Autonomous vehicle guidance in unknown environments

Gaining from significant advances in their performance granted by technological evolution, Autonomous Vehicles are rapidly increasing the number of fields of possible and effective applications. From operations in hostile, dangerous environments (military use in removing unexploded projectiles, survey of nuclear power and chemical industrial plants following accidents) to repetitive 24h tasks (border surveillance), from power-multipliers helping in production to less exotic commercial application in household activities (cleaning robots as consumer electronics products), the combination of autonomy and motion offers nowadays impressive options. In fact, an autonomous vehicle can be completed by a number of sensors, actuators, devices making it able to exploit a quite large number of tasks. However, in order to successfully attain these results, the vehicle should be capable to navigate its path in different, sometimes unknown environments. This is the goal of this dissertation: to analyze and - mainly - to propose a suitable solution for the guidance of autonomous vehicles. The frame in which this research takes its steps is the activity carried on at the Guidance and Navigation Lab of Sapienza – Università di Roma, hosted at the School of Aerospace Engineering. Indeed, the solution proposed has an intrinsic, while not limiting, bias towards possible space applications, that will become obvious in some of the following content. A second bias dictated by the Guidance and Navigation Lab activities is represented by the choice of a sample platform. In fact, it would be difficult to perform a meaningful study keeping it a very general level, independent on the characteristics of the targeted kind of vehicle: it is easy to see from the rough list of applications cited above that these characteristics are extremely varied. The Lab hosted – even before the beginning of this thesis activity – a simple, home-designed and manufactured model of a small, yet performing enough autonomous vehicle, called RAGNO (standing for Rover for Autonomous Guidance Navigation and Observation): it was an obvious choice to select that rover as the reference platform to identify solutions for guidance, and to use it, cooperating to its improvement, for the test activities which should be considered as mandatory in this kind of thesis work to validate the suggested approaches. The draft of the thesis includes four main chapters, plus introduction, final remarks and future perspectives, and the list of references. The first chapter (“Autonomous Guidance Exploiting Stereoscopic Vision”) investigates in detail the technique which has been deemed as the most interesting for small vehicles. The current availability of low cost, high performance cameras suggests the adoption of the stereoscopic vision as a quite effective technique, also capable to making available to remote crew a view of the scenario quite similar to the one humans would have. Several advanced image analysis techniques have been investigated for the extraction of the features from left- and right-eye images, with SURF and BRISK algorithm being selected as the most promising one. In short, SURF is a blob detector with an associated descriptor of 64 elements, where the generic feature is extracted by applying sequential box filters to the surrounding area. The features are then localized in the point of the image where the determinant of the Hessian matrix H(x,y) is maximum. The descriptor vector is than determined by calculating the Haar wavelet response in a sampling pattern centered in the feature. BRISK is instead a corner detector with an associated binary descriptor of 512 bit. The generic feature is identified as the brightest point in a sampling circular area of N pixels while the descriptor vector is calculated by computing the brightness gradient of each of the N(N-1)/2 pairs of sampling points. Once left and right features have been extracted, their descriptors are compared in order to determine the corresponding pairs. The matching criterion consists in seeking for the two descriptors for which their relative distance (Euclidean norm for SURF, Hamming distance for BRISK) is minimum. The matching process is computationally expensive: to reduce the required time the thesis successfully explored the theory of the epipolar geometry, based on the geometric constraint existing between the left and right projection of the scene point P, and indeed limiting the space to be searched. Overall, the selected techniques require between 200 and 300 ms on a 2.4GHz clock CPU for the feature extraction and matching in a single (left+right) capture, making it a feasible solution for slow motion vehicles. Once matching phase has been finalized, a disparity map can be prepared highlighting the position of the identified objects, and by means of a triangulation (the baseline between the two cameras is known, the size of the targeted object is measured in pixels in both images) the position and distance of the obstacles can be obtained. The second chapter (“A Vehicle Prototype and its Guidance System”) is devoted to the implementation of the stereoscopic vision onboard a small test vehicle, which is the previously cited RAGNO rover. Indeed, a description of the vehicle – the chassis, the propulsion system with four electric motors empowering the wheels, the good roadside performance attainable, the commanding options – either fully autonomous, partly autonomous with remote monitoring, or fully remotely controlled via TCP/IP on mobile networks - is included first, with a focus on different sensors that, depending on the scenario, can integrate the stereoscopic vision system. The intelligence-side of guidance subsystem, exploiting the navigation information provided by the camera, is then detailed. Two guidance techniques have been studied and implemented to identify the optimal trajectory in a field with scattered obstacles: the artificial potential guidance, based on the Lyapunov approach, and the A-star algorithm, looking for the minimum of a cost function built on graphs joining the cells of a mesh over-imposed to the scenario. Performance of the two techniques are assessed for two specific test-cases, and the possibility of unstable behavior of the artificial potential guidance, bouncing among local minima, has been highlighted. Overall, A-star guidance is the suggested solution in terms of time, cost and reliability. Notice that, withstanding the noise affecting information from sensors, an estimation process based on Kalman filtering has been also included in the process to improve the smoothness of the targeted trajectory. The third chapter (“Examples of Possible Missions and Applications”) reports two experimental campaigns adopting RAGNO for the detection of dangerous gases. In the first one, the rover accommodates a specific sensor, and autonomously moves in open fields, avoiding possible obstacles, to exploit measurements at given time intervals. The same configuration for RAGNO is also used in the second campaign: this time, however, the path of the rover is autonomously computed on the basis of the way points communicated by a drone which is flying above the area of measurements and identifies possible targets of interest. The fourth chapter (“Guidance of Fleet of Autonomous Vehicles ”) stresses this successful idea of fleet of vehicles, and numerically investigates by algorithms purposely written in Matlab the performance of a simple swarm of two rovers exploring an unknown scenario, pretending – as an example - to represent a case of planetary surface exploration. The awareness of the surrounding environment is dictated by the characteristics of the sensors accommodated onboard, which have been assumed on the basis of the experience gained with the material of previous chapter. Moreover, the communication issues that would likely affect real world cases are included in the scheme by the possibility to model the comm link, and by running the simulation in a multi-task configuration where the two rovers are assigned to two different computer processes, each of them having a different TCP/IP address with a behavior actually depending on the flow of information received form the other explorer. Even if at a simulation-level only, it is deemed that such a final step collects different aspects investigated during the PhD period, with feasible sensors’ characteristics (obviously focusing on stereoscopic vision), guidance technique, coordination among autonomous agents and possible interesting application cases

