The Unmanned Aerial Vehicle (UAV) has over the last years become an increasingly prevalent technology in several sectors of modern society. Many UAVs are today used in a wide series of applications, from disaster relief to surveillance. A recent initiative by the Swedish Sea Rescue Society (SSRS) aims to implement UAVs in their emergency response. By quickly deploying drones to an area of interest, an assessment can be made, prior to personnel getting there, thus saving time and increasing the likelihood of a successful rescue operation. An aircraft like this, that will travel great distances, have to rely on a navigation system that does not require an operator to continuously see the vehicle. To travel to its goal, or search an area, the operator should be able to define a travel route that the UAV follows, by feeding it a series of waypoints. As an initial step towards that kind of system, this thesis has developed and tested the concept of waypoint navigation on a small and slow airship/blimp, in a simulated indoor environment. Mainly, two commonly used navigation algorithms were tested and compared. One is inspired by a sub-category of machine learning: reinforcement learning (RL), and the other one is based on the rapidly exploring random tree (RRT) algorithm. Four experiments were conducted to compare the two methods in terms of travel distance, average speed, energy efficiency, as well as robustness towards changes in the waypoint configurations. Results show that when the blimp was controlled by the best performing RL-based version, it generally travelled a more optimal (distance-wise) path than the RRT-based method. It also, in most cases, proved to be more robust against changes in the test tracks, and performed more consistently over different waypoint configurations. However, the RRT approach usually resulted in a higher average speed and energy efficiency. Also, the RL algorithm had some trouble navigating tracks where a physical obstacle was present. To sum up, the choice of algorithm depends on which parameters are prioritized by the blimp operator for a certain track. If a high velocity and energy efficiency is desirable, the RRT-based method is recommended. However, if it is important that the blimp travels as short a distance as possible between waypoints, and a higher degree of consistency in its performance is wanted, then the RL-method should be used. Moving forward from this report, toward the future implementation of both methods in rescue operations, it would be reasonable to analyze their performance under more realistic conditions. This can be done using a real indoor airship. Looking at how hardware that do not exceed the payload of the blimp can execute both methods and how the blimp will determine its position and orientation is recommended. It would also be interesting to see how different reward function affect the performance of the blimp. / Den obemannade luftfarkosten (UAV) har under de senaste åren blivit en teknik vars användning blivit allt vanligare i flera sektorer av det moderna samhället. Olika sorters UAV robotar associeras idag med en omfattande serie användningsområden, från katastrofhjälp till övervakning. Ett nyligen påbörjat initiativ från svenska sjöräddningssällskapet (SSRS) syftar till att implementera drönare i deras utryckningar. Genom att snabbt sända drönare till platsen i fråga, kan en bedömning göras innan personal kommer dit, vilket sparar tid och ökar sannolikheten för en framgångsrik räddningsaktion. En farkost som denna, som kommer att resa långa sträckor, måste förlita sig på ett navigationssystem som inte kräver att en operatör kontinuerligt ser farkosten. För att resa till sitt mål, eller söka av ett område, bör operatören kunna definiera en resväg som drönaren följer genom att ge den en serie vägpunkter. Som ett inledande steg mot den typen av system har denna uppsats utvecklat och testat begreppet vägpunktsnavigering på ett litet och långsamt luftskepp/blimp, i en simulerad inomhusmiljö. Huvudsakligen testades och jämfördes två vanligt förekommande navigationsalgoritmer. En inspirerad av en underkategori till maskininlärning: förstärkningsinlärning (RL), och den andra baserad på rapidly exploring random tree (RRT) algoritmen. Fyra experiment utfördes för jämföra båda metoderna med avseende på färdsträcka, medelhastighet, energieffektivitet samt robusthet gentemot ändringar i färdpunktskonfigurationerna. Resultaten visar att när blimpen kontrollerades av den bästa RL-baserade versionen åkte den generellt en mer avståndsmässigt optimal väg än när den RRT-baserade metoden användes. I de flesta fallen visade sig även RL-metoden vara mer robust mot förändringar i testbanorna, och presterade mer konsekvent över olika vägpunktskonfigurationer. RRT-metoden resulterade dock vanligtvis i en högre medelhastighet och energieffektivitet. RL-algoritmen