Machine Learning Algorithms for Robotic Navigation and Perception and Embedded Implementation Techniques

L'abstract è presente nell'allegato / the abstract is in the attachmen

Novel parallel approaches to efficiently solve spatial problems on heterogeneous CPU-GPU systems

Addressing this task is difficult as (i) it requires analysing large databases in a short time, and (ii) it is commonly addressed by combining different methods with complex data dependencies, making it challenging to exploit parallelism on heterogeneous CPU-GPU systems. Moreover, most efforts in this context focus on improving the accuracy of the approaches and neglect reducing the processing time—the most accurate algorithm was designed to process the fingerprints using a single thread. We developed a new methodology to address the latent fingerprint identification problem called “Asynchronous processing for Latent Fingerprint Identification” (ALFI) that speeds up processing while maintaining high accuracy. ALFI exploits all the resources of CPU-GPU systems using asynchronous processing and fine-coarse parallelism to analyse massive fingerprint databases. We assessed the performance of ALFI on Linux and Windows operating systems using the well-known NIST/FVC databases. Experimental results revealed that ALFI is on average 22x faster than the state-of-the-art identification algorithm, reaching a speed-up of 44.7x for the best-studied case. In terrain analysis, Digital Elevation Models (DEMs) are relevant datasets used as input to those algorithms that typically sweep the terrain to analyse its main topological features such as visibility, elevation, and slope. The most challenging computation related to this topic is the total viewshed problem. It involves computing the viewshed—the visible area of the terrain—for each of the points in the DEM. The algorithms intended to solve this problem require many memory accesses to 2D arrays, which, despite being regular, lead to poor data locality in memory. We proposed a methodology called “skewed Digital Elevation Model” (sDEM) that substantially improves the locality of memory accesses and exploits the inherent parallelism of rotational sweep-based algorithms. Particularly, sDEM applies a data relocation technique before accessing the memory and computing the viewshed, thus significantly reducing the execution time. Different implementations are provided for single-core, multi-core, single-GPU, and multi-GPU platforms. We carried out two experiments to compare sDEM with (i) the most used geographic information systems (GIS) software and (ii) the state-of-the-art algorithm for solving the total viewshed problem. In the first experiment, sDEM results on average 8.8x faster than current GIS software, despite considering only a few points because of the limitations of the GIS software. In the second experiment, sDEM is 827.3x faster than the state-of-the-art algorithm considering the best case. The use of Unmanned Aerial Vehicles (UAVs) with multiple onboard sensors has grown enormously in tasks involving terrain coverage, such as environmental and civil monitoring, disaster management, and forest fire fighting. Many of these tasks require a quick and early response, which makes maximising the land covered from the flight path an essential goal, especially when the area to be monitored is irregular, large, and includes many blind spots. In this regard, state-of-the-art total viewshed algorithms can help analyse large areas and find new paths providing all-round visibility. We designed a new heuristic called “Visibility-based Path Planning” (VPP) to solve the path planning problem in large areas based on a thorough visibility analysis. VPP generates flyable paths that provide high visual coverage to monitor forest regions using the onboard camera of a single UAV. For this purpose, the hidden areas of the target territory are identified and considered when generating the path. Simulation results showed that VPP covers up to 98.7% of the Montes de Malaga Natural Park and 94.5% of the Sierra de las Nieves National Park, both located in the province of Malaga (Spain). In addition, a real flight test confirmed the high visibility achieved using VPP. Our methodology and analysis can be easily applied to enhance monitoring in other large outdoor areas.In recent years, approaches that seek to extract valuable information from large datasets have become particularly relevant in today's society. In this category, we can highlight those problems that comprise data analysis distributed across two-dimensional scenarios called spatial problems. These usually involve processing (i) a series of features distributed across a given plane or (ii) a matrix of values where each cell corresponds to a point on the plane. Therefore, we can see the open-ended and complex nature of spatial problems, but it also leaves room for imagination to be applied in the search for new solutions. One of the main complications we encounter when dealing with spatial problems is that they are very computationally intensive, typically taking a