Quantization for decentralized learning under subspace constraints

In this paper, we consider decentralized optimization problems where agents have individual cost functions to minimize subject to subspace constraints that require the minimizers across the network to lie in low-dimensional subspaces. This constrained formulation includes consensus or single-task optimization as special cases, and allows for more general task relatedness models such as multitask smoothness and coupled optimization. In order to cope with communication constraints, we propose and study an adaptive decentralized strategy where the agents employ differential randomized quantizers to compress their estimates before communicating with their neighbors. The analysis shows that, under some general conditions on the quantization noise, and for sufficiently small step-sizes $\mu$, the strategy is stable both in terms of mean-square error and average bit rate: by reducing $\mu$, it is possible to keep the estimation errors small (on the order of $\mu$) without increasing indefinitely the bit rate as $\mu\rightarrow 0$. Simulations illustrate the theoretical findings and the effectiveness of the proposed approach, revealing that decentralized learning is achievable at the expense of only a few bits

Gli scavi dell’Università degli Studi di Napoli “L’Orientale” nell’abitato greco-romano di Cuma (2007-2013)

Sintesi degli scavi nell'abitato greco-romano di Cuma, anni 2007-201

Share - Publish - Store - Preserve. Methodologies, Tools and Challenges for 3D Use in Social Sciences and Humanities

Through this White Paper, which gathers contributions from experts of 3D data as well as professionals concerned with the interoperability and sustainability of 3D research data, the PARTHENOS project aims at highlighting some of the current issues they have to face, with possible specific points according to the discipline, and potential practices and methodologies to deal with these issues. During the workshop, several tools to deal with these issues have been introduced and confronted with the participants experiences, this White Paper now intends to go further by also integrating participants feedbacks and suggestions of potential improvements. Therefore, even if the focus is put on specific tools, the main goal is to contribute to the development of standardized good practices related to the sharing, publication, storage and long-term preservation of 3D data

Design, construction, and test of the Gas Pixel Detectors for the IXPE mission

Due to be launched in late 2021, the Imaging X-Ray Polarimetry Explorer (IXPE) is a NASA Small Explorer mission designed to perform polarization measurements in the 2-8 keV band, complemented with imaging, spectroscopy and timing capabilities. At the heart of the focal plane is a set of three polarization-sensitive Gas Pixel Detectors (GPD), each based on a custom ASIC acting as a charge-collecting anode. In this paper we shall review the design, manufacturing, and test of the IXPE focal-plane detectors, with particular emphasis on the connection between the science drivers, the performance metrics and the operational aspects. We shall present a thorough characterization of the GPDs in terms of effective noise, trigger efficiency, dead time, uniformity of response, and spectral and polarimetric performance. In addition, we shall discuss in detail a number of instrumental effects that are relevant for high-level science analysis -- particularly as far as the response to unpolarized radiation and the stability in time are concerned.Comment: To be published in Astroparticle Physic