hade också problem med att navigera banor där den behövde ta sig runt ett hinder. Sammanfattningsvis beror valet av algoritm på vilka parametrar som prioriteras av blimpoperatören för en viss bana. Om en hög hastighet och energieffektivitet är önskvärd rekommenderas den RRT-baserade metoden. Men om det är viktigt att blimpen reser så kort avstånd som möjligt mellan färdpunkterna, och har en jämnare prestanda, bör RL-metoden användas. För att ta nästa steg, mot en framtida implementering av båda metoder i räddningsoperationer, vore det rimligt att analysera deras prestanda under mer realistiska förhållanden. Detta skulle kunna göras inomhus med ett riktigt luftskepp. Författarna rekommenderar att undersöka om hårdvara som inte överstiger blimpens maxlast kan utföra båda metodernas beräkningar och hur blimpen bestämmer sin position och orientering. Det skulle också vara intressant att se hur olika belöningsfunktioner påverkar blimpens prestanda.

ITC/USA 2010 Conference Proceedings / The Forty-Sixth Annual International Telemetering Conference and Technical Exhibition / October 25-28, 2010 / Town and Country Resort & Convention Center, San Diego, California / Telemetry receivers must typically be located so that obstacles do not block the signal path. This can be challenging in geometrically complex indoor environments, such as factories, health care facilities, or offices. An accurate method for estimating the paths followed by typical telemetry transmitters in these environments can assist in system planning. It may be acceptable to provide marginal coverage to areas which are rarely visited, or areas which transmitters quickly transit. This paper discusses the use of the ant colony optimization and its application to the telemetry system planning problem.

In product manufacturing, assembly approximately represents 50% of the total work hours. Therefore, an efficient and fast assembly system is crucial to get competitive advantages at the global market and have the right product quality. Today, the verification of the assembly system is mostly done by utilizing software based simulation tools even though limitations have been identified. The purpose of this thesis is to identify when the use of additive manufacturing technology could be used in assessing the feasibility of the assembly system design. The research questions were threefold. First, identifying limitations that are connected with the used assembly simulation tools. Secondly, to investigate when additive manufacturing can act as a complement to these assembly simulations. Finally, to develop a framework that will assist the decision makers when to use additive manufacturing as a complement to assembly simulations. The researchers used the method of case study combined with a literature review. The case study collected data from semi-structured interviews, which formed the major portion of the empirical findings. Observations in a final assembly line and the additive manufacturing workshop provided valuable insights into the complexity of assembly systems and additive manufacturing technologies. In addition, document studies of the used visualization software at the case company resulted in an enhanced understanding of the current setting. The case study findings validate the limitations with assembly simulations described in theory. The most frequent ones are related to visibility, positioning, forces needed for the assembly operator, and accessibility between different parts. As both theory and case study findings are consistent in this respect, simulation engineers should be conscious of when to find other methods than simulation for designing the assembly system. One such alternative method is the utilization of additive manufacturing. The thesis outlines a number of situations where additive manufacturing indeed could act as a complement to assembly simulation. The authors argue that the results and findings to a large degree are applicable to other industries as the automotive sector is very global and competitive in nature and encompasses a large variety of complex assembly operations. A structured framework was also developed that could act as a decision support. The framework takes into account three dimensions that are crucial for the decision; (1) the assembly simulation limitation, (2) the context of the assembly and which parts are involved and (3) the possible limitations of additive manufacturing in the specific context. This impartial decision framework could help companies with complex assembly systems to know when to use additive manufacturing, as well as for which parts and subparts additive manufacturing is applicable. To increase the longevity of the decision framework, new improvements of assembly simulation tools and additive manufacturing technologies, respectively, should be incorporated in the framework.

