Shared control of an aerial cooperative transportation system with a cable-suspended payload

This paper presents a novel bilateral shared framework for a cooperative aerial transportation and manipulation system composed by a team of micro aerial vehicles with a cable-suspended payload. The human operator is in charge of steering the payload and he/she can also change online the desired shape of the formation of robots. At the same time, an obstacle avoidance algorithm is in charge of avoiding collisions with the static environment. The signals from the user and from the obstacle avoidance are blended together in the trajectory generation module, by means of a tracking controller and a filter called dynamic input boundary (DIB). The DIB filters out the directions of motions that would bring the system too close to singularities, according to a suitable metric. The loop with the user is finally closed with a force feedback that is informative of the mismatch between the operator’s commands and the trajectory of the payload. This feedback intuitively increases the user’s awareness of obstacles or configurations of the system that are close to singularities. The proposed framework is validated by means of realistic hardware-in-the-loop simulations with a person operating the system via a force-feedback haptic interface

Application of a differentiator-based adaptive super-twisting controller for a redundant cable-driven parallel robot

In this paper we present preliminary, experimental results of an Adaptive Super-Twisting Sliding-Mode Controller with time-varying gains for redundant Cable-Driven Parallel Robots. The sliding-mode controller is paired with a feed-forward action based on dynamics inversion. An exact sliding-mode differentiator is implemented to retrieve the velocity of the end-effector using only encoder measurements with the properties of finite-time convergence, robustness against perturbations and noise filtering. The platform used to validate the controller is a robot with eight cables and six degrees of freedom powered by 940 W compact servo drives. The proposed experiment demonstrates the performance of the controller, finite-time convergence and robustness in tracking a trajectory while subject to external disturbances up to approximately 400% the mass of the end-effector

Semi-Autonomous trajectory generation for mobile robots with integral haptic shared control

A new framework for semi-Autonomous path planning for mobile robots that extends the classical paradigm of bilateral shared control is presented. The path is represented as a B-spline and the human operator can modify its shape by controlling the motion of a finite number of control points. An autonomous algorithm corrects in real time the human directives in order to facilitate path tracking for the mobile robot and ensures i) collision avoidance, ii) path regularity, and iii) attraction to nearby points of interest. A haptic feedback algorithm processes both human's and autonomous control terms, and their integrals, to provide an information of the mismatch between the path specified by the operator and the one corrected by the autonomous algorithm. The framework is validated with extensive experiments using a quadrotor UAV and a human in the loop with two haptic interfaces

Adaptive-Attentive Geolocalization From Few Queries: A Hybrid Approach

We tackle the task of cross-domain visual geo-localization, where the goal is to geo-localize a given query image against a database of geo-tagged images, in the case where the query and the database belong to different visual domains. In particular, at training time, we consider having access to only few unlabeled queries from the target domain. To adapt our deep neural network to the database distribution, we rely on a 2-fold domain adaptation technique, based on a hybrid generative-discriminative approach. To further enhance the architecture, and to ensure robustness across domains, we employ a novel attention layer that can easily be plugged into existing architectures. Through a large number of experiments, we show that this adaptive-attentive approach makes the model robust to large domain shifts, such as unseen cities or weather conditions. Finally, we propose a new large-scale dataset for cross-domain visual geo-localization, called SVOX

Roll rate thresholds and perceived realism in driving simulation

Due to limited operational space, in dynamic driving simulators it is common practice to implement motion cueing algorithms that tilt the simulator cabin to reproduce sustained accelerations. In order to avoid conflicting inertial cues, the tilt rate is kept below drivers’ perceptual thresholds, which are typically derived from the results of classical vestibular research where additional sensory cues to self-motion are removed. Here we conduct two experiments in order to assess whether higher tilt limits can be employed to expand the user’s perceptual workspace of dynamic driving simulators. In the first experiment we measure detection thresholds for roll in conditions that closely resemble typical driving. In the second experiment we measure drivers’ perceived realism in slalom driving for sub-, near- and supra-threshold roll rates. Results show that detection threshold for roll in an active driving task is remarkably higher than the limits currently used in motion cueing algorithms to drive simulators. Supra-threshold roll rates in the slalom task are also rated as more realistic. Overall, our findings suggest that higher tilt limits can be successfully implemented in motion cueing algorithms to better optimize simulator operational space

A novel framework for closed-loop robotic motion simulation - Part II: motion cueing design and experimental validation

This paper, divided in two Parts, considers the problem of realizing a 6-DOF closed-loop motion simulator by exploiting an anthropomorphic serial manipulator as motion platform. After having proposed a suitable inverse kinematics scheme in Part I [1], we address here the other key issue, i.e., devising a motion cueing algorithm tailored to the specific robot motion envelope. An extension of the well-known classical washout filter designed in cylindrical coordinates will provide an effective solution to this problem. The paper will then present a thorough experimental evaluation of the overall architecture (inverse kinematics + motion cueing) on the chosen scenario: closed-loop simulation of a Formula 1 racing car. This will prove the feasibility of our approach in fully exploiting the robot motion capabilities as a motion simulator

Modeling and control of UAV bearing formations with bilateral high-level steering

