A Social Approach for Target Localization: Simulation and Implementation in the marXbot Robot

Foraging is a common benchmark problem in collective robotics in which a robot (the forager) explores a given environment while collecting items for further deposition at specific locations. A typical real-world application of foraging is garbage collection where robots collect garbage for further disposal in pre-defined locations. This work proposes a method to cooperatively perform the task of finding such locations: instead of using local or global localization strategies relying on pre-installed infrastructure, the proposed approach takes advantage of the knowledge gathered by a population about the localization of the targets. In our approach, robots communicate in an intrinsic way the estimation about how near they are from a target; these estimations are used by neighbour robots for estimating their proximity, and for guiding the navigation of the whole population when looking for these specific areas. We performed several tests in a simulator, and we validated our approach on a population of real robots. For the validation tests we used a mobile robot called marXbot. In both cases (i.e., simulation and implementation on real robots), we found that the proposed approach efficiently guides the robots towards the pre-specified targets while allowing the modulation of their speed

Augmenting a convolutional neural network with local histograms ::a case study in crop classification from high-resolution UAV imagery

The advent of affordable drones capable of taking high resolution images of agricultural fields creates new challenges and opportunities in aerial scene understanding. This paper tackles the problem of recognizing crop types from aerial imagery and proposes a new hybrid neural network architecture which combines histograms and convolutional units. We evaluate the performance of the proposed model on a 23-class classification task and compare it to other models. The result is an improvement of the classification performance

Exploring internal representations of deep neural networks

This paper introduces a method for the generation of images that activate any target neuron or group of neurons of a trained convolutional neural network (CNN). These images are created in such a way that they contain attributes of natural images such as color patterns or textures. The main idea of the method is to pre-train a deep generative network on a dataset of natural images and then use this network to generate images for the target CNN. The analysis of the generated images allows for a better understanding of the CNN internal representations, the detection of otherwise unseen biases, or the creation of explanations through feature localization and description

EvoBoids ::co-design of a physical and virtual game using artificial evolution

We present the co-design of a gaming scenario between an Artificial Evolution algorithm and a human designer. Such co-design is twofold, consisting of an initial stage in which a genetic algorithm is used to evolve the control parameters that define the behavior of a group of virtual agents. This produces interesting and unexpected results not only creating differentiated behaviors but also increasing the flexibility of the character to adapt to a given objective. In the second stage of the game design a human interplay was introduced in adding other elements to the game, such as other characters and new game dynamics. In this paper we introduce a game that integrates virtual and physical characters while taking advantage of such co-design approach. The physical character consists of a robot which controlled through a Natural User Interface can be part of the game by interacting with other characters in the virtual environment

Ubichip, Ubidule, and MarXbot ::a hardware platform for the simulation of complex systems

This paper presents the final hardware platform developed in the Perplexus project. This platform is composed of a reconfigurable device called the ubichip, which is embedded on a pervasive platform called the ubidule, and can also be integrated on the marXbot robotic platform. The whole platform is intended to provide a hardware platform for the simulation of complex systems, and some examples of them are presented at the end of the paper

Analysis of Andean blackberry (Rubus glaucus) production models obtained by means of artificial neural networks exploiting information collected by small-scale growers in Colombia and publicly available meteorological data ::a case study of lulo (Solanum quitoense), an under-researched Andean fruit

The Andean blackberry (Rubus glaucus) is an important source of income in hillside regions of Colombia. However, growers have little reliable information on the factors that affect the development and yield of the crop, and therefore there is a dearth of information on how to effectively manage the crop. Site specific information recorded by small-scale producers of the Andean blackberry on their production systems and soils coupled with publicly available meteorological data was used to develop models of such production systems. Multilayer perceptrons and Self-Organizing Maps were used as computational models in the identification and visualization of the most important variables for modeling the production of Andean blackberry. Artificial neural networks were trained with information from 20 sites in Colombia where the Andean blackberry is cultivated. Multilayer perceptrons predicted with a reasonable degree of accuracy the production response of the crop. The soil depth, the average temperature, external drainage, and the accumulated precipitation of the first month before harvest were critical determinants of productivity. A proxy variable of location was used to describe overall differences in management between farmers groups. The use of this proxy indicated that, even under essentially similar environmental conditions, large differences in production could be assigned to management effects. The information obtained can be used to determine sites that are suitable for Andean blackberry production, and to transfer of management practices from sites of high productivity to sites with similar environmental conditions which currently have lower levels of productivity

Interpretation of commercial production information ::a case study of lulo (Solanum quitoense), an under-researched Andean fruit

Every time a farmer plants and harvests a crop represents a unique event or experiment. Our premise is that if it were possible to characterize the production system in terms of management and the environmental conditions, and if information on the harvested product were collected from a large number of harvesting events under varied conditions, it should be possible to develop data-driven models that describe the production system. These models can then be used to identify appropriate growing conditions and improved management practices for crops that have received little attention from researchers. The analysis and interpretation of commercial production data in the context of naturally occurring variation in environmental and management, as opposed to controlled experimental data, requires novel approaches. Information was available on both variation in commercial production of the tropical fruit, lulo (Solanum quitoense), and the associated environmental conditions in Colombia. This information was used to develop and evaluate procedures for the interpretation of the variation in commercial production of lulo. The most effective procedures depended on expert guidance: it was not possible to develop a simple effective one step procedure, but rather an iterative approach was required. The most effective procedure was based on the following steps. First, highly correlated independent variables were evaluated and those that were effectively duplicates were eliminated. Second, regression models identified those environmental factors most closely associated with the dependent variable of fruit yield. The environmental factors associated with variation in fruit yield were then used for more in depth analysis, and those environmental variables not associated with yield were excluded from further analysis. Linear regression and multilayer perceptron regression models explained 65–70% of the total variation in yield. Both models identified three of the same factors but the multilayer perceptron based on a neural network identified one location as an additional factor. Third, the three environmental factors common to both regression models were used to define three Homogeneous Environmental Conditions (HECs) using Self-Organizing Maps (SOM). Fourth, yield was analyzed with a mixed model with the categorical variables of HEC, location, as a proxy for cultural factors associated with a geographic region, and farm as proxy for management skills. The mixed model explained more than 80% of the total variation in yield with 61% associated with the HECs and 19% with farm. Location had minimal effects. The results of this model can be used to determine the appropriate environmental conditions for obtaining high yields for crops where only commercial data are available, and also to identify those farms that have superior management practices for given environmental conditions