Thesis (MEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2009. / This thesis relates to the successful development of an unmanned terrestrial vehicle (UTV) capable of operating in an obstacle ridden environment. The primary focus of the project is on the specific path planning algorithms. It is shown that specific methods of populating the obstacle-free space can be combined with methods of extracting the shortest path from these popula- tions. Through use of such combinations the successful generation of optimal collision-free paths is demonstrated. Previously developed modular architectures are combined and modified to create a UTV platform which meets all the requirements for implementation of navigational systems and path planning algorithms on board the platform. A two-dimensional kinematic state estimator is developed. This estimator makes use of extended Kalman Filter theory to optimally combine measurements from low cost sensors to yield the vehicle’s state vector. Lateral guidance controllers are developed to utilize this estimated state vector in a feedback control configuration. The entire system is then successfully demonstrated within a simulation environment. Finally, practical results from two days of test runs are provided in both written and interactive form

Thesis (MScEng)--Stellenbosch University, 2012 / ENGLISH ABSTRACT: This thesis presents the design and implementation of an autonomous navigational system and the automation of a practical demonstrator vehicle. It validates the proposed navigation architecture using simple functional navigational modules on the said vehicle. The proposed navigation architecture is a hierarchical structure, with a mission planner at the top, followed by the route planner, the path planner and a vehicle controller with the vehicle hardware at the base. A vehicle state estimator and mapping module runs in parallel to provide feedback data. The controls of an all terrain vehicle are electrically actuated and equipped with feedback sensors to form a complete drive-by-wire solution. A steering controller and velocity control state machine are designed and implemented on an existing on-board controller that includes a six degrees-of-freedom kinematic state estimator. A lidar scanner detects obstacles. The lidar data is mapped in real time to a local three-dimensional occupancy grid using a Bayesian update process. Each lidar beam is projected within the occupancy grid and the occupancy state of a ected cells is updated. A lidar simulation environment is created to test the mapping module before practical implementation. For planning purposes, the three-dimensional occupancy grid is converted to a two-dimensional drivability map. The path planner is an adapted rapidly exploring random tree (RRT) planner, that assumes Dubins car kinematics for the vehicle. The path planner optimises a cost function based on path length and a risk factor that is derived from the drivability map. A simple mission planner that accepts user-de ned waypoints as objectives is implemented. Practical tests veri ed the potential of the navigational structure implemented in this thesis. / AFRIKAANSE OPSOMMING: In hierdie tesis word die ontwerp en implementering van 'n outonome navigasiestelsel weergegee, asook die outomatisering van 'n praktiese demonstrasievoertuig. Dit regverdig die voorgestelde navigasie-argitektuur op die bogenoemde voertuig deur gebruik te maak van eenvoudige, funksionele navigasie-modules. Die voorgestelde navigasie-argitektuur is 'n hi erargiese struktuur, met die missie-beplanner aan die bo-punt, gevolg deur die roetebeplanner, die padbeplanner en voertuigbeheerder, met die voertuighardeware as basisvlak. 'n Voertuigtoestandsafskatter en karteringsmodule loop in parallel om terugvoer te voorsien. Die kontroles van 'n vierwiel-motor ets is elektries geaktueer en met terugvoersensors toegerus om volledig rekenaarbeheerd te wees. 'n Stuur-beheerder en 'n snelheid-toestandmasjien is ontwerp en ge mplementeer op 'n bestaande aanboordverwerker wat 'n kinematiese toestandsafskatter in ses grade van vryheid insluit. 'n Lidar-skandeerder registreer hindernisse. Die lidar-data word in re ele tyd na 'n lokale drie-dimensionele besettingsrooster geprojekteer deur middel van 'n Bayesiese opdateringsproses. Elke lidar-straal word in die besettingsrooster geprojekteer en die besettingstoestand van betrokke selle word opdateer. 'n Lidar-simulasie-omgewing is geskep om die karteringsmodule te toets voor dit ge mplementeer word. Die drie-dimensionele besettingsrooster word na 'n twee-dimensionele rybaarheidskaart verwerk vir beplanningsdoeleindes. Die padbeplanner is 'n aangepaste spoedig-ontdekkende-lukrake-boom en neem Dubinskar kinematika vir die voertuig aan. Die padbeplanner optimeer 'n koste-funksie, gebaseer op padlengte en 'n risiko-faktor, wat vanaf die rybaarheidskaart verkry word. 'n Eenvoudige missie-beplanner, wat via-punte as doelstellings neem, is ge mplementeer. Praktiese toetsritte veri eer die potensiaal van die navigasiestruktuur, soos hier beskryf.

