A aquisição de dados das variáveis químicas e físicas da água de lagos e reservatórios é de extrema importância. Monitorando a qualidade da água distribuída e consumida em nossas cidades estamos protegendo o nosso recurso natural mais valioso e conseqüentemente nossas próprias vidas. Esse monitoramento na atualidade ainda se realiza de forma bastante convencional e trabalhosa. Esta dissertação apresenta o desenvolvimento de um sistema de telemetria de dados e vídeo completo entre uma plataforma autônoma para coleta de dados hidrológicos e uma estação de trabalho em terra. Este sistema fará uma composição com outros projetos já desenvolvidos nesta área de pesquisa, como a embarcação convencional utilizada como plataforma autônoma com seus motores, sensores e atuadores; a sonda de coleta de dados, responsável pela aquisição das variáveis químicas e físicas da água; sistema de posicionamento, que utiliza um GPS e bússola eletrônica para orientação, atuando e coletando dados dos sensores da embarcação; e sistema de gerenciamento, responsável por definir e orientar quais os pontos de coletas a serem pesquisados. Foram desenvolvidos algoritmos de comunicação entre os dispositivos de hardware existentes, efetivou-se a comunicação da plataforma autônoma e do microcomputador na estação terrestre através de um enlace de rádio freqüência, desenvolveu-se um programa visual que permite o acompanhamento do estado dos sensores/atuadores do sistema em terra em tempo mínimo e habilita para o usuário um controle remoto de navegação da embarcação autônoma de forma manual e completa se ocorrer a falha de algum equipamento de posicionamento. O programa armazena todas as variáveis referentes ao processo de coleta em um banco de dados, permitindo a análise e comparação das diferentes medições realizadas em diferentes períodos. / The data acquisition of the water chemical and physical variables in lakes and reservoirs is of extreme importance. Monitoring the quality of water supplied and consumed in our cities we are protecting the most valuable of our natural resources, and consequently our own lifes. Monitoring data quality, still in our days, is done in a conventional and hard manner. This work presents the development of a data and video telemetry system between an autonomous platform for water monitoring and a ground base. This system will complement the following already developed projects in this area, the autonomous platform constructed from a conventional boat with its engines, sensors and actuators; the data collecting probe, responsible for the acquisition of the water chemical and physical variables; the positioning system based on GPS and an electronic compass, responsible for navigation to the data collecting points in the reservoir. Communication algorithms between all the hardware were developed, radio frequency links were used to transmit data between the ground and the autonomous platform. A visual software application, that allows to visualize the state off the system variables, was developed. A remote control mode of the platform allows full manual navigation control of the platform, being of extreme importance in case of system crash. The visual software application developed allows to store all the important research variables in a data bank, allowing further analysis of the collected data.

abstract: Automated driving systems are in an intensive research and development stage, and the companies developing these systems are targeting to deploy them on public roads in a very near future. Guaranteeing safe operation of these systems is crucial as they are planned to carry passengers and share the road with other vehicles and pedestrians. Yet, there is no agreed-upon approach on how and in what detail those systems should be tested. Different organizations have different testing approaches, and one common approach is to combine simulation-based testing with real-world driving. One of the expectations from fully-automated vehicles is never to cause an accident. However, an automated vehicle may not be able to avoid all collisions, e.g., the collisions caused by other road occupants. Hence, it is important for the system designers to understand the boundary case scenarios where an autonomous vehicle can no longer avoid a collision. Besides safety, there are other expectations from automated vehicles such as comfortable driving and minimal fuel consumption. All safety and functional expectations from an automated driving system should be captured with a set of system requirements. It is challenging to create requirements that are unambiguous and usable for the design, testing, and evaluation of automated driving systems. Another challenge is to define useful metrics for assessing the testing quality because in general, it is impossible to test every possible scenario. The goal of this dissertation is to formalize the theory for testing automated vehicles. Various methods for automatic test generation for automated-driving systems in simulation environments are presented and compared. The contributions presented in this dissertation include (i) new metrics that can be used to discover the boundary cases between safe and unsafe driving conditions, (ii) a new approach that combines combinatorial testing and optimization-guided test generation methods, (iii) approaches that utilize global optimization methods and random exploration to generate critical vehicle and pedestrian trajectories for testing purposes, (iv) a publicly-available simulation-based automated vehicle testing framework that enables application of the existing testing approaches in the literature, including the new approaches presented in this dissertation. / Dissertation/Thesis / Doctoral Dissertation Computer Engineering 2019

