Metrological characterization of a vision-based system for relative pose measurements with fiducial marker mapping for spacecrafts

An improved approach for the measurement of the relative pose between a target and a chaser spacecraft is presented. The selected method is based on a single camera, which can be mounted on the chaser, and a plurality of fiducial markers, which can be mounted on the external surface of the target. The measurement procedure comprises of a closed-form solution of the Perspective from n Points (PnP) problem, a RANdom SAmple Consensus (RANSAC) procedure, a non-linear local optimization and a global Bundle Adjustment refinement of the marker map and relative poses. A metrological characterization of the measurement system is performed using an experimental set-up that can impose rotations combined with a linear translation and can measure them. The rotation and position measurement errors are calculated with reference instrumentations and their uncertainties are evaluated by the Monte Carlo method. The experimental laboratory tests highlight the significant improvements provided by the Bundle Adjustment refinement. Moreover, a set of possible influencing physical parameters are defined and their correlations with the rotation and position errors and uncertainties are analyzed. Using both numerical quantitative correlation coefficients and qualitative graphical representations, the most significant parameters for the final measurement errors and uncertainties are determined. The obtained results give clear indications and advice for the design of future measurement systems and for the selection of the marker positioning on a satellite surface

Data fusion of images and 3D range data

A robot is a machine that embodies decades of research and development. Born as a simple mechanical devices, these machines evolved together with our technology and knowledge, reaching levels of automation never imagined before. The modern dream is represented by the cooperative robotics, where the robots do not just work for the people, but together with the people. Such result can be achieved only if these machines are able to acquire knowledge through perception, in other words they need to collect sensor measurements from which they extract meaningful information of the environment in order to adapt their behavior. This thesis speaks about the topic of the autonomous object recognition and picking for Automated Guided Vehicles, AGVs, robots employed nowadays in the automatic logistic plants. The development of a technology capable of achieving such task would be a significant technological improvement compared to the structure currently used in this field: rigid, strongly constrained and with a very limited human machine interaction. Automating the process of picking by making such vehicles more smart would open to many possibilities, both in terms of organization of the plants, both for the remarkable economic implications deriving from the abatement of many of the associated fixed costs. The logistics field is indeed a niche, in which the costs of the technology represent the true limit to its spread, costs due mainly to the limitations of the current technology. The work is therefore aimed at creating a stand-alone technology, usable directly on board of the modern AGVs, with minimal modifications in terms of hardware and software. The elements that made possible such development are the multi-sensor approach and data-fusion. The thesis starts with the analysis of the state of the art related of the field of the automated logistic, focusing mostly on the most innovative applications and researches on the automatization of the load/unload of the goods in the modern logistic plants. What emerges form the analysis it is that there is a technological gap between the world of the research and the industrial reality: the results and solutions proposed by the first seem not match the requirements and specification of the second. The second part of the thesis is dedicated to the sensors used: industrial cameras, planar 2D safety laser scanners and 3D time of flight cameras (TOF). For every device a specific (and independent) process is developed in order to recognize and localize Euro pallets: the information that AGVs require in order to perform the picking of an object are the three coordinates that define its pose in the 2D space, $[x,y,\theta]$, position and attitude. The focus is addressed both on the maximization of the reliability of the algorithms and both on the capability in providing a correct estimation of uncertainty of the results. The information content that comes from the uncertainty represents a key aspect for this work, in which the probabilistic characterization of the results and the adoption of the guidelines of the measurement field are the basis for a new approach to the problem. That allowed both the modification of state of the art algorithms both the development of new ones, developing a system that in the final implementation and tests has shown a reliability in the identification process sufficiently high to fulfill the industrial standards, 99\% of positive identifications. The third part is devoted to the calibration of system. In order to ensure a reliable process of identification and picking it is indeed fundamental to evaluate the relations between the sensing devices, sensor-sensor calibration, but also to relate the results obtained with the machine, sensor-robot calibration. These calibrations are critical steps that characterize the measurement chain between the target object and the robot controller. From that chain depends the overall accuracy in performing the forking procedure and, more important, the safety of such operation. The fourth part represents the core element of the thesis, the fusion of the identifications obtained from the different sensors. The multi-sensor approach is a strategy that allows the overcome of possible operational limits due to the measurement capabilities of the individual sensors, taking the best from the different devices and thus improving the performance of the entire system. This is particularly true in the case in which there are independent information sources, these, once fused, provide results way more reliable than the simple comparison of the data. Because of the different typology of the sensors involved, Cartesian ones like the laser and the TOF, and perspective ones like the camera, a specific fusion strategy is developed. The main benefit that the fusion provides is a reliable rejection of the possible false positives, which could cause very dangerous situations like the impact with objects or worst. A further contribution of this thesis is the risk prediction for the maneuver of picking. Knowing the uncertainty in the identification process, in calibration and in the motion of the vehicle it is possible to evaluate the confidence interval associated to a safe forking, the one that occurs without impact between the tines and the pallet. That is critical for the decision making logic of the AGV in order to ensure a safe functionality of the machine during all daily operations. Last part of the thesis presents the experimental results. The aforementioned topics have been implemented on a real robot, testing the behavior of the developed algorithms in various operative conditions