Design and development of a stereo vision-based navigation and guidance system for a space rover

The lag in telecommunications between a planetary exploration rover and the Earth does not allow the real-time control of the platform. Autonomous guidance and navigation capabilities are therefore mandatory requirements for planetary exploration missions. This paper deals with the definition, design, assembling and testing of a low-cost guidance and navigation system intended to be into the rover RAGNO (Rover for Autonomous Ground Navigation and Observation), an autonomous platform developed at Sapienza Università di Roma to familiarize with vehicles operating in hazardous or poorly known environment. The navigation function, while possibly completed by magnetometers and additional sensors, is mainly based on visual techniques, as they can reliably offer the required accuracy with suitable cost and complexity. For the purpose of obstacles identification and localization, basic to rovers operating in unknown environment, a stereoscopic navigation system has been selected. The observation from two points of view allows to localize a detected object by means of a simple triangulation of corresponding point pairs (features). The basic relations and the techniques implemented to detect and to match the features of the two images are briefly explained. As a result, the rover is able to autonomously understand the surrounding scenario. Once the obstacles have been localized, a safe and optimal path to reach the desired targets must be planned. To this aim, the implementation of a guidance algorithm based on manifold cell discretization, graph theory and length optimization has been presented. This algorithm, a customization of the A-star search method, is compared to a more classical one, based on the Lyapunov approach with the introduction of artificial potential functions centered in the identified obstacles

Capabilities of stereo vision systems for future space missions

Future space missions are likely to exploit advanced computer vision systems, providing high accuracy and a rich and comprehensive perception of the scenario/environment. Till now, main tasks of computer vision has been the detection and recognition of objects of an already known shape: this approach was adopted in the automatic rendezvous, carried on by identifying specific markers in acquired images. Improvements in optical sensors' quality, software skills and computational power allow today to attempt a full reconstruction of the observed scene, and this new approach could greatly expand the applications of computer vision in space missions. Stereo vision is a suitable option for these advanced techniques, offering significant advantages in terms of cost and constraints to the host spacecraft with respect to active vision systems, and probably the more convenient solution for small platforms. Binocular configurations, or even multiple points of view, enable a full 3D representation of the scenario, providing the information about distance missing from a pinhole representation. Specific algorithms are obviously needed to combine the different images, matching up extracted features. The paper discusses stereoscopic vision with a focus on the applications to in-orbit robotic activities and rover navigation. The basic relations as well as the technique to match the features captured by the two cameras, leading to the evaluation of the optical depth in the scene, are briefly recalled. With specific reference to the case of a rover, the detail of the implementation of a stereo vision system on board a small vehicle called RAGNO designed and built a the Guidance and Navigation Lab of Sapienza Universita’ di Roma are presented. The system is able to autonomously drive the rover towards a defined target, also selected from gathered images, while identifying and avoiding intermediate obstacles. Indeed, a complete guidance, navigation and control function with not-toosophisticated hardware has been designed and tested. Characteristics and quantitative figures of this successful implementation are presented

Communication and navigation architecture for planetary exploration carried-on by a swarm of mobile robots

Planetary exploration is a milestone of future space programs. Continuous evolution in robotics technologies, including improvement in sensors, in miniaturization, in software engineering allows to focus on the concept of unmanned, autonomous exploration carried on by swarm of mobile robots. As taught by several biology's examples, the cooperation among smaller, simpler agents provide effectiveness and ensures a good return even in case of the loss of some - or several - robots. This cooperation is primarily based on the exchange among the swarm's agents of information including payload data and positioning insight. A simple (to keep expandable platforms and their subsystems at a reasonable level in cost and complexity), yet effective communication and navigation architecture is requested to perform the task. The paper is part of a project aiming to first analyse the requirements for this architecture at the general level, and then investigate possible solutions by means of numerical simulations performed with purposely prepared software codes. Performance clearly depends on the mission environment, and in such a concern swarms composed by rovers, drones or low-altitude flying platforms have to be considered, include peculiar motion constraints and issues. Simulations consider radiofrequency links in different bands, with preliminary modelling of the channels' characteristics. Obstacles and outages, breaking the connections, are modelled. A specific attention is devoted to the navigation, in order to assess the nature and amount of data required to provide either a relative or absolute positioning capability: this information is crucial for referencing the data collected by the payload as well as for the coordinated guidance of the swarm. Being robustness the crucial asset of the swarm concept, the performance in different mission scenarios are evaluated on the basis of both deterministic and non-deterministic failures. Previous hands-on experience gained from studies and experiments devoted to small fleets of rovers operating in terrestrial, hostile environment is an important input for this research. As a result, a software code in MATLAB has been prepared and tested in order to be able, during next step of the project, to simulate in details the behaviour of fleets ranging from 2 to tens of agents operating in different scenarios of interest for planetary exploration. In this software code, the communications behaviour enters by limiting the mutual availability of information to the different agents (outages due to interposed obstacles) or out-of-range relative positioning. Navigation instead comes with the uncertainty in the knowledge of the kinematic state. These missing data affect the guidance computation and indeed limit the effectiveness of the exploration itself