In farming operation, machinery represents a major cost; therefore, good fleet management can have a great impact on the producer’s profit, especially considering the increasing costs of fuel and production inputs in recent years. One of the tasks to be accomplished in order to improve fleet management is planning the path that the machine should take to cover the field while working. Information such as distance traveled, time and fuel consumption as well as agricultural inputs wasted due to off-target-application areas are crucial in the path planning process. Parameters such as field boundary size and geometry, machine total width as well as control width resolution present a great impact on the information necessary for path planning. Researchers around the world have proposed methods that approach specific aspects related to path planning, the majority addressing machine field efficiency per-se, which a function of total time spent in the field as well as effective working time. However, wasted inputs due to off-target-application areas in the maneuvering regions, especially in oddly shaped agricultural fields might be as important as field efficiency when it comes down to the total operation cost. Thus, the main purpose of this research was to develop a path planning method that accounts for not only machinery field efficiency, but also the supply inputs. This research was accomplished in a threefold approach where in the first step an algorithm for computing off-target application area was developed, implemented and validated resulting in a computational tool that can be used to evaluate potential savings when using automatic section control on agricultural fields of complex field boundary. This tool allowed accomplishment of the second step, which was an investigation and better understanding of field size and shape as well as machine width of the effects on off-target application areas resulting in an empirical method for such estimations based on object shape descriptors. Finally, a path planning algorithm was developed and evaluated taking into consideration the aspects of machine field efficiency as well as off-target application areas.

In this thesis, a cognitive neural predictive controller system has been designed to guide a nonholonomic wheeled mobile robot during continuous and non-continuous trajectory tracking and to navigate through static obstacles with collision-free and minimum tracking error. The structure of the controller consists of two layers; the first layer is a neural network system that controls the mobile robot actuators in order to track a desired path. The second layer of the controller is cognitive layer that collects information from the environment and plans the optimal path. In addition to this, it detects if there is any obstacle in the path so it can be avoided by re-planning the trajectory using particle swarm optimisation (PSO) technique. Two neural networks models are used: the first model is modified Elman recurrent neural network model that describes the kinematic and dynamic model of the mobile robot and it is trained off-line and on-line stages to guarantee that the outputs of the model will accurately represent the actual outputs of the mobile robot system. The trained neural model acts as the position and orientation identifier. The second model is feedforward multi-layer perceptron neural network that describes a feedforward neural controller and it is trained off-line and its weights are adapted on-line to find the reference torques, which controls the steady-state outputs of the mobile robot system. The feedback neural controller is based on the posture neural identifier and quadratic performance index predictive optimisation algorithm for N step-ahead prediction in order to find the optimal torque action in the transient to stabilise the tracking error of the mobile robot system when the trajectory of the robot is drifted from the desired path during transient state. Three controller methodologies were developed: the first is the feedback neural controller; the second is the nonlinear PID neural feedback controller and the third is nonlinear inverse dynamic neural feedback controller, based on the back-stepping method and Lyapunov criterion. The main advantages of the presented approaches are to plan an optimal path for itself avoiding obstructions by using intelligent (PSO) technique as well as the analytically derived control law, which has significantly high computational accuracy with predictive optimisation technique to obtain the optimal torques control action and lead to minimum tracking error of the mobile robot for different types of trajectories. The proposed control algorithm has been applied to monitor a nonholonomic wheeled mobile robot, has demonstrated the capability of tracking different trajectories with continuous gradients (lemniscates and circular) or non-continuous gradients (square) with bounded external disturbances and static obstacles. Simulations results and experimental work showed the effectiveness of the proposed cognitive neural predictive control algorithm; this is demonstrated by the minimised tracking error to less than (1 cm) and obtained smoothness of the torque control signal less than maximum torque (0.236 N.m), especially when external disturbances are applied and navigating through static obstacles. Results show that the five steps-ahead prediction algorithm has better performance compared to one step-ahead for all the control methodologies because of a more complex control structure and taking into account future values of the desired one, not only the current value, as with one step-ahead method. The mean-square error method is used for each component of the state error vector to compare between each of the performance control methodologies in order to give better control results.