Emergency response times have been shown to be directly correlated with mortality rates of out-of-hospital patients. Studies have been conducted to show the relationship between time and mortality rates until patients receive the proper treatment. With more cardiac arrests and other life threatening illnesses occurring in the United States, more emergency calls will be required as well. As of today, technological advancements have been made to reduce response times, but human factors still require certain procedures, causing delays in the run time and increasing the rate of mortality. Here we show the results of emergency response times with the market penetration of connected and autonomous vehicles. With connected and autonomous vehicles, the average time emergency vehicles spend on the roadways can be significantly decreased. Safety procedures with human drivers can be eliminated, giving the emergency vehicle a proper right-of-way through virtual emergency lanes and removing the need to slow down and avoid vehicles at intersections or during periods of heavy congestion. Our results show a three minute decrease in response time under full market penetration of the technology, reducing the mortality rate and increasing the potential to save lives.

The author's concept of the distance and tracking control problem for autonomous vehicles relates to the cooperative behaviour of two successive vehicles travelling in the same environment. This behaviour requires one vehicle, designated the leader to move autonomously around it's environment with other vehicles, designated followers maintaining a coincident travel path and desired longitudinal distance with respect to the leader. Distance and tracking control is beneficial in numerous applications including guiding autonomous vehicles in Intelligent Transport Systems (ITS) which increases traffic safety and the capacity of pre-existing road infrastructure. Service robotics may also benefit from the cost savings and flexibility offered by distance and tracking control which enables a number of robots to cooperate together in order to achieve a task beyond the capabilities ofjust one robot. Using a distance and tracking control scheme an intelligent leader robot may guide a number of less intelligent (and therefore less costly and less complex) followers to a work-site to perform a task. The author's approach to the distance and tracking control problem consisted of two separate solutions - an initial solution used as a starting point and learning experience and a second, more robust, fuzzy control-based solution. This thesis briefly describes the initial solution, but places a greater emphasis on the second solution. The reason for this is that the fuzzy control-based solution offers significant improvement on the initial solution and was developed based on conclusions drawn from the initial solution. Most implementations of distance and tracking control, sometimes referred to as Intelligent Cruise Control (ICC) or platooning, are limited to longitudinal distance control only. The leader tracking control is performed either implicitly by a separate lane-following control system or by human drivers. The fuzzy control-based solution offered in this thesis performs both distance and tracking control of an autonomous follower vehicle with respect to a leader vehicle in front of it. It represents a simple and cost effective solution to the requirements of autonomous vehicles operating in ITS schemes - particularly close formation platooning. The follower tracks a laser signal emitted by the leader and monitors the distance to the follower at the same time using ultrasonic ranging techniques. The follower uses the data obtained from these measuring techniques as inputs to a fuzzy controller algorithm to adjust its distance and alignment with respect to the leader. Other systems employed on road vehicles utilise video-based leader tracking, or a range of lane-following methods such as magnetometer or video-based methods. Typically these methods are disadvantaged by substantial unit and/or infrastructure costs associated with their deployment. The limitations associated with the solutions presented in this thesis arise in curved trajectories at larger longitudinal distance separations between vehicles. The effects of these limitations on road vehicles has yet to be fully quantified, however it is thought that these effects would not disadvantage its use in close formation platooning. The fuzzy control-based distance and tracking control solution features two inputs, which are the distance and alignment of the follower with respect to the leader. The fuzzy controller asserts two outputs, which are left and right wheel velocities to control the speed and trajectory of a differential drive vehicle. Each of the input and output fuzzy membership functions has seven terms based around lambda, Z-type and S-type functions. The fuzzy rule base consists of 49 rules and the fuzzy inference stage is based on the MAX/MIN method. A Centre of Maximum (CoM) def'uzzification method is used to provide the two crisp valued outputs to the vehicle motion control. The methods chosen for the fuzzy control of distance and tracking for autonomous vehicles were selected based on a compromise between their computational complexity and performance characteristics. This compromise was necessary in order to implement the chosen controller structure on pre-existing hardware test beds based on an 8-bit microcontrollers with limited memory and processing resources. Overall the fuzzy control-based solution presented in this thesis effectively solves the distance and tracking control problem. The solution was applied to differential drive hardware test-beds and was tested to verify performance. The solution was thoroughly tested in both the simulation environment and on hardware test-beds. Several issues are identified in this thesis regarding the application of the solution to other platforms and road vehicle use. The solution will be shown to be directly portable to service robotics applications and, with minor modifications, applicable to road vehicle close-formation platooning.

