En la presente tesis se describen los trabajos desarrollados para obtener un sistema completo de navegación autónoma para robots terrestres con cinemática Ackermann operando en entornos exteriores no estructurados y dinámicos. Los trabajos aquí descritos abarcan las principales áreas de la robótica móvil terrestre, incluyendo localización, detección y seguimiento de obstáculos dinámicos, análisis del terreno, planificación y control, e incluso la creación de plataformas robóticas de investigación a partir de máquinas convencionales. A lo largo de la tesis se mantiene un especial énfasis en la modularidad y escalabilidad del sistema y en lograr la capacidad de navegación en ausencia de mapas (sin renunciar a las ventajas de poder integrar otro tipo de planteamientos en caso necesario), lo cual es especialmente interesante en entornos exteriores de grandes dimensiones, o en los que la escasez de landmarks dificulta la localización mediante técnicas de SLAM. En general, la tesis se articula en torno a la complementariedad de la percepción local basada en sensores LiDAR tridimensionales -capaces de generar una ingente cantidad de información geométrica de gran precisión del entorno en el que se encuentra el robot- con la capacidad de los sistemas de posicionamiento basado en satélite de proporcionar información de localización con un error acotado en cualquier punto del planeta. En concreto, se puede decir que la presente tesis narra el camino realizado desde el nacimiento de la idea abstracta (el diseño de un sistema de navegación), hasta la obtención de un sistema completamente autónomo, capaz de operar en la red peatonal de un campus universitario. / Esta tesis ha sido financiada por la ayuda para la Formación del Profesorado Universitario con código FPU15/04446 del Ministerio de Educación, Cultura y Deporte.

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With the increasingly prevalent use of robots, this paper presents the design and evaluation of a multifunctional Unmanned Ground Vehicle (UGV) with an adjustable suspension system, overmolding omni-wheels, and a unique tool head pick-up mechanism. The UGV addresses current adaptability, performance, and versatility limitations across various industries, including agriculture, construction, and surveillance. The adjustable suspension system enhances the UGV's stability and adaptability on diverse terrains, and the overmolding omni-wheels improve maneuverability and durability in off-road conditions. The tool head pick-up mechanism allows for the seamless integration of various tools, enabling the UGV to perform multiple tasks without manual intervention. A comprehensive performance evaluation assessed the UGVs' versatility, load capacity, passability, and adaptability. The results indicate that the proposed UGV design successfully addresses current limitations and has the potential to revolutionize various applications in different industries. Further research and development are necessary to optimize the UGV's performance, safety, and cost-effectiveness. / Thesis / Master of Applied Science (MASc)

The research on autonomous vehicles, and more specifically cooperation between autonomous vehicles, has become a prominent research field during the last cou- ple of decades. One example is the combination of an unmanned aerial vehicle (UAV) together with an unmanned ground vehicle (UGV). The benefits of this are that the two vehicles complement each other, where the UAV provides an aerial view and can reach areas where a ground vehicle can not. Furthermore, since the UAV has a limited range, the UGV can then serve as transport and recharge sta- tion for the UAV. This master thesis studies how model predictive control (MPC) can be used to land a UAV on a moving UGV.  A linear MPC is chosen, since previous work using this has shown promising results. The UAV is chosen to be controlled using commands in pitch, roll and climbing rate. The MPC is designed as a decoupled controller, with a separate horizontal and vertical controller. This allows for a spatial constraint to be im- plemented, which constrains the UAV from entering ground level before arriving above the UGV. It also constrains the UAV from potentially hitting protruding ob- jects on the UGV. The horizontal controller uses a simple planner, which guides the UAV to land on the UGV from behind.  The MPC is evaluated using a additive white Gaussian noise (AWGN) sen- sor error model with zero mean. The scenario used is that the UAV starts 50 m from the UGV, and the UGV starts driving in a given direction turning randomly. The MPC lands successfully in 100 % of the simulations for a wide range of tun- ings. The MPC maintains the same landing statistics with a delay in the sensor information of up to 500 ms. The AWGN could be increased while maintaining successful landings, however with significantly more retakes and longer landing times. Lower AWGN variance only slightly improves performance, suggesting that the MPC is quite robust towards high variance in the state estimation.  The MPC is also compared to a PID controller. The MPC has significantly shorter landing times. The PID has a more oscillatory control signal, however, the PID has a lower variance in landing positions, but a slightly less centered mean on the UGV. The overall results show that an MPC can be used to achieve a flexible controller that can be tuned and reformulated to fit the situation, and performs as good or better compared to a PID controller.  The hardware tests show promising results for the implementation of the MPC. The controller is not tuned and no system identification is done specifi- cally for the physical UAV, suggesting that the controller is robust for varying settings. Even though the UAV never lands on the UGV, the visual behavior and control signal plots suggest that it would be able to land. However, these tests are performed using global navigation satellite system state estimation on a sta- tionary UGV, therefore further tests need to be performed in more challenging scenarios.

