Denna rapport har jämfört olika path tracking-algoritmer för att se vilken som presterar bäst för spelagenter som färdas i konstant hastighet. Tre vanliga path tracking algoritmer som ofta används inom robotik har valts ut för undersökningen, Follow The Carrot, Pure Pursuit och Vector Pursuit. Algoritmerna har implementerats i C# och simuleringarna har genomförts i Unity 4.0. Path tracking-algoritmerna har testats på ett antal olika vägar för att se hur de lyckas följa vägen. Av simuleringarna så visar det sig att Pure Pursuit och Vector Pursuit presterade likvärdigt för spelagenter i konstant hastighet samt att de presterade bättre än Follow The Carrot.

Cette thèse traite de la question du contrôle du mouvement d'engins non-holonomes et sous-actionnés évoluant de manière coordonnée et autonome. Les différentes approches considérées sont le suivi de trajectoire (Trajectory Tracking TT) et le suivi de chemin (path following PF). Une nouvelle méthode de contrôle est proposée. Dénommée Path-Tracking (PT), elle permet de cumuler les avantages de chacune des deux précédentes méthodes, permettant de cumuler la souplesse de la convergence induite par le suivi de chemin avec le respect des contraintes temporelles du suivi de trajectoire. L'étude et la réalisation de la commande démarre avec l'étude du cas du robot nonholonome de type Unicycle' et se base sur les principes de Lyapunov' et de Backstepping'. Ces premiers résultats sont ensuite étendus au cas d'un véhicule sous-marin sous-actionné de type AUV (Autonomous Underwater Vehicle'), en analysant les similarités cinématiques entre ces deux types de véhicules. De plus, il est montré la nécessité de prendre en compte les propriétés dynamiques du système de type AUV, et la condition de Stern dominancy' est établie de façon à garantir que le problème est bien posé et ainsi que la commande soit aisément calculable. Dans la cas d'un système marin sur-actionné, qui peut ainsi effectuer des tâches de navigation au long cours et de positionnement désiré (Station keeping'), une commande hybride est proposée. Enfin, la question du contrôle coordonné d'une formation d'engins marin est abordée. Les colutions de commande pour les taches de suivi de chemin coordonné (coordinated path following') et de coordinated path tracking' sont proposées. Les principes du leader-follower' et la méthode des structures virtuelles sont ainsi traitées dans un cadre de contrôle centralisé, et le cas décentralisé est traité en utilisant certains principes de théorie des graphes. / In this dissertation, the problems of motion control of underactuated autonomous vehicles are addressed,namely trajectory tracking (TT), path following (PF), and novelly proposed path tracking whichblending the PF and TT together in order to achieve smooth spatial convergence and tight temporalperformance as well.The control design is firstly started from the benchmark case of nonholonomic unicycle-type vehicles,where the Lyapunov-based design and backstepping technique are employed, and then it is extendedto the underactuated AUVs based on the similarity between the control inputs of two kinds of vehicles.Moreover, dealing with acceleration of side-slip angle is highlighted and stern-dominant property of AUVsis standing out in order to achieve well-posed control computation. Transitions of motion control fromunderactuated to fully actuated AUVs are also proposed.Finally, coordinated formation control of multiple autonomous vehicles are addressed in two-folds,including coordinated paths following and coordinated paths tracking, based on leader-follower andvirtual structure method respectively under the centralized control framework, and then solved underdecentralized control framework by resorting to algebraic graph theory.

The purpose of this project is to implement obstacle avoidance algorithms to drive the articulated vehicle autonomously in an unknown environment, which is simulated by AgX Dynamics™ simulation software and controlled by Matlab® programming software. Three driving modes are developed for driving the vehicle (Manual, Semi-autonomous and Autonomous) in this project. Path tracking algorithms and obstacle avoidance algorithms are implemented to navigate the vehicle. A GUI was built and used for the manual driving mode in this project. The semi-autonomous mode checked different cases: change lanes, U-turn, following a line, following a path and figure 8 course. The autonomous mode is implemented to drive the articulated vehicle in an unknown environment with moving to a pose path tracking algorithm and VFH+ obstacle avoidance algorithm. Thus, the simulation model and VFH+ obstacle avoidance algorithm seems to be working fine and still can be improved for the autonomous vehicle. The result of this project showed a good performance of the simulation model. Moreover, this simulation software helps to minimize the cost of the articulated vehicle since all tests are in the simulation rather than in the reality.