With the advent of faster computer processors and better optimization algorithms, Model Predictive Control (MPC) systems are more readily used for real-time applications. This research focuses on the application of MPC to trajectory generation of autonomous vehicles in an online manner. The operating environment is assumed to be unknown with various different types of obstacles. Models of simplified 2-D dynamics of the vehicle are developed, discretized and validated against a nonlinear CarSim vehicle model. The developed model is then used to predict future states of the vehicle. The relationship of the weight transfer to the tire slip angle is investigated. The optimal trajectory tracking problem is formulated in terms of a cost function minimization with constraints. Initially, a gradient descent method is used to minimize the cost function. A MATLAB based MPC controller is developed and interfaced with CarSim in order to test the controller on a vehicle operating in a realistic environment. The effects of varying MPC look-ahead horizon lengths on the computation time, simulation cost and the tracking performance are also investigated. Simulation results show that the new MPC controller provides satisfactory online obstacle avoidance and tracking performance. Also, a trajectory tracking criterion with goal point information is found to be superior to traditional trajectory tracking methods since they avoid causing the vehicle to retreat once a large obstacle is detected on the desired path. It is further demonstrated that at a controller frequency of 20Hz, the implementation is real-time implementable only at shorter horizon lengths. / UOIT

The problem of dynamic autonomous agent placement for tracking moving targets arises in many real-life applications, such as rescue operations, security, surveillance, and reconnaissance. The objective of this thesis is to develop a distributed hierarchical approach to address this problem. After the approach is developed, it is tested on a number of urban surveillance scenarios. The proposed approach views the placement problem as a multi-tiered architecture entailing modules for low-level sensor data preprocessing and fusion, decentralized decision support, knowledge building, and centralized decision support. This thesis focuses upon the modules of decentralized decision support and knowledge building. The decentralized decision support module requires a great deal of coordination among agents to achieve the mission objectives. The module entails two classes of distributed algorithms: non-model-based algorithms and model-based algorithms. The first class is used as a place holder while a model is built to describe agents knowledge about target behaviors. After the model is built and evaluated, agents switch to the model-based algorithms. To apply the approach to urban environments, urban terrain zones are classified, and the problem is mathematically formulated for two different types of urban terrain, namely low-rise, widely spaced and high-rise, closely spaced zones. An instance of each class of algorithms is developed for each of the two types of urban terrain. The algorithms are designed to run in a distributed fashion to address scalability and fault tolerance issues. The class of model-based algorithms includes a distributed model-based algorithm for dealing with evasive targets. The algorithm is designed to improve its performance over time as it learns from past experience how to deal with evasive targets. Apart from the algorithms, a model estimation module is developed to build motion models online from sensor observations. The approach is evaluated through a set of simulation experiments inspired from real-life scenarios. Experimental results reveal the superiority of the developed algorithms over existing ones and the applicability of the online model-building method. Therefore, it is concluded that the overall distributed approach is capable of handling agent placement or surveillance applications in urban environments among other applications.

