A Benchmarking of DCM Based Architectures for Position and Velocity Controlled Walking of Humanoid Robots

This paper contributes towards the development and comparison of Divergent-Component-of-Motion (DCM) based control architectures for humanoid robot locomotion. More precisely, we present and compare several DCM based implementations of a three layer control architecture. From top to bottom, these three layers are here called: trajectory optimization, simplified model control, and whole-body QP control. All layers use the DCM concept to generate references for the layer below. For the simplified model control layer, we present and compare both instantaneous and Receding Horizon Control controllers. For the whole-body QP control layer, we present and compare controllers for position and velocity control robots. Experimental results are carried out on the one-meter tall iCub humanoid robot. We show which implementation of the above control architecture allows the robot to achieve a walking velocity of 0.41 meters per second.Comment: Submitted to Humanoids201

Wild animals' biologging through machine learning models

In recent decades the biodiversity crisis has been characterised by a decline and extinction of many animal species worldwide. To aid in understanding the threats and causes of this demise, conservation scientists rely on remote assessments. Innovation in technology in the form of microelectromechanical systems (MEMs) has brought about great leaps forward in understanding of animal life. The MEMs are now readily available to ecologists for remotely monitoring the activities of wild animals. Since the advent of electronic tags, methods such as biologging are being increasingly applied to the study of animal ecology, providing information unattainable through other techniques. In this paper, we discuss a few relevant instances of biologging studies. We present an overview on biologging research area, describing the evolution of acquisition of behavioural information and the improvement provided by tags. In second part we will review some common data analysis techniques used to identify daily activity of animals

Deep electrical resistivity tomography along the tectonically active Middle Aterno Valley (2009 L'Aquila earthquake area, central Italy)

Pucci et. al.Three 2-D Deep Electrical Resistivity Tomography (ERT) transects, up to 6.36 km long, were obtained across the Paganica-San Demetrio Basin, bounded by the 2009 L'Aquila M-w 6.1 normal-faulting earthquake causative fault (central Italy). The investigations allowed defining for the first time the shallow subsurface basin structure. The resistivity images, and their geological interpretation, show a dissected Mesozoic-Tertiary substratum buried under continental infill of mainly Quaternary age due to the long-term activity of the Paganica-San Demetrio normal faults system (PSDFS), ruling the most recent deformational phase. Our results indicate that the basin bottom deepens up to 600 m moving to the south, with the continental infill largely exceeding the known thickness of the Quaternary sequence. The causes of this increasing thickness can be: (1) the onset of the continental deposition in the southern sector took place before the Quaternary, (2) there was an early stage of the basin development driven by different fault systems that produced a depocentre in the southern sector not related to the present-day basin shape, or (3) the fault system slip rate in the southern sector was faster than in the northern sector. We were able to gain sights into the long-term PSDFS behaviour and evolution, by comparing throw rates at different timescales and discriminating the splays that lead deformation. Some fault splays exhibit large cumulative throws (> 300 m) in coincidence with large displacement of the continental deposits sequence (> 100 m), thus testifying a general persistence in time of their activity as leading splays of the fault system. We evaluate the long-term (3-2.5 Myr) cumulative and Quaternary throw rates of most of the leading splays to be 0.08-0.17 mm yr(-1), indicating a substantial stability of the faults activity. Among them, an individual leading fault splay extends from Paganica to San Demetrio ne' Vestini as a result of a post-Early Pleistocene linkage of two smaller splays. This 15 km long fault splay can explain the Holocene surface ruptures observed to be larger than those occurred during the 2009 L'Aquila earthquake, such as revealed by palaeoseismological investigations. Finally, the architecture of the basin at depth suggests that the PSDFS can also rupture a longer structure at the surface, allowing earthquakes larger than M 6.5, besides rupturing only small sections, as it occurred in 2009.This work was financially supported by Project ‘Fondo per gli Investimenti della Ricerca di Base (FIRB) Abruzzo: High-resolution analyses for assessing the seismic hazard and risk of the areas affected by the 2009 April 6 earthquake’, No. RBAP10ZC8K_005.Peer reviewe

Localizing Tortoise Nests by Neural Networks

The goal of this research is to recognize the nest digging activity of tortoises using a device mounted atop the tortoise carapace. The device classifies tortoise movements in order to discriminate between nest digging, and non-digging activity (specifically walking and eating). Accelerometer data was collected from devices attached to the carapace of a number of tortoises during their two-month nesting period. Our system uses an accelerometer and an activity recognition system (ARS) which is modularly structured using an artificial neural network and an output filter. For the purpose of experiment and comparison, and with the aim of minimizing the computational cost, the artificial neural network has been modelled according to three different architectures based on the input delay neural network (IDNN). We show that the ARS can achieve very high accuracy on segments of data sequences, with an extremely small neural network that can be embedded in programmable low power devices. Given that digging is typically a long activity (up to two hours), the application of ARS on data segments can be repeated over time to set up a reliable and efficient system, called Tortoise@, for digging activity recognition