Trajectory planning is an important component of autonomous driving. It takes the result of route-level navigation plan and generates the motion-level commands that steer an autonomous passenger vehicle (APV). Prior work on solving this problem uses either a sampling-based or optimization-based trajectory planner, accompanied by some high-level rule generation components.

This dissertation has three contributions in the area of path planning for Unmanned Aerial Vehicle (UAV) Search And Track (SAT) missions. These contributions are: (a) the study of a novel metric, G, used to quantify the value of the target information gained during a search and track mission, (b) an optimal planning horizon that minimizes time-error of a planning horizon when interrupted by Poisson random events, and (c) a modified Particle Swarm Optimization (PSO) algorithm for search missions that uses the prior target distribution in the generation of paths rather than just in the evaluation of them. UAV route planning is an important topic with many applications. Of these, military applications are the best known. This dissertation focuses on route planning for SAT missions that jointly optimize the conflicting objectives of detecting new targets and monitoring previously detected targets. The information theoretic approach proposed here is different from and is superior to existing approaches. One of the main differences is that G quantifies the value of the target information rather than the information itself. Several examples are provided to highlight G’s desirable properties. Another important component of path planning is the selection of a planning horizon, which specifies the amount of time to include in a plan. Unfortunately, little research is available to aid in the selection of a planning horizon. The proposed planning horizon is derived in the context of plan updates triggered by Poisson random events. To our knowledge, it is the only theoretically derived horizon available making it an important contribution. While the proposed horizon is optimal in minimizing planning time errors, simulation results show that it is also near optimal in minimizing the average time needed to capture an evasive target. The final contribution is the modified PSO. Our modification is based on the idea that PSO should be provided with the target distribution for path generation. This allows the algorithm to create candidate path plans in target rich regions. The modified PSO is studied using a search mission and is used in the study of G.