A swarm of autonomous rovers for cooperative planetary exploration

The improvements in computational power, miniaturization processes, sensors and robotics technology allow today to realize low cost, small and multi-tasking mobile robotic platforms with high level of artificial intelligence. In addition, the increasing capabilities of the computer vision in the field of 3D scene reconstruction and obstacles localization allow to integrate an efficient and robust multi-vision based navigation system thus supplying learning capacity. This architecture is typical of an autonomous rover, i.e. a mobile platform which is able to navigate, to plan and follow a safe path without human remote control. These skills are obviously mandatory requirements for space missions like planetary or asteroid exploration. Till now, typical missions has been characterized by non-cooperative autonomous rovers working in different and quite far areas. Increased availability of performing autonomous platforms now suggests to plan missions involving a swarm of cooperative rovers exploring a predefined area of a planet, an asteroid or a comet, with a common target. Coordination leads to versatility and lower cost in terms of data return. The logic of cooperation is based on the fact that each member of the swarm shares a set of information that might help the others in achieving their tasks. The paper describes a possible system architecture of a swarm of two small rovers designed, built and tested at the Guidance and Navigation Lab of Università di Roma “La Sapienza". The goal of the mission is to reach a common and pre-assigned target position - a hypothetic scientific post on the surface of a planet - while exploring and analysing the surrounding environment. The coordinates of a localised obstacles are shared with the other member in order to build a common and continuously upgradable map of the environment and a database of available safe paths. The whole architecture of the designed autonomous guidance and vision-based navigation system together with the communication system are presented in detail

A small platform application for close Inspection of an out of control satellite

The field of space proximity operations such as on-orbit inspection, refueling, repair and debris removal has been recently exploring new innovations for what technology and application scenarios concern. For this scope, small orbiting platforms, despite their limited power and operative capacity, represent a low-cost solution for the majority of proximity missions. Regarding mission architecture, various researches have been carried out to analyze the feasibility of several practical applications in a proximity operation scenario. In this sense, GNC systems, which play a fundamental role for the successfulness of the mission, have undergone relevant improvements. Optical navigation has proved to be a reliable solution for the navigation system, optimal path planning algorithms have been augmented with secure and robust collision avoidance, while control strategies offer satisfying performance in terms of desired position and velocity tracking. The present research shows a possible and feasible application of a small platform (chaser) for the close inspection of an uncontrolled, un-cooperative, Earth-orbiting satellite (target). In an initial phase of the mission, the chaser will follow a closed relative orbit (Parking Orbit) around the target and use measurements acquired from a passive camera and a range sensor in order to estimate target’s relative position, velocity, orientation, angular motion and shape. By means of the acquired information, a set of relevant viewpoints for a close and clear inspection of the target is evaluated. As a second step, starting from the Parking Orbit, the chaser will perform a rendezvous maneuver to reach one of the viewpoints, selected as the optimal among all. The Approach Trajectory followed in this phase is computed to be optimal from a propellant consumption point of view and free of collisions with the target. The desired trajectory will be tracked by implementing an impedance control law. Once the viewpoint has been reached, the chaser will maintain the relative position and orientation while performing the inspection by means of a camera mounted on a robotic arm. At the end of this operation, the chaser will transfer to the subsequent viewpoint, following an optimized and safe path. The mission will be achieved once all the viewpoints have been visited. In the paper rigorous analytical formulations of the relative dynamics, estimate process, optimal path planning and control strategy will be presented. Additionally, the feasibility of the proposed architecture will be evaluated through numerical simulations on some test case scenarios