A system of an unmanned aerial vehicle (UAV) collaborating with an unmanned ground vehicle (UGV) for use in for example surveillance, reconnaissance, transport and target acquisition is studied. The project investigates the problem of estimating the relative position, velocity and orientation between the UAV and the UGV required to autonomously land the UAV on the UGV during movement. The use of global navigation satellite system (GNSS) receivers are not considered since they are sensitive to interference and spoofing attacks.  The developed estimation system consists of an extended Kalman filter (EKF) using measurements from several sensors, including: a camera, barometers, inertial measurement units (IMUs) and impulse-radio ultra-wide bandwidth (IRUWB) transceivers. Primarily the use of near infrared (NIR) light emitting diodes (LEDs) attached to the UGV and a camera on the UAV is investigated. Several configurations of LED placements, and what errors to expect when measuring them with the camera, are evaluated. The performance is evaluated in both simulations and hardware sensor tests, but no live experiments that include any autonomous landing manoeuvre are conducted.  The results indicate that high estimation precision can be achieved, at close range the errors in position average below 2 cm and in orientation under 0.5 degrees. However, some problems arise from the detection and identification of the LEDs. Further, if measurements of the LEDs are completely missing, the estimation precision suffers due to error accumulation in the inertial navigation. These results indicate that autonomous landing is possible, since the amount of LED measurements and consequently also the estimation precision increases as the relative position decreases.

Die Kartierung und Lokalisierung eines mobilen Roboters in seiner Umgebung ist eine wichtige Voraussetzung für dessen Autonomie. In dieser Arbeit wird der Einsatz eines 3D-Laserscanners zur Erfüllung dieser Aufgaben untersucht. Durch die optimierte Anordnung eines rotierenden 2D-Laserscanners werden hochauflösende Bereiche vorgegeben. Zudem wird mit Hilfe von ICP die Kartierung und Lokalisierung im Stillstand durchgeführt. Bei der Betrachtung zur Verbesserung der Bewegungsschätzung wird auch eine Möglichkeit zur Lokalisierung während der Bewegung mit 3D-Scans vorgestellt. Die vorgestellten Algorithmen werden durch Experimente mit realer Hardware evaluiert.

Die Kartierung und Lokalisierung eines mobilen Roboters in seiner Umgebung ist eine wichtige Voraussetzung für dessen Autonomie. In dieser Arbeit wird der Einsatz eines 3D-Laserscanners zur Erfüllung dieser Aufgaben untersucht. Durch die optimierte Anordnung eines rotierenden 2D-Laserscanners werden hochauflösende Bereiche vorgegeben. Zudem wird mit Hilfe von ICP die Kartierung und Lokalisierung im Stillstand durchgeführt. Bei der Betrachtung zur Verbesserung der Bewegungsschätzung wird auch eine Möglichkeit zur Lokalisierung während der Bewegung mit 3D-Scans vorgestellt. Die vorgestellten Algorithmen werden durch Experimente mit realer Hardware evaluiert.

The interest in autonomous surveillance has increased due to advances in autonomous systems and sensor theory. This thesis is a preliminary study of the cooperation between UGVs and stationary sensors when monitoring a dedicated area. The primary focus is the path planning of a UGV for different initial intrusion alarms. Cell decomposition, i.e., spatial partitioning, of the area of surveillance was utilized, and the objective function is based on the probability of a present intruder in each cell. These probabilities were modeled through two different methods: ExpPlanner, utilizing an exponential decay function. Markov planner, utilizing a Markov chain to propagate the probabilities. The performance of both methods improves when a confident alarm system is utilized. By prioritizing the direction of the planned paths, the performances improved further. The Markov planner outperforms the ExpPlanner in finding a randomly walking intruder. The ExpPlanner is suitable for passive surveillance, and the Markov planner is suitable for ”aggressive target hunting”.

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This thesis presents a human interface which helps users efficiently allocate multiple unmanned ground vehicles (UGVs) cooperating to accomplish timing-sensitive missions in an urban environment. The urban environment consists of obstacles and a hazardous region. The obstacles represent a "no-go zone" while the hazardous region represents a high-risk area. The main object of this problem is to minimize the team operational cost while satisfying timing constraints. Operational costs for individual vehicles are based on risk and power consumption, and are calculated using path length and vehicle velocity. In this thesis, three types of timing constraints are considered: simultaneous arrival, tight sequential arrival, and loose sequential arrival. Coordination variables and functions are the strategy by which both temporal and spatial information is used to achieve cooperative timing at a minimum cost. Specifically, coordination variables and functions are used to plan trajectories for a team of UGVs that satisfy timing constraints. The importance of properly representing information to users, allowing them to make efficient decisions, is also discussed. Four different control interfaces (temporal, spatial, cost, and coordination variable/function control) were tested. A full factorial design of experiments was performed with response time, workload, and quality of decision as metrics used to evaluate the quality and effectiveness of each interface. Based on the results of this experiment, a final graphical user interface (GUI) was designed and is described. It incorporates a combination of coordination variable/function control and cost control. This GUI is capable of planning paths for vehicles based on cooperative timing constraints and enables users to make high quality decisions in deploying a group of vehicles.