A lightweight, inexpensive robotic system for insect vision

Designing hardware for miniaturized robotics which mimics the capabilities of flying insects is of interest, because they share similar constraints (i.e. small size, low weight, and low energy consumption). Research in this area aims to enable robots with similarly efficient flight and cognitive abilities. Visual processing is important to flying insects' impressive flight capabilities, but currently, embodiment of insect-like visual systems is limited by the hardware systems available. Suitable hardware is either prohibitively expensive, difficult to reproduce, cannot accurately simulate insect vision characteristics, and/or is too heavy for small robotic platforms. These limitations hamper the development of platforms for embodiment which in turn hampers the progress on understanding of how biological systems fundamentally works. To address this gap, this paper proposes an inexpensive, lightweight robotic system for modelling insect vision. The system is mounted and tested on a robotic platform for mobile applications, and then the camera and insect vision models are evaluated. We analyse the potential of the system for use in embodiment of higher-level visual processes (i.e. motion detection) and also for development of navigation based on vision for robotics in general. Optic flow from sample camera data is calculated and compared to a perfect, simulated bee world showing an excellent resemblance

Evaluación de un programa de intervención prenatal en embarazadas con fetos pequeños para la edad gestacional

La prematuridad y el retraso de crecimiento intrauterino constituyen actualmente los problemas más importantes de la Medicina Fetal y de la Neonatología y son las causas más frecuentes de la morbilidad y mortalidad perinatal en los países desarrollados. OBJETIVO. Valorar la eficacia de un programa de intervención de apoyo prenatal (creado ex-novo) dirigido a madres gestantes de fetos Pequeños para la Edad Gestacional (PEG): detectar si este procedimiento mejora el desarrollo físico y neuroconductual del neonato, el estado emocional de la madre y el vínculo entre ambos. METODOLOGÍA. Estudio quasiexperimental tipo ensayo clínico controlado y sin asignación aleatoria de la intervención realizado en el área Materno-fetal de BCNatal (corporación del Servicio de Medicina Maternofetal del Hospital Clínic y el Hospital Sant Joan de Déu de Barcelona). El tamaño final de la muestra fue de 158 embarazadas, de las cuales 65 formaron parte del grupo intervención y 93 formaron parte del grupo control. RESULTADOS. Al finalizar el programa se observa que el feto y el neonato muestran una mayor ganancia de peso y mayor perímetro craneal en el grupo intervención. En cuanto a las capacidades y competencias del neonato, valoradas con la Escala de Brazelton, los del grupo intervención obtienen unos resultados discretamente superiores en casi todos los parámetros estudiados, destacando una mayor capacidad de habituación ante los estímulos auditivos. En relación a la embarazada, los resultados más relevantes al finalizar el programa son una disminución de la ansiedad (valorada con el cuestionario STAI) y una mayor vinculación afectiva materno-filial (valorada con la escala EVAP). CONCLUSIONES. Para las madres gestantes de fetos PEG, el hecho de haber participado en un programa de intervención de apoyo prenatal tiene un resultado beneficioso para ambos, madre e hijo, presentando menos ansiedad materna, mejores condiciones para establecer el vínculo así como una mejora en el desarrollo físico e indicios de mejores capacidades neuroconductuales en el neonato.Prematurity and intrauterine growth restriction are currently the most important problems in Fetal Medicine and Neonatology and also are the most frequent causes of perinatal morbidity and mortality in developed countries.The Objectives were to evaluate the effectiveness of a prenatal support program (created ex-novo) aimed at pregnant mothers of small fetuses for Gestational Age (PEG): to detect if this procedure improves the physical and neurobehavioral development of the neonate, the emotional state of the mother and the bond between them. This was a quasiexperimental study of a controlled clinical trial and without random assignment of the intervention performed in the Maternal-fetal area of BCNatal (Hospital of the Maternal-Fetal Medicine Service of Hospital Clínic and Sant Joan de Déu Hospital in Barcelona). The final sample size was 158 pregnant women, of whom 65 were part of the intervention group and 93 were part of the control group. At the end of the program, it is observed that the fetus and the neonate show a greater weight gain and greater cranial perimeter in the intervention group. As for the abilities and competences of the newborn, evaluated with the Brazelton Scale, those in the intervention group obtained slightly better results in almost all the studied parameters, emphasizing a greater capacity of habituation before the auditory stimuli. In relation to the pregnant woman, the most relevant results at the end of the program are a reduction of anxiety (valued with the STAI questionnaire) and a greater maternal-filial affective attachment (valued with the EVAP scale). In conclusion, for pregnant mothers of PEG fetuses, having participated in a prenatal support intervention program has a beneficial outcome for both mother and child, with less maternal anxiety, better bonding conditions, and improved development physical and signs of better neurobehavioral abilities in the neonate