CPS (Cyber-Physical Systems) enable a new class of applications that perceive their surroundings using raw data from sensors, monitor the timing of dynamic processes, and control the physical environment. Since failures and misbehaviors in application domains such as cars, medical devices, nuclear power plants, etc., may cause significant damage to life and/or property, CPS need to be safe and dependable. A conventional way of improving dependability is to use redundant hardware to replicate the whole (sub)system. Although hardware replication has been widely deployed in conventional mission-critical systems, it is cost-prohibitive to many emerging CPS application domains. Hardware replication also leads to limited system flexibility. This dissertation studies the problem of making CPS affordably dependable and develops a system-level framework that manages critical CPS resources including processors, networks, and sensors. Our framework called SAFER (System-level Architecture for Failure Evasion in Real-time applications) incorporates configurable software mechanisms and policies to tolerate failures of critical CPS resources while meeting their timing constraints. It supports adaptive graceful degradation, the effective use of different sensor modalities, and the fault-tolerant schemes of hot standby, cold standby, and re-execution. SAFER reliably and efficiently allocates tasks and their backups to CPU and sensor resources while satisfying network traffic constraints. It also fuses and (re)configures sensor data used by tasks to recover from system failures. The SAFER framework aims to guarantee the timeliness of different types of tasks that fall into one of four categories: (1) tasks with periodic arrivals, (2) tasks with continually varying periods, (3) tasks with parallel threads, and (4) tasks with self-suspensions. We offer the schedulability analyses and runtime support for such tasks with and without resource failures. Finally, the functionality of the proposed system is evaluated on a self-driving car using SAFER. We conclude that the proposed framework analytically satisfies timing constraints and predictably operates systems with and without resource failures, hence making CPS dependable and timely.

This thesis presents an online path planning algorithm developed for unmanned vehicles in charge of autonomous border patrol. In this Pursuit-Evasion game, the unmanned vehicle is required to capture multiple trespassers on its own before any of them reach a target safe house where they are safe from capture. The problem formulation is based on Isaacs’ Target Guarding problem, but extended to the case of multiple evaders. The proposed path planning method is based on Rapidly-exploring random trees (RRT) and is capable of producing trajectories within several seconds to capture 2 or 3 evaders. Simulations are carried out to demonstrate that the resulting trajectories approach the optimal solution produced by a nonlinear programming-based numerical optimal control solver. Experiments are also conducted on unmanned ground vehicles to show the feasibility of implementing the proposed online path planning algorithm on physical applications.

This thesis presents an online path planning algorithm developed for unmanned vehicles in charge of autonomous border patrol. In this Pursuit-Evasion game, the unmanned vehicle is required to capture multiple trespassers on its own before any of them reach a target safe house where they are safe from capture. The problem formulation is based on Isaacs’ Target Guarding problem, but extended to the case of multiple evaders. The proposed path planning method is based on Rapidly-exploring random trees (RRT) and is capable of producing trajectories within several seconds to capture 2 or 3 evaders. Simulations are carried out to demonstrate that the resulting trajectories approach the optimal solution produced by a nonlinear programming-based numerical optimal control solver. Experiments are also conducted on unmanned ground vehicles to show the feasibility of implementing the proposed online path planning algorithm on physical applications.

This research aims to understand the different requirements of civilian multiple autonomous vehicle systems in order to propose an adequate solution for the resource allocation problem. A new classification of unmanned system applications is presented with focus on unmanned aerial vehicles (UAVs). The main resource allocation systems requirements in each category are presented and discussed. A novel dynamic resource allocation model is introduced for efficient sharing of services provided by ad hoc assemblies of heterogeneous autonomous vehicles. A key contribution is the provision of capability to dynamically select sensors and platforms within constraints imposed by time dependencies, refuelling, and transportation services. The resource allocation problem is modelled as a connected network of nodes and formulated as an Integer Linear Program (ILP). Solution fitness is prioritized over computation time. Simulation results of an illustrative scenario are used to demonstrate the ability of the model to plan for sensor selection, refuelling, collaboration and cooperation between heterogeneous resources. Prioritization of operational cost leads to missions that use cheaper resources but take longer to complete. Prioritization of completion time leads to shorter missions at the expense of increased overall resource cost. Missions can be successfully re-planned through dynamic reallocation of new requests during a mission. Monte Carlo studies on systems of increasing complexity show that good solutions can be obtained using low time resolutions, with small time windows at a relatively low computational cost. In comparison with other approaches, the developed ILP model provides provably optimal solutions at the expense of longer computation time. Flight test procedures were developed for performing low cost experiments on a small scale, using commercial off the shelf equipment, with ability to infer conclusions on the large-scale implementation. Flight test experiments were developed and performed that assessed the performance of the developed ILP model and successfully demonstrated its main capabilities.