long time to produce the desired result. This drawback is also an opportunity to use heterogeneous systems to address spatial problems more efficiently. Heterogeneous systems give the developer greater freedom to speed up suitable algorithms by increasing the parallel programming options available, making it possible for different parts of a program to run on the dedicated hardware that suits them best. Several of the spatial problems that have not been optimised for heterogeneous systems cover very diverse areas that seem vastly different at first sight. However, they are closely related due to common data processing requirements, making them suitable for using dedicated hardware. In particular, this thesis provides new parallel approaches to tackle the following three crucial spatial problems: latent fingerprint identification, total viewshed computation, and path planning based on maximising visibility in large regions. Latent fingerprint identification is one of the essential identification procedures in criminal investigations. Addressing this task is difficult as (i) it requires analysing large databases in a short time, and (ii) it is commonly addressed by combining different methods with complex data dependencies, making it challenging to exploit parallelism on heterogeneous CPU-GPU systems. Moreover, most efforts in this context focus on improving the accuracy of the approaches and neglect reducing the processing time—the most accurate algorithm was designed to process the fingerprints using a single thread. We developed a new methodology to address the latent fingerprint identification problem called “Asynchronous processing for Latent Fingerprint Identification” (ALFI) that speeds up processing while maintaining high accuracy. ALFI exploits all the resources of CPU-GPU systems using asynchronous processing and fine-coarse parallelism to analyse massive fingerprint databases. We assessed the performance of ALFI on Linux and Windows operating systems using the well-known NIST/FVC databases. Experimental results revealed that ALFI is on average 22x faster than the state-of-the-art identification algorithm, reaching a speed-up of 44.7x for the best-studied case. In terrain analysis, Digital Elevation Models (DEMs) are relevant datasets used as input to those algorithms that typically sweep the terrain to analyse its main topological features such as visibility, elevation, and slope. The most challenging computation related to this topic is the total viewshed problem. It involves computing the viewshed—the visible area of the terrain—for each of the points in the DEM. The algorithms intended to solve this problem require many memory accesses to 2D arrays, which, despite being regular, lead to poor data locality in memory. We proposed a methodology called “skewed Digital Elevation Model” (sDEM) that substantially improves the locality of memory accesses and exploits the inherent parallelism of rotational sweep-based algorithms. Particularly, sDEM applies a data relocation technique before accessing the memory and computing the viewshed, thus significantly reducing the execution time. Different implementations are provided for single-core, multi-core, single-GPU, and multi-GPU platforms. We carried out two experiments to compare sDEM with (i) the most used geographic information systems (GIS) software and (ii) the state-of-the-art algorithm for solving the total viewshed problem. In the first experiment, sDEM results on average 8.8x faster than current GIS software, despite considering only a few points because of the limitations of the GIS software. In the second experiment, sDEM is 827.3x faster than the state-of-the-art algorithm considering the best case. The use of Unmanned Aerial Vehicles (UAVs) with multiple onboard sensors has grown enormously in tasks involving terrain coverage, such as environmental and civil monitoring, disaster management, and forest fire fighting. Many of these tasks require a quick and early response, which makes maximising the land covered from the flight path an essential goal, especially when the area to be monitored is irregular, large, and includes many blind spots. In this regard, state-of-the-art total viewshed algorithms can help analyse large areas and find new paths providing all-round visibility. We designed a new heuristic called “Visibility-based Path Planning” (VPP) to solve the path planning problem in large areas based on a thorough visibility analysis. VPP generates flyable paths that provide high visual coverage to monitor forest regions using the onboard camera of a single UAV. For this purpose, the hidden areas of the target territory are identified and considered when generating the path. Simulation results showed that VPP covers up to 98.7% of the Montes de Malaga Natural Park and 94.5% of the Sierra de las Nieves National Park, both located in the province of Malaga (Spain). In addition, a real flight test confirmed the high visibility achieved using VPP. Our methodology and analysis can be easily applied to enhance monitoring in other large outdoor areas

