Search results for " Rob"
showing 10 items of 689 documents
Modeling the insect mushroom bodies: application to a delayed match-to-sample task.
2013
Despite their small brains, insects show advanced capabilities in learning and task solving. Flies, honeybees and ants are becoming a reference point in neuroscience and a main source of inspiration for autonomous robot design issues and control algorithms. In particular, honeybees demonstrate to be able to autonomously abstract complex associations and apply them in tasks involving different sensory modalities within the insect brain. Mushroom Bodies (MBs) are worthy of primary attention for understanding memory and learning functions in insects. In fact, even if their main role regards olfactory conditioning, they are involved in many behavioral achievements and learning capabilities, as …
A Segmentation System for Soccer Robot Based on Neural Networks
2000
An innovative technique for segmentation of color images is proposed. The technique implements an approach based on thresholding of the hue histogram and a feed-forward neural network that learns to recognize the hue ranges of meaningful objects. A new function for detecting valleys of the histogram has been devised and tested. A novel blurring algorithm for noise reduction that works effectively when used over hue image has been employed. The reported experimental results show that the technique is reliable and robust even in presence of changing environmental conditions. Extended experimentation has been carried on the framework of the Robot Soccer World Cup Initiative (RoboCup).
State classification for autonomous gas sample taking using deep convolutional neural networks
2017
Despite recent rapid advances and successful large-scale application of deep Convolutional Neural Networks (CNNs) using image, video, sound, text and time-series data, its adoption within the oil and gas industry in particular have been sparse. In this paper, we initially present an overview of opportunities for deep CNN methods within oil and gas industry, followed by details on a novel development where deep CNN have been used for state classification of autonomous gas sample taking procedure utilizing an industrial robot. The experimental results — using a deep CNN containing six layers — show accuracy levels exceeding 99 %. In addition, the advantages of using parallel computing with GP…
Pose classification using support vector machines
2000
In this work a software architecture is presented for the automatic recognition of human arm poses. Our research has been carried on in the robotics framework. A mobile robot that has to find its path to the goal in a partially structured environment can be trained by a human operator to follow particular routes in order to perform its task quickly. The system is able to recognize and classify some different poses of the operator's arms as direction commands like "turn-left", "turn-right", "go-straight", and so on. A binary image of the operator silhouette is obtained from the gray-level input. Next, a slice centered on the silhouette itself is processed in order to compute the eigenvalues …
A Neural Solution for a Mobile Robot Navigation into Unknown Indoor Environments Using Visual Landmarks
1998
In this paper we present a neural solution for a mobile robot navigation into unknown indoor environments by using landmarks. Robot navigation task is implemented by two groups of processes based on MLP neural networks classifiers: a Low Level Vision System performs obstacle avoidance and corridor following, while an High Level Vision System extracts landmarks contents and performs goal directed navigation. A path-planner manages the two navigation systems and interacts with the robot hardware. The proposed solution is very strong and flexible and can be used to drive a mobile robot in real indoor environments. In the paper experimental results are also reported.
A spiking network for body size learning inspired by the fruit fly
2013
The concept of peripersonal space is an interesting research topics for psychologists, neurobiologists and for robotic applications. A living being can learn the representation of its own body to take the correct behavioral decision when interacting with the world. To transfer these important learning mechanisms on bio-robots, simple and efficient solutions can be found in the insect world. In this paper a neural-based model for body-size learning is proposed taking into account the results obtained in experiments with fruit flies. Simulations and experimental results on a roving platform are reported and compared with the biological counterpart.
A Feed-Forward Neural Network for Robust Segmentation of Color Images
1999
A novel approach for segmentation of color images is proposed. The approach is based on a feed-forward neural network that learns to recognize the hue range of meaningful objects. Experimental results showed that the proposed method is effective and robust even in presence of changing environmental conditions. The described technique has been tested in the framework of the Robot Soccer World Cup Initiative (RoboCup). The approach is fully general and it may be successfully employed in any intermediate level image-processing task, where the color is a meaningful descriptor.
EARLINET observations of the 14-22-may long-range dust transport event during SAMUM 2006: validation of results from dust transport modelling
2009
We observed a long-range transport event of mineral dust from North Africa to South Europe during the Saharan Mineral Dust Experiment (SAMUM) 2006. Geometrical and optical properties of that dust plume were determined with Sun photometer of the Aerosol Robotic Network (AERONET) and Raman lidar near the North African source region, and with Sun photometers of AERONET and lidars of the European Aerosol Research Lidar Network (EARLINET) in the far field in Europe. Extinction-to-backscatter ratios of the dust plume over Morocco and Southern Europe do not differ. Ångstr¨om exponents increase with distance from Morocco. We simulated the transport, and geometrical and optical properties of the dus…
Robotic path planning for non-destructive testing through RoboNDT
2015
The requirement to increase inspection speeds for non-destructive testing (NDT) is common to many manufacturers. The prevalence of complex curved surfaces in modern products provides motivation for the use of 6 axis robots in these inspections. The techniques and issues associated with conventional manual inspection techniques and automated systems for the inspection of large complex surfaces were reviewed. This paper presents a new MATLAB based software solution (RoboNDT), aiming to fulfil the requirements of robotized NDT inspection. RoboNDT enables flexible trajectory path planning to be accomplished for the inspection of complex curved surfaces. This newly developed software is capable …
Eiropas Savienības ekonomiskā attīstība: nozaru un reģionu dimensijas
2020
Šajā darbā tiek pētīta investīciju efektivitāte ražošanas nozarēs un ekonomiskās izaugsmes faktori Eiropas Savienībā NUTS2 reģionu līmenī laika posmā no 2002. līdz 2018. gadam. Autors izmanto DEA un SFA metodes, lai izpētītu bruto pamatkapitāla veidošanas izdevumu efektivitāti, kā arī BMA, lai atrastu ekonomiskas izaugsmei noteicošos faktorus. Rezultāti liecina, ka daži ES reģioni un valstis, ieskaitot Latviju, varētu uzlabot KPV uz vienu nodarbināto, paaugstinot resursu izmantošanas efektivitāti. Darbā arī tiek atrasta nosacītās konverģences esamība, kā arī ekonomikas izaugsmes noteicošie faktori: izglītības kvalitāte, angļu valodas prasme, ICT patentu skaits un lielāks rūpniecības nozares…