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Technological advancements have created a dependency on robotic systems to meet the daily needs of society. These advancements force an increase in the integration of humans and machines when considering equipment within working environments. Research has shown that current students are more prone to become passive users of engineering technology when compared to previous generations. This trend is...
Robots often operate in built environments containing underlying structure that can be exploited to help predict future observations. In this work, we present a deep learning based approach to predict exit locations of buildings. This technique exploits the inherent structure of buildings to create a model. A convolutional neural network is trained using a database of building blueprints and used...
Recent works of non-rigid registration have shown promising applications on tasks of deformable manipulation. Those approaches use thin plate spline-robust point matching (TPS-RPM) algorithm to regress a transformation function, which could generate a corresponding manipulation trajectory given a new pose/shape of the object. However, this method regards the object as a bunch of discrete and independent...
This paper addresses bird song analysis based on semi-automatic annotation. Research in animal behavior, especially with birds, would be aided by automated (or semiautomated) systems that can localize sounds, measure their timing, and identify their source. This is difficult to achieve in real environments where several birds may be singing from different locations and at the same time. Analysis of...
Nowadays, agricultural and mining industry applications require saving energy in mobile robotic tasks. This critical issue encouraged us to enhance the performance of path tracking controllers during manoeuvring over slippery and rough terrains. In this scenario, we propose probabilistic approaches under machine learning schemes in order to optimally self-tune the controller. The approaches are real...
This paper presents a simulation-based safety training simulator for robot assisted surgery. While adverse events occur rarely during training, they could be fatal to the patients if they happen during real surgical procedures and are not handled properly by the surgical team. In this work we propose a hardware-in-the-loop robotic surgery simulator with high fidelity of the robot motion in a simulated...
In this paper we describe our process for synthesizing frameworks for recognizing student talents in the areas of Computational Thinking (CT) and Engineering Design (ED) from prior research. Computer science education research has resulted in multiple, overlapping definitions of CT as an approach towards solving problems using methods and tools that are derived from computer science. Our development...
Multi-concept visual classification is emerging as a common environment perception technique, with applications in autonomous mobile robot navigation. Supervised visual classifiers are typically trained with large sets of images, hand annotated by humans with region boundary outlines followed by label assignment. This annotation is time consuming, and unfortunately, a change in environment requires...
The development of reliable and robust visual recognition systems is a main challenge towards the deployment of autonomous robotic agents in unconstrained environments. Learning to recognize objects requires image representations that are discriminative to relevant information while being invariant to nuisances, such as scaling, rotations, light and background changes, and so forth. Deep Convolutional...
This paper addresses the problem of road scene segmentation in conventional RGB images by exploiting recent advances in semantic segmentation via convolutional neural networks (CNNs). Segmentation networks are very large and do not currently run at interactive frame rates. To make this technique applicable to robotics we propose several architecture refinements that provide the best trade-off between...
Indoor robot localization systems using wireless signal measurements have gained popularity in recent years, as wireless Local Area Networks can be found practically everywhere. In this field, a popular approach is the use of fingerprinting techniques, such as Gaussian Processes. In our approach, we improve Gaussian Processes based mapping using path loss models as priors. Path loss models encode...
We address the problem of ego-noise reduction, i.e., suppressing the noise a robot causes by its own motions. Such noise degrades the recorded microphone signal massively such that the robot's auditory capabilities suffer. To suppress it, it is intuitive to use also motor data, since it provides additional information about the robot's joints and thereby the noise sources. We propose to fuse motor...
In this manuscript we propose a distributed classifier to perform inference on a person daily behaviour routine, based on multi-modal input data. The model is implemented on a social robot and allows to efficiently fuse locally perceived information with data classified remotely on a cloud. Unlike the dominant multi-class approaches, where each class is classified separately, the multi-label scheme...
Visual localization is the process of finding the location of a camera from the appearance of the images it captures. In this work, we propose an observation model that allows the use of images for particle filter localization. To achieve this, we exploit the capabilities of Gaussian Processes to calculate the likelihood of the observation for any given pose, in contrast to methods which restrict...
Currently, studies on learning relationship between objects focus on the text domain. There are a few researchers who focus on relationship learning between objects in other domains. In these researches, they have tried to represent the qualitative description of structure of objects, and the symbolic relationship between them. This output provides symbolic meaning to the inter-object relationships...
This work introduces a novel artificial intelligence approach to household object recognition. The approach used in this work is feature-based and it works toward recognition under a broad range of circumstances. The necessary image processing techniques are applied to recognize the objects. These techniques include removal of shadow that is segmenting the object from its shadow, extraction of shape...
This paper introduces a new Learning from Demonstration (LfD)-based method that makes usage of robot effector forces and torques recorded during expert demonstrations, to generate force-based haptic guidance reference trajectories on-line, that are intended to be used during haptic shared control for additional operator ‘guidance’. Derived haptic guidance trajectories are superimposed to master-device...
Two human factors studies were conducted to assess the effectiveness of intelligent agents' user interfaces that were designed based on the Situation awareness-based Agent Transparency (SAT) model. Results show that agents' transparency (based on the SAT model) can benefit operator performance and support proper calibration of trust in the agents. Increasing levels of transparency enhanced operator's...
Information technology tools are gaining increasing importance in surgical skill training and assessment, due to the need of objective and repeatable measurement of performance. This is especially true for Minimally Invasive Surgery (MIS), where complex tool manipulation should be mastered employing artificial phantoms and simulators — either in the case of manual or robotic laparoscopy. In this paper,...
Ground penetrating radar (GPR) is used to evaluate deterioration of reinforced concrete bridge decks based on measuring signal attenuation from embedded rebar. The existing methods for obtaining deterioration maps from GPR data often require manual interaction and offsite processing. In this paper, a novel algorithm is presented for automated rebar detection and analysis. We test the process with...
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