Self-Weighted Multilateration for Indoor Positioning Systems

The paper proposes an improved method for calculating the position of a movable tag whose distance to a (redundant) set of fixed beacons is measured by some suitable physical principle (typically ultra wide band or ultrasound propagation). The method is based on the multilateration technique, where the contribution of each individual beacon is weighed on the basis of a recurring, self-supported calibration of the measurement repeatability of each beacon at a given distance range. The work outlines the method and its implementation, and shows the improvement in measurement quality with respect to the results of a commercial Ultra-Wide-Band (UWB) system when tested on the same set of raw beacon-to-tag distances. Two versions of the algorithm are proposed: one-dimensional, or isotropic, and 3D. With respect to the standard approach, the isotropic solution managed to reduce the maximum localization error by around 25%, with a maximum error of 0.60 m, while the 3D version manages to improve even further the localization accuracy, with a maximum error of 0.45 m

Development and Characterization of a Safety System for Robotic Cells Based on Multiple Time of Flight (TOF) Cameras and Point Cloud Analysis

In this paper, a vision system for safety applications in human-robot collaboration is presented. The system is based on two Time-Of-Flight (TOF) cameras for 3D acquisition. The point clouds are registered in a common reference system, and human and robot recognition are then implemented. Human recognition is performed using a customized version of the Histogram of Oriented Gradient (HOG) algorithm. Robot recognition is achieved using a procedure based on the Kanade-Lucas-Tomasi (KLT) algorithm. Two safety strategies have been developed. The first one is based on the definition of suitable comfort zones of both the operator and the robot; the second implements virtual barriers between the operator and the robot. The vision system has been characterized in terms of (i) human and robot recognition performance, (ii) correctness of the detection of safety situations and (iii) evaluation of the time delays in the detection. The results show that the human operator is robustly recognized provided that he moves frontally with respect to the TOF cameras and the robot is always recognized. The safety situations are always identified correctly with an average time delay of 0.86 0.63 s (k=1)

Information Dynamics of the Brain, Cardiovascular and Respiratory Network during Different Levels of Mental Stress

In this study, an analysis of brain, cardiovascular and respiratory dynamics was conducted combining information-theoretic measures with the Network Physiology paradigm during different levels of mental stress. Starting from low invasive recordings of electroencephalographic, electrocardiographic, respiratory, and blood volume pulse signals, the dynamical activity of seven physiological systems was probed with one-second time resolution measuring the time series of the δ , θ , α and β brain wave amplitudes, the cardiac period (RR interval), the respiratory amplitude, and the duration of blood pressure wave propagation (pulse arrival time, PAT). Synchronous 5-min windows of these time series, obtained from 18 subjects during resting wakefulness (REST), mental stress induced by mental arithmetic (MA) and sustained attention induced by serious game (SG), were taken to describe the dynamics of the nodes composing the observed physiological network. Network activity and connectivity were then assessed in the framework of information dynamics computing the new information generated by each node, the information dynamically stored in it, and the information transferred to it from the other network nodes. Moreover, the network topology was investigated using directed measures of conditional information transfer and assessing their statistical significance. We found that all network nodes dynamically produce and store significant amounts of information, with the new information being prevalent in the brain systems and the information storage being prevalent in the peripheral systems. The transition from REST to MA was associated with an increase of the new information produced by the respiratory signal time series (RESP), and that from MA to SG with a decrease of the new information produced by PAT. Each network node received a significant amount of information from the other nodes, with the highest amount transferred to RR and the lowest transferred to δ , θ , α and β . The topology of the physiological network underlying such information transfer was node- and state-dependent, with the peripheral subnetwork showing interactions from RR to PAT and between RESP and RR, PAT consistently across states, the brain subnetwork resulting more connected during MA, and the subnetwork of brain–peripheral interactions involving different brain rhythms in the three states and resulting primarily activated during MA. These results have both physiological relevance as regards the interpretation of central and autonomic effects on cardiovascular and respiratory variability, and practical relevance as regards the identification of features useful for the automatic distinction of different mental states

Garment-based motion capture (GaMoCap): high-density capture of human shape in motion

This paper presents a new motion capture (MoCap) system, the garment-based motion capture system-GaMoCap. The key feature is the use of an easily wearable garment printed with colour-coded pattern and a generic multicamera setup with standard video cameras. The coded pattern allows a high-density distribution of markers per unit of surface (about 40 markers per 100 cm), avoiding markers-swap errors. The high density of markers reconstructed makes possible a simultaneous reconstruction of shape and motion, which gives several concurrent advantages with respect to the state of the art and providing performances comparable with previous marker-based systems. In particular, we provide effective solutions to counter the soft-tissue artefact which is a common problem for garment-based techniques. This effect is reduced using Point Cluster Technique to filter out the points strongly affected by non-rigid motion. Uncertainty of motion estimation has been experimentally quantified by comparing with a state-of-the-art commercial system and numerically predicted by means of a Monte Carlo Method procedure. The experimental evaluation was performed on three different articulated motions: shoulder, knee and hip flexion-extension. The results shows that for the three motion angles estimated with GaMoCap, the system provides comparable accuracies against a commercial VICON system