In this paper we address the problem of controlling the motion of a group of unmanned aerial vehicles (UAVs) bound to keep a formation defined in terms of only relative angles (i.e. a bearing formation). This problem can naturally arise within the context of several multi-robot applications such as, e.g. exploration, coverage, and surveillance. First, we introduce and thoroughly analyze the concept and properties of bearing formations, and provide a class of minimally linear sets of bearings sufficient to uniquely define such formations. We then propose a bearing-only formation controller requiring only bearing measurements, converging almost globally, and maintaining bounded inter-agent distances despite the lack of direct metric information.The controller still leaves the possibility of imposing group motions tangent to the current bearing formation. These can be either autonomously chosen by the robots because of any additional task (e.g. exploration), or exploited by an assisting human co-operator. For this latter 'human-in-the-loop' case, we propose a multi-master/multi-slave bilateral shared control system providing the co-operator with some suitable force cues informative of the UAV performance. The proposed theoretical framework is extensively validated by means of simulations and experiments with quadrotor UAVs equipped with onboard cameras. Practical limitations, e.g. limited field-of-view, are also considered. © The Author(s) 2012

miR-519d Overexpression Is Associated With Human Obesity

Obesity is a consequence of imbalance of food intake and energy expenditure that results in storage of energy as fat, primarily in adipose tissue. MicroRNAs are non-coding RNAs that regulate gene expression in metabolic pathways and they are also involved in fat-cell development. The aim of this study was to evaluate whether microRNA dysfunction contributes to obesity. We analyzed, by microarray, the expression profile of 1,458 microRNAs in subcutaneous adipose tissue (SAT) from nondiabetic severely obese (n = 20) and nonobese adults (n = 8). Among 42 differently expressed microRNAs, we confirmed by reverse-transcription PCR (RT-PCR) that miR-519d was overexpressed whereas the protein levels of peroxisome proliferator-activated receptor-α (PPARA) (a predicted miR 519d target) were lower, at western analysis, in severely obese vs. nonobese subjects. We also show that miR-519d specifically and dose-dependently suppressed translation of the PPARA protein, and increased lipid accumulation during preadipocyte differentiation. Because PPARA plays a central role in fatty acid homeostasis, and in the transcriptional regulation of genes that are necessary for maintenance of the redox balance during the oxidative catabolism of fatty acids, we suggest that PPARA loss and miR-519d overexpression could be associated with metabolic imbalance and subsequent adipocyte hypertrophy in SAT during obesity

Electronics design of the RPC system for the OPERA muon spectrometer

The present document describes the front-end electronics of the RPC system that instruments the magnet muon spectrometer of the OPERA experiment. The main task of the OPERA spectrometer is to provide particle tracking information for muon identification and simplify the matching between the Precision Trackers. As no trigger has been foreseen for the experiment, the spectrometer electronics must be self-triggered with single-plane readout capability. Moreover, precision time information must be added within each event frame for off-line reconstruction. The read-out electronics is made of three different stages: the Front-End Boards (FEBs) system, the Controller Boards (CBs) system and the Trigger Boards(TBs) system. The FEB system provides discrimination of the strip incoming signals; a FAST-OR output of the input signals is also available for trigger plane signal generation. FEB signals are acquired by the CB system that provides the zero suppression and manages the communication to the DAQ and Slow Control. A Trigger Board allows to operate in both self-trigger mode (the FEB’s FAST-OR signal starts the plane acquisition) or in external-trigger mode (different conditions can be set on the FAST-OR signals generated from different planes)

VPR-Bench: An Open-Source Visual Place Recognition Evaluation Framework with Quantifiable Viewpoint and Appearance Change

Visual place recognition (VPR) is the process of recognising a previously visited place using visual information, often under varying appearance conditions and viewpoint changes and with computational constraints. VPR is related to the concepts of localisation, loop closure, image retrieval and is a critical component of many autonomous navigation systems ranging from autonomous vehicles to drones and computer vision systems. While the concept of place recognition has been around for many years, VPR research has grown rapidly as a field over the past decade due to improving camera hardware and its potential for deep learning-based techniques, and has become a widely studied topic in both the computer vision and robotics communities. This growth however has led to fragmentation and a lack of standardisation in the field, especially concerning performance evaluation. Moreover, the notion of viewpoint and illumination invariance of VPR techniques has largely been assessed qualitatively and hence ambiguously in the past. In this paper, we address these gaps through a new comprehensive open-source framework for assessing the performance of VPR techniques, dubbed “VPR-Bench”. VPR-Bench (Open-sourced at: https://github.com/MubarizZaffar/VPR-Bench) introduces two much-needed capabilities for VPR researchers: firstly, it contains a benchmark of 12 fully-integrated datasets and 10 VPR techniques, and secondly, it integrates a comprehensive variation-quantified dataset for quantifying viewpoint and illumination invariance. We apply and analyse popular evaluation metrics for VPR from both the computer vision and robotics communities, and discuss how these different metrics complement and/or replace each other, depending upon the underlying applications and system requirements. Our analysis reveals that no universal SOTA VPR technique exists, since: (a) state-of-the-art (SOTA) performance is achieved by 8 out of the 10 techniques on at least one dataset, (b) SOTA technique in one community does not necessarily yield SOTA performance in the other given the differences in datasets and metrics. Furthermore, we identify key open challenges since: (c) all 10 techniques suffer greatly in perceptually-aliased and less-structured environments, (d) all techniques suffer from viewpoint variance where lateral change has less effect than 3D change, and (e) directional illumination change has more adverse effects on matching confidence than uniform illumination change. We also present detailed meta-analyses regarding the roles of varying ground-truths, platforms, application requirements and technique parameters. Finally, VPR-Bench provides a unified implementation to deploy these VPR techniques, metrics and datasets, and is extensible through templates