Abstract concept learning in a simple neural network inspired by the insect brain

The capacity to learn abstract concepts such as 'sameness' and 'difference' is considered a higher-order cognitive function, typically thought to be dependent on top-down neocortical processing. It is therefore surprising that honey bees apparantly have this capacity. Here we report a model of the structures of the honey bee brain that can learn sameness and difference, as well as a range of complex and simple associative learning tasks. Our model is constrained by the known connections and properties of the mushroom body, including the protocerebral tract, and provides a good fit to the learning rates and performances of real bees in all tasks, including learning sameness and difference. The model proposes a novel mechanism for learning the abstract concepts of 'sameness' and 'difference' that is compatible with the insect brain, and is not dependent on top-down or executive control processing

Fuzzy Logic Unmanned Air Vehicle Motion Planning

There are a variety of scenarios in which the mission objectives rely on an unmanned aerial vehicle (UAV) being capable of maneuvering in an environment containing obstacles in which there is little prior knowledge of the surroundings. With an appropriate dynamic motion planning algorithm, UAVs would be able to maneuver in any unknown environment towards a target in real time. This paper presents a methodology for two-dimensional motion planning of a UAV using fuzzy logic. The fuzzy inference system takes information in real time about obstacles (if within the agent's sensing range) and target location and outputs a change in heading angle and speed. The FL controller was validated, and Monte Carlo testing was completed to evaluate the performance. Not only was the path traversed by the UAV often the exact path computed using an optimal method, the low failure rate makes the fuzzy logic controller (FLC) feasible for exploration. The FLC showed only a total of 3% failure rate, whereas an artificial potential field (APF) solution, a commonly used intelligent control method, had an average of 18% failure rate. These results highlighted one of the advantages of the FLC method: its adaptability to complex scenarios while maintaining low control effort

Characterisation and upgrade of the communication between overhead controllers and Kilobots

<div> <div> <div> <p>This study consists in (i) characterising the infrared (IR) communication between the Kilobot robots and the overhead controller (OHC), (ii) identifying potential communication problems and (iii) designing solutions to them. Typically Kilobots operate in large environments, therefore a large OHC communication range is necessary (e.g., to begin or stop an experiment). We investigated how the communication range is influenced by the surface material (glass vs. whiteboard), the ambient light and the type of LEDs on the OHC. The material that maximises the communication range is the whiteboard (Perspexà Frost matt acrylic, color Moonlight White S2 1T41) which also reduces the reflections from the fluorescent lights. Additionally, our study shows that the ambient light influences the communication range; as such, we designed and implemented an external IR light device that considerably improves the communication. Finally, to reach a large area of coverage, we designed and implemented a new OHC board. The board is equipped with the infrared LEDs (TSAL6200) that gave the best performance in the performed tests. In this report, we provide the results of our tests and the design of the new boards. </p> </div> </div> </div

A Computational model of the integration of landmarks and motion in the insect central complex

The insect central complex (CX) is an enigmatic structure whose computational function has evaded inquiry, but has been implicated in a wide range of behaviours. Recent experimental evidence from the fruit fly (Drosophila melanogaster) and the cockroach (Blaberus discoidalis) has demonstrated the existence of neural activity corresponding to the animal's orientation within a virtual arena (a neural 'compass'), and this provides an insight into one component of the CX structure. There are two key features of the compass activity: an offset between the angle represented by the compass and the true angular position of visual features in the arena, and the remapping of the 270° visual arena onto an entire circle of neurons in the compass. Here we present a computational model which can reproduce this experimental evidence in detail, and predicts the computational mechanisms that underlie the data. We predict that both the offset and remapping of the fly's orientation onto the neural compass can be explained by plasticity in the synaptic weights between segments of the visual field and the neurons representing orientation. Furthermore, we predict that this learning is reliant on the existence of neural pathways that detect rotational motion across the whole visual field and uses this rotation signal to drive the rotation of activity in a neural ring attractor. Our model also reproduces the 'transitioning' between visual landmarks seen when rotationally symmetric landmarks are presented. This model can provide the basis for further investigation into the role of the central complex, which promises to be a key structure for understanding insect behaviour, as well as suggesting approaches towards creating fully autonomous robotic agents.19 page(s