Les systèmes de transport intelligents (STI) sont conçus pour améliorer les transports, réduire les accidents, le temps de transport et la consommation de carburant, tout en augmentant la sécurité, le confort et l'efficacité de conduite. L'objectif final de ITS est de développer ADAS pour faciliter les tâches de conduite, jusqu'au développement du véhicule entièrement automatisé. Les systèmes actuels ne sont pas assez robustes pour atteindre un niveau entièrement automatisé à court terme. Les environnements urbains posent un défi particulier, car le dynamisme de la scène oblige les algorithmes de navigation à réagir en temps réel aux éventuels changements, tout en respectant les règles de circulation et en évitant les collisions avec les autres usagers de la route. Sur cette base, cette thèse propose une approche de la planification locale en deux étapes pour apporter une solution au problème de la navigation en milieu urbain. Premièrement, les informations statiques des contraintes de la route et du véhicule sont considérées comme générant la courbe optimale pour chaque configuration de virage réalisable, où plusieurs bases de données sont générées en tenant compte de la position différente du véhicule aux points de début et de fin des courbes, permettant ainsi une analyse réaliste. planificateur de temps pour analyser les changements de concavité en utilisant toute la largeur de la voie. Ensuite, la configuration réelle de la route est envisagée dans le processus en temps réel, où la distance disponible et la netteté des virages à venir et consécutifs sont étudiées pour fournir un style de conduite à la manière humaine optimisant deux courbes simultanément, offrant ainsi un horizon de planification étendu. Par conséquent, le processus de planification en temps réel recherche le point de jonction optimal entre les courbes. Les critères d’optimalité minimisent à la fois les pics de courbure et les changements abrupts, en recherchant la génération de chemins continus et lisses. Quartic Béziers est l'algorithme d'interpolation utilisé en raison de ses propriétés, permettant de respecter les limites de la route et les restrictions cinématiques, tout en permettant une manipulation facile des courbes. Ce planificateur fonctionne à la fois pour les environnements statiques et dynamiques. Les fonctions d'évitement d'obstacles sont présentées en fonction de la génération d'une voie virtuelle qui modifie le chemin statique pour effectuer chacune des deux manoeuvres de changement de voie sous la forme de deux courbes, convertissant le problème en un chemin statique. Ainsi, une solution rapide peut être trouvée en bénéficiant du planificateur local statique. Il utilise une discrétisation en grille de la scène pour identifier l'espace libre nécessaire à la construction de la route virtuelle, où le critère de planification dynamique consiste à réduire la pente pour les changements de voie. Des essais de simulation et des tests expérimentaux ont été réalisés pour valider l'approche dans des environnements statiques et dynamiques, adaptant la trajectoire en fonction du scénario et du véhicule, montrant la modularité du système. / Intelligent Transportation Systems (ITS) developments are conceived to improve transportation reducing accidents, transport time and fuel consumption, while increasing driving security, comfort and efficiency. The final goal of ITS is the development of ADAS for assisting in the driving tasks, up to the development of the fully automated vehicle. Despite last ADAS developments achieved a partial-automation level, current systems are not robust enough to achieve fully-automated level in short term. Urban environments pose a special challenge, since the dynamism of the scene forces the navigation algorithms to react in real-time to the eventual changes, respecting at the same time traffic regulation and avoiding collisions with other road users. On this basis, this PhD thesis proposes a two-staged local planning approach to provide a solution to the navigation problem on urban environments. First, static information of both road and vehicle constraints is considered to generate the optimal curve for each feasible turn configuration, where several databases are generated taking into account different position of the vehicle at the beginning and ending points of the curves, allowing the real-time planner to analyze concavity changes making use of the full lane width.Then, actual road layout is contemplated in the real-time process, where both the available distance and the sharpness of upcoming and consecutive turns are studied to provide a human-like driving style optimizing two curves concurrently, offering that way an extended planning horizon. Therefore, the real-time planning process searches the optimal junction point between curves. Optimality criteria minimizes both curvature peaks and abrupt changes on it, seeking the generation of continuous and smooth paths. Quartic Béziers are the interpolating-based curve algorithm used due to their properties, allowing compliance with road limits and kinematic restrictions, while allowing an easy manipulation of curves. This planner works both for static and dynamic environments. Obstacle avoidance features are presented based on the generation of a virtual lane which modifies the static path to perform each of the two lane change maneuvers as two curves, converting the problem into a static-path following. Thus, a fast solution can be found benefiting from the static local planner. It uses a grid discretization of the scene to identify the free space to build the virtual road, where the dynamic planning criteria is to reduce the slope for the lane changes. Both simulation and experimental test have been carried out to validate the approach, where vehicles performs path following on static and dynamic environments adapting the path in function of the scenario and the vehicle, testing both with low-speed cybercars and medium-speed electic platforms, showing the modularity of the system.