ADAPTIVE SAMPLING METHODS FOR TESTING AUTONOMOUS SYSTEMS

In this dissertation, I propose a software-in-the-loop testing architecture that uses adaptive sampling to generate test suites for intelligent systems based upon identifying transitions in high-level mission criteria. Simulation-based testing depends on the ability to intelligently create test-cases that reveal the greatest information about the performance of the system in the fewest number of runs. To this end, I focus on the discovery and analysis of performance boundaries. Locations in the testing space where a small change in the test configuration leads to large changes in the vehicle's behavior. These boundaries can be used to characterize the regions of stable performance and identify the critical factors that affect autonomous decision making software. By creating meta-models which predict the locations of these boundaries we can efficiently query the system and find informative test scenarios. These algorithms form the backbone of the Range Adversarial Planning Tool (RAPT): a software system used at naval testing facilities to identify the environmental triggers that will cause faults in the safety behavior of unmanned underwater vehicles (UUVs). This system was used to develop UUV field tests which were validated on a hardware platform at the Keyport Naval Testing Facility. The development of test cases from simulation to deployment in the field required new analytical tools. Tools that were capable of handling uncertainty in the vehicle's performance, and the ability to handle large datasets with high-dimensional outputs. This approach has also been applied to the generation of self-righting plans for unmanned ground vehicles (UGVs) using topological transition graphs. In order to create these graphs, I had to develop a set of manifold sampling and clustering algorithms which could identify paths through stable regions of the configuration space. Finally, I introduce an imitation learning approach for generating surrogate models of the target system's control policy. These surrogate agents can be used in place of the true autonomy to enable faster than real-time simulations. These novel tools for experimental design and behavioral modeling provide a new way of analyzing the performance of robotic and intelligent systems, and provide a designer with actionable feedback

Team MIT Urban Challenge Technical Report

This technical report describes Team MITs approach to theDARPA Urban Challenge. We have developed a novel strategy forusing many inexpensive sensors, mounted on the vehicle periphery,and calibrated with a new cross-­modal calibrationtechnique. Lidar, camera, and radar data streams are processedusing an innovative, locally smooth state representation thatprovides robust perception for real­ time autonomous control. Aresilient planning and control architecture has been developedfor driving in traffic, comprised of an innovative combination ofwell­proven algorithms for mission planning, situationalplanning, situational interpretation, and trajectory control. These innovations are being incorporated in two new roboticvehicles equipped for autonomous driving in urban environments,with extensive testing on a DARPA site visit course. Experimentalresults demonstrate all basic navigation and some basic trafficbehaviors, including unoccupied autonomous driving, lanefollowing using pure-­pursuit control and our local frameperception strategy, obstacle avoidance using kino-­dynamic RRTpath planning, U-­turns, and precedence evaluation amongst othercars at intersections using our situational interpreter. We areworking to extend these approaches to advanced navigation andtraffic scenarios

Reitinsuunnittelu määrätyssä järjestyksessä tehtäville peltotöille usean työkoneen yhteistyönä

