ROBUST project: Control Framework for Deep Sea Mining Exploration

This paper presents the control framework under development within the ROBUST Horizon 2020 project, whose goal is the development of an autonomous robotic system for the exploration of deep-sea mining sites. After a bathymetric survey of the initial zone of interest, the robotized system selects a subarea deemed to have the most chances of containing a manganese nodule field and proceeds with a detailed low altitude survey. Whenever a possible nodule is found, it performs an insitu measurement through laser induced spectroscopy. To do so, the underwater vehicle must first land on the seafloor, with a certain precision to allow a subsequent fixed-based manipulation, bringing its manipulator endowed with the laser system in the position to carry out the measurement. The work reports the developed control architecture and the simulation results supporting it

Event-based Classification with Recurrent Spiking Neural Networks on Low-end Micro-Controller Units

Due to its intrinsic sparsity both in time and space, event-based data is optimally suited for edge-computing applications that require low power and low latency. Time varying signals encoded with this data representation are best processed with Spiking Neural Networks (SNN). In particular, recurrent SNNs (RSNNs) can solve temporal tasks using a relatively low number of parameters, and therefore support their hardware implementation in resource-constrained computing architectures. These premises propel the need of exploring the properties of these kinds of structures on low-power processing systems to test their limits both in terms of computational accuracy and resource consumption, without having to resort to full-custom implementations. In this work, we implemented an RSNN model on a low-end, resource-constrained ARM-Cortex-M4-based Micro Controller Unit (MCU). We trained it on a down-sampled version of the N-MNIST event-based dataset for digit recognition as an example to assess its performance in the inference phase. With an accuracy of 97.2%, the implementation has an average energy consumption as low as 4.1μJ and a worst-case computational time of 150.4μs per time-step with an operating frequency of 180 MHz, so the deployment of RSNNs on MCU devices is a feasible option for small image vision real-time tasks

Reactive direction control for a mobile robot: A locust-like control of escape direction emerges when a bilateral pair of model locust visual neurons are integrated

Locusts possess a bilateral pair of uniquely identifiable visual neurons that respond vigorously to the image of an approaching object. These neurons are called the lobula giant movement detectors (LGMDs). The locust LGMDs have been extensively studied and this has lead to the development of an LGMD model for use as an artificial collision detector in robotic applications. To date, robots have been equipped with only a single, central artificial LGMD sensor, and this triggers a non-directional stop or rotation when a potentially colliding object is detected. Clearly, for a robot to behave autonomously, it must react differently to stimuli approaching from different directions. In this study, we implement a bilateral pair of LGMD models in Khepera robots equipped with normal and panoramic cameras. We integrate the responses of these LGMD models using methodologies inspired by research on escape direction control in cockroaches. Using ‘randomised winner-take-all’ or ‘steering wheel’ algorithms for LGMD model integration, the khepera robots could escape an approaching threat in real time and with a similar distribution of escape directions as real locusts. We also found that by optimising these algorithms, we could use them to integrate the left and right DCMD responses of real jumping locusts offline and reproduce the actual escape directions that the locusts took in a particular trial. Our results significantly advance the development of an artificial collision detection and evasion system based on the locust LGMD by allowing it reactive control over robot behaviour. The success of this approach may also indicate some important areas to be pursued in future biological research

Hardware calibrated learning to compensate heterogeneity in analog RRAM-based Spiking Neural Networks

Spiking Neural Networks (SNNs) can unleash the full power of analog Resistive Random Access Memories (RRAMs) based circuits for low power signal processing. Their inherent computational sparsity naturally results in energy efficiency benefits. The main challenge implementing robust SNNs is the intrinsic variability (heterogeneity) of both analog CMOS circuits and RRAM technology. In this work, we assessed the performance and variability of RRAM-based neuromorphic circuits that were designed and fabricated using a 130 nm technology node. Based on these results, we propose a Neuromorphic Hardware Calibrated (NHC) SNN, where the learning circuits are calibrated on the measured data. We show that by taking into account the measured heterogeneity characteristics in the off-chip learning phase, the NHC SNN self-corrects its hardware non-idealities and learns to solve benchmark tasks with high accuracy. This work demonstrates how to cope with the heterogeneity of neurons and synapses for increasing classification accuracy in temporal tasks

DexROV: Enabling effective dexterous ROV operations in presence of communication latency

Subsea interventions in the oil & gas industry as well as in other domains such as archaeology or geological surveys are demanding and costly activities for which robotic solutions are often deployed in addition or in substitution to human divers - contributing to risks and costs cutting. The operation of ROVs (Remotely Operated Vehicles) nevertheless requires significant off-shore dedicated manpower to handle and operate the robotic platform and the supporting vessel. In order to reduce the footprint of operations, DexROV proposes to implement and evaluate novel operation paradigms with safer, more cost effective and time efficient ROV operations. As a keystone of the proposed approach, manned support will in a large extent be delocalized within an onshore ROV control center, possibly at a large distance from the actual operations, relying on satellite communications. The proposed scheme also makes provision for advanced dexterous manipulation and semi-autonomous capabilities, leveraging human expertise when deemed useful. The outcomes of the project will be integrated and evaluated in a series of tests and evaluation campaigns, culminating with a realistic deep sea (1,300 meters) trial in the Mediterranean sea

Optimal solid state neurons

Bioelectronic medicine is driving the need for neuromorphic microcircuits that integrate raw nervous stimuli and respond identically to biological neurons. However, designing such circuits remains a challenge. Here we estimate the parameters of highly nonlinear conductance models and derive the ab initio equations of intracellular currents and membrane voltages embodied in analog solid-state electronics. By configuring individual ion channels of solid-state neurons with parameters estimated from large-scale assimilation of electrophysiological recordings, we successfully transfer the complete dynamics of hippocampal and respiratory neurons in silico. The solid-state neurons are found to respond nearly identically to biological neurons under stimulation by a wide range of current injection protocols. The optimization of nonlinear models demonstrates a powerful method for programming analog electronic circuits. This approach offers a route for repairing diseased biocircuits and emulating their function with biomedical implants that can adapt to biofeedback.ISSN:2041-172