Abstract The aim of this work is to improve the overall robustness in equation-oriented chemical engineering simulation work. Because the performance of locally convergent solving methods is strongly dependent on a favourable initial guess, bounded homotopy methods were investigated as a way to enlarge the domain of convergence. Bounded homotopies make it possible to keep the homotopy path inside a feasible problem domain. Thus the fatal errors possibly caused by unfeasible variable values in thermodynamic subroutines can be avoided. To enable the utilization of a narrow bounding zone, modifications were proposed for bounded homotopies. The performance of the modifications was studied with simple test problems and several types of distillation systems in the MATLAB environment. The findings illustrate that modified bounded homotopies with variables mapping make it possible to bound the homotopy path strictly to run inside a feasible problem domain. The homotopy path can be tracked accurately and flexibly also inside a narrow bounding zone. It was also noticed that by utilizing the concept of bounding the homotopy path with respect to the homotopy parameter, the possibility of approaching starting point and solution multiplicities is increased in cases where the traditional problem-independent homotopy method fails. The concept aims to connect separate homotopy path branches thus offering a trackable path with real space arithmetic. Even though the modified bounded homotopies were found to overcome several challenges often encountered with traditional problem-independent homotopy continuation methods, alone they are not enough to guarantee that the solution is approached from an arbitrary starting point. Therefore, problem-tailored solving procedures were implemented in the consideration of complex column configurations. Problem-tailored solving procedures aim to offer feasible consecutive sub-problems and thus direct the solving towards the state distribution that fulfils exact product purity specifications. As a whole, the modified bounded homotopies and problem-tailored solving procedures were found to improve the overall robustness of an equation-oriented solving approach. Thus the threshold for designing and implementing complex process systems such as complex distillation configurations for practical use could be lowered.

The automotive industry has been involved in making vehicles autonomous to different levels in the past decade rapidly. Particularly in the commercial vehicle market, there is a significant necessity to make trucks have a certain level of automation to help reduce dependence on human efforts to drive. This could help in reducing several accidents caused by human error. Interestingly there are several challenges and solutions in achieving and implementing autonomous driving for trucks. First, a benchmark of different control architectures that can make a truck drive autonomously are explored. The chosen controllers (Pure Pursuit, Stanley, Linear Quadratic Regulator, Sliding Mode Control and Model Predictive Control) vary in their simplicity in implementation and versatility in handling different vehicle parameters and constraints. A thorough comparison of these path tracking controllers are performed using several metrics. Second, a collision avoidance system based on cubic polynomials, inspired by rapidly exploring random tree (RRT) is presented. Some of the path tracking controllers are limited by their ability and hence a standalone collision avoidance system is needed to provide safe maneuvering. Simulations are performed for different test cases with and without obstacles. These simulations help compare safety margin and driving comfort of each path tracking controller that are integrated with the collision avoidance system. Third, different performance metrics like change in acceleration input, change in steering input, error in path tracking, deviation from base frame of track file and lateral and longitudinal margin between ego and target vehicle are presented. To conclude, a set of suitable controllers for heavy articulated vehicles are developed and benchmarked. / Bilindustrin har varit involverad i att göra fordon autonoma till olika nivåer under det senaste decenniet snabbt. Särskilt på marknaden för kommersiella fordon finns det ett stort behov av att få lastbilar att ha en viss nivå av automatisering för att minska beroendet av mänskliga ansträngningar att köra. Detta kan hjälpa till att minska flera olyckor orsakade av mänskliga fel. Intressant nog finns det flera utmaningar och lösningar för att uppnå och implementera autonom körning för lastbilar. Först utforskas ett riktmärke av olika styrarkitekturer som kan få en lastbil att köra autonomt. De valda kontrollerna (Pure Pursuit, Stanley, Linear Quadratic Regulator, Sliding Mode Control och Model Predictive Control) varierar i sin enkelhet i implementering och mångsidighet när det gäller att hantera olika fordonsparametrar och begränsningar. En grundlig jämförelse av dessa vägspårningskontroller utförs med hjälp av flera mätvärden. För det andra presenteras ett system för undvikande av kollisioner baserat på kubiska polynom, inspirerat av snabbt utforskande slumpmässiga träd (RRT). Vissa av vägspårningskontrollerna är begränsade av sin förmåga och därför behövs ett fristående system för att undvika kollisioner för att ge säker manövrering. Simuleringar utförs för olika testfall med och utan hinder. Dessa simuleringar hjälper till att jämföra säkerhetsmarginal och körkomfort för varje vägspårningskontroller som är integrerade med kollisionsundvikande systemet. För det tredje presenteras olika prestandamått som förändring i accelerationsinmatning, förändring i styrinmatning, fel i banspårning, avvikelse från basramen för spårfilen och lateral och longitudinell marginal mellan ego och målfordon. Avslutningsvis utvecklas och benchmarkas en uppsättning lämpliga styrenheter för tunga ledade fordon.

For the past couple of years researchers around theworld have tried to develop fully autonomous vehicles. One of theproblems that they have to solve is how to navigate in a dynamicworld with ever-changing variables. This project was initiated tolook into one scenario of the path planning problem; overtakinga human driven vehicle. Model Predictive Control (MPC) hashistorically been used in systems with slower dynamics but withadvancements in computation it can now be used in systems withfaster dynamics. In this project autonomous vehicles controlledby MPC were simulated in Python based on the kinematic bicyclemodel. Constraints were posed on the overtaking vehicle suchthat the two vehicles would not collide. Results show that anovertake, that keeps a proper distance to the other vehicle andfollows common traffic laws, is possible in certain scenarios. / Under de senaste åren har forskare världen över försökt utveckla fullt autonoma fordon. Ett av problemen som behöver lösas är hur man navigerar i en dynamisk värld med ständigt förändrande variabler. