed to identify block-like foreign fibers, and an improved canny detector is also developed to segment wire-like foreign fibers from raw cotton. The procedure naturally provides color image segmentation method with region growing algorithm for better adaptability. Experiments on a variety of images show that the proposed algorithms can effectively segment foreign fibers from test images under various circumstances