Non-Linear Neuronal Responses as an Emergent Property of Afferent Networks: A Case Study of the Locust Lobula Giant Movement Detector

In principle it appears advantageous for single neurons to perform non-linear operations. Indeed it has been reported that some neurons show signatures of such operations in their electrophysiological response. A particular case in point is the Lobula Giant Movement Detector (LGMD) neuron of the locust, which is reported to locally perform a functional multiplication. Given the wide ramifications of this suggestion with respect to our understanding of neuronal computations, it is essential that this interpretation of the LGMD as a local multiplication unit is thoroughly tested. Here we evaluate an alternative model that tests the hypothesis that the non-linear responses of the LGMD neuron emerge from the interactions of many neurons in the opto-motor processing structure of the locust. We show, by exposing our model to standard LGMD stimulation protocols, that the properties of the LGMD that were seen as a hallmark of local non-linear operations can be explained as emerging from the dynamics of the pre-synaptic network. Moreover, we demonstrate that these properties strongly depend on the details of the synaptic projections from the medulla to the LGMD. From these observations we deduce a number of testable predictions. To assess the real-time properties of our model we applied it to a high-speed robot. These robot results show that our model of the locust opto-motor system is able to reliably stabilize the movement trajectory of the robot and can robustly support collision avoidance. In addition, these behavioural experiments suggest that the emergent non-linear responses of the LGMD neuron enhance the system's collision detection acuity. We show how all reported properties of this neuron are consistently reproduced by this alternative model, and how they emerge from the overall opto-motor processing structure of the locust. Hence, our results propose an alternative view on neuronal computation that emphasizes the network properties as opposed to the local transformations that can be performed by single neurons

Estudio aerobiológico de la diversidad polínica y su potencial alergénico en el oasis del sur de Mendoza, Argentina

Los conocimientos provenientes del campo de estudio de la Aerobiología favorecen el análisis inmunológico de los alérgenos atmosféricos procedentes de polen y esporas fúngicas. Esto posibilita conocer la carga alergénica del aire en el ambiente y de esta manera, valorar mejor la relación exposición / reacción / clínica en los pacientes en tratamiento por alergias. No existen estudios previos de este tema realizados a nivel regional ni provincial en Mendoza. Una base de datos de identificación de posibles alérgenos provenientes de la polinización de espacios verdes urbanos en la ciudad de San Rafael y General Alvear contribuye a la epidemiología ambiental sobre las afecciones alérgicas respiratorias inducidas por polen y esporas. En esta presentación damos a conocer un proyecto de investigación en Aerobiología, con el fin de generar conocimiento aerobiológico de la zona urbana del oasis del sur mendocino (San Rafael y General Alvear), que contribuye a conocer la carga alergénica proveniente de granos de polen y esporas presentes en el ambiente. Para ello, se están llevando a cabo tres líneas de trabajo que consisten en: (1) el relevamiento, localización y mapeo de la vegetación urbana en floración, (2) la elaboración de una colección de referencia palinológica, y (3) el muestreo diario de aeropartículas atmosféricas urbanas. Se presentan los resultados preliminares obtenidos desde el inicio del proyecto y se muestran las líneas de trabajo que seguirá el curso de esta investigación. A futuro, los estudios aerobiológicos permitirían el desarrollo de programas de seguimiento, prevención y control en los índices de la cantidad de polen y esporas presentes en la atmósfera. Esta herramienta puede describir el potencial alergénico en espacios urbanos sus perjuicios ambientales. De esta manera, una investigación con estas características puede ser un aporte directo a la formulación de políticas de salud pública y planificación urbana de la ciudad.Fil: Guerci, Alejandra. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; Argentina. Museo Municipal de Historia Natural San Rafael - Unidad Asociada al CCT Mendoza; Argentina. Instituto de Enseñanza Superior 9-011 del Atuel; ArgentinaFil: Rojo, Leandro David. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; Argentina. Museo Municipal de Historia Natural San Rafael - Unidad Asociada al CCT Mendoza; ArgentinaFil: Indiveri, Martina. Gobierno de la Provincia de Mendoza. Hospital Teodoro Schestakow.; ArgentinaFil: Nuñez Sada, Maria Florencia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Farina, Lucia. Museo Municipal de Historia Natural San Rafael - Unidad Asociada al CCT Mendoza; ArgentinaFil: Aguilar, Mariano. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; Argentina. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Llano, Carina Lourdes. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Mendoza; ArgentinaFil: Lucero, A.. Universidad de Mendoza; ArgentinaFil: Negreira, Gabriel Alfredo. Instituto de Enseñanza Superior 9-011 del Atuel; ArgentinaFil: Vazquez, Maria Soledad. Universidad Tecnologica Nacional. Facultad Reg.san Rafael. Instituto de Evolucion, Ecologia Historica y Ambiente. - Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Mendoza. Instituto de Evolucion, Ecologia Historica y Ambiente.; ArgentinaFil: Rodriguez, L. F.. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Gallardo, C. A.. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Giraudo, S. B.. Museo Municipal de Historia Natural San Rafael - Unidad Asociada al CCT Mendoza; ArgentinaXIV Encuentro del Centro Internacional de Ciencias de la TierraSan RafaelArgentinaCentro Internacional para Estudios de la TierraComisión Nacional de Energía AtómicaUniversidad Nacional de CuyoUniversidad Tecnológica Nacional. Facultad Regional San Rafae