Coverage path planning is the task of finding a collision free path that passes over every point of an area or volume of interest. In agriculture, the coverage task is encountered especially in the process of crop cultivation. Several tasks are performed on the field, one after the other, during the cultivation cycle. Cooperation means that multiple agents, in this case vehicles, are working together towards a common goal. Several studies consider the problem where a single task is divided and assigned among the agents. In this thesis, however, the vehicles have different tasks that are sequentially dependent, that is, the first task must be completed before the other. The tasks are performed simultaneously on the same area. The literature review suggests that there is a lack of previous research on this topic. The objective of this thesis was to develop an algorithm to solve the cooperative coverage path planning problem for sequentially dependent tasks. A tool chain that involves Matlab, Simulink and Visual Studio was adapted for the development and testing of the solution. A development and testing architecture was designed including a compatible interface to a simulation and a real-life test environment. Two different algorithms were implemented based on the idea of computing short simultaneous paths at a time and scheduling them in real-time. The results were successfully demonstrated in a real-life test environment with two tractors equipped with a disc cultivator and a seeder. The objective was to sow the test area. The test drives show that with the algorithms that were developed in this thesis it is possible to perform two sequentially dependent agricultural coverage tasks simultaneously on the same area.Kattavassa reitinsuunnittelussa yritetään löytää polku, jonka aikana määritelty ala tai tilavuus tulee käytyä läpi niin että alueen jokainen piste on käsitelty. Maataloudessa tämä tehtävä on merkityksellinen erityisesti peltoviljelyssä. Useita peltotöitä suoritetaan yksi toisensa jälkeen samalla alueella viljelyvuoden aikana. Useissa tutkimuksissa käsitellään yhteistyönä tehtävää reitinsuunnittelua, jossa yksi tehtävä on jaettu osiin ja osat jaetaan useiden tekijöiden kuten robottien kesken. Tässä diplomityössä peltotyökoneilla on kuitenkin omat erilliset tehtävänsä, joilla on määrätty järjestys, eli niiden suorittaminen riippuu työjärjestyksestä. Työkoneet työskentelevät samanaikaisesti samalla alueella. Diplomityössä tehty kirjallisuuskatsaus viittaa siihen, että vastaavaa aihetta ei ole aiemmin tutkittu. Tämän diplomityön tavoitteena on kehittää algoritmi, jolla voidaan toteuttaa reitinsuunnittelu määrätyssä järjestyksessä tehtäville peltotöille usean peltotyökoneen yhteistyönä. Algoritmikehitystä ja testausta varten suunniteltiin yhtenäinen rajapinta, jolla algoritmia voitaisiin testata sekä simulaatiossa että todellisessa testitilanteessa. Algoritmikehityksessä käytettiin työkaluina Matlab, Simulink ja Visual Studio -ohjelmia. Työssä toteutettiin kaksi algoritmia, jotka perustuvat samaan ideaan: suunnitellaan kerrallaan kaksi lyhyttä samanaikaista polkua, jotka ajoitetaan reaaliajassa. Algoritmeja testattiin todellisessa testiympäristössä kahden työkoneen yhteistyönä, kun tavoitteena on kylvää koko testialue. Ensimmäinen työvaihe suoritettiin lautasmuokkaimella ja toinen kylvökoneella. Testiajot osoittavat, että diplomityössä kehitetyillä algoritmeilla voidaan ohjata kahden toisistaan riippuvaisen peltotyön toteutus samanaikaisesti samalla peltoalueella

Trajectory planning based on adaptive model predictive control: Study of the performance of an autonomous vehicle in critical highway scenarios

Increasing automation in automotive industry is an important contribution to overcome many of the major societal challenges. However, testing and validating a highly autonomous vehicle is one of the biggest obstacles to the deployment of such vehicles, since they rely on data-driven and real-time sensors, actuators, complex algorithms, machine learning systems, and powerful processors to execute software, and they must be proven to be reliable and safe. For this reason, the verification, validation and testing (VVT) of autonomous vehicles is gaining interest and attention among the scientific community and there has been a number of significant efforts in this field. VVT helps developers and testers to determine any hidden faults, increasing systems confidence in safety, security, functional analysis, and in the ability to integrate autonomous prototypes into existing road networks. Other stakeholders like higher-management, public authorities and the public are also crucial to complete the VTT process. As autonomous vehicles require hundreds of millions of kilometers of testing driven on public roads before vehicle certification, simulations are playing a key role as they allow the simulation tools to virtually test millions of real-life scenarios, increasing safety and reducing costs, time and the need for physical road tests. In this study, a literature review is conducted to classify approaches for the VVT and an existing simulation tool is used to implement an autonomous driving system. The system will be characterized from the point of view of its performance in some critical highway scenarios.O aumento da automação na indústria automotiva é uma importante contribuição para superar muitos dos principais desafios da sociedade. No entanto, testar e validar um veículo altamente autónomo é um dos maiores obstáculos para a implantação de tais veículos, uma vez que eles contam com sensores, atuadores, algoritmos complexos, sistemas de aprendizagem de máquina e processadores potentes para executar softwares em tempo real, e devem ser comprovadamente confiáveis e seguros. Por esta razão, a verificação, validação e teste (VVT) de veículos autónomos está a ganhar interesse e atenção entre a comunidade científica e tem havido uma série de esforços significativos neste campo. A VVT ajuda os desenvolvedores e testadores a determinar quaisquer falhas ocultas, aumentando a confiança dos sistemas na segurança, proteção, análise funcional e na capacidade de integrar protótipos autónomos em redes rodoviárias existentes. Outras partes interessadas, como a alta administração, autoridades públicas e o público também são cruciais para concluir o processo de VTT. Como os veículos autónomos exigem centenas de milhões de quilómetros de testes conduzidos em vias públicas antes da certificação do veículo, as simulações estão a desempenhar cada vez mais um papel fundamental, pois permitem que as ferramentas de simulação testem virtualmente milhões de cenários da vida real, aumentando a segurança e reduzindo custos, tempo e necessidade de testes físicos em estrada. Neste estudo, é realizada uma revisão da literatura para classificar abordagens para a VVT e uma ferramenta de simulação existente é usada para implementar um sistema de direção autónoma. O sistema é caracterizado do ponto de vista do seu desempenho em alguns cenários críticos de autoestrad

Hybrid approaches for mobile robot navigation

The work described in this thesis contributes to the efficient solution of mobile robot navigation problems. A series of new evolutionary approaches is presented. Two novel evolutionary planners have been developed that reduce the computational overhead in generating plans of mobile robot movements. In comparison with the best-performing evolutionary scheme reported in the literature, the first of the planners significantly reduces the plan calculation time in static environments. The second planner was able to generate avoidance strategies in response to unexpected events arising from the presence of moving obstacles. To overcome limitations in responsiveness and the unrealistic assumptions regarding a priori knowledge that are inherent in planner-based and a vigation systems, subsequent work concentrated on hybrid approaches. These included a reactive component to identify rapidly and autonomously environmental features that were represented by a small number of critical waypoints. Not only is memory usage dramatically reduced by such a simplified representation, but also the calculation time to determine new plans is significantly reduced. Further significant enhancements of this work were firstly, dynamic avoidance to limit the likelihood of potential collisions with moving obstacles and secondly, exploration to identify statistically the dynamic characteristics of the environment. Finally, by retaining more extensive environmental knowledge gained during previous navigation activities, the capability of the hybrid navigation system was enhanced to allow planning to be performed for any start point and goal point

Advancing Spatiotemporal Research of Visitor Travel Patterns Within Parks and Protected Areas

Recent technological advances have made it possible to more accurately understand visitor travel patterns and their associated impacts. These advancements help to: accumulate voluminous data sets, collect alternative location data similar to GPS data, conduct spatiotemporal inferential statistics, and advance spatiotemporal visualizations. However, investigations of visitor travel patterns have not kept pace with recent technological advancements. Therefore, the purpose of this dissertation was to advance spatiotemporal research of visitor travel patterns within parks and protected areas by leveraging new technologies. The studies reported in this dissertation were designed to begin filling this gap, and include results from research conducted at: 1) Theodore Roosevelt National Park to identify which spatiotemporal variables are the most important to managers for understanding visitor travel patterns; 2) Hawai’i Volcanoes National Park to identify air tour travel patterns; and 3) the Bonneville Salt Flats to understand visitor travel patterns in a dispersed recreation setting that lacks organizational infrastructure. These three independent but conceptually linked studies were designed to inform our understanding of visitor travel patterns within parks and protected areas. This information is important so that park managers: a) understand how space and time influence visitor routes; and b) have relevant information to continue to conserve the biophysical resource while providing opportunities for quality visitor experiences. Results from the study at Theodore Roosevelt National Park showed that managers identified three temporal variables as being the most important towards understanding visitor travel patterns. These variables were analyzed to determine time allocation and vehicle speed patterns. Results from the study at Hawai’i Volcanoes National Park determined air tour travel patterns and which terrestrial attraction areas were the most affected by air tours. The study at the Bonneville Salt Flats identified potential areas of conflict and designed areas recommended for monitoring. Overall, this dissertation contributes to further understanding of visitor travel patterns, which provides information for managers to continue conserving parks and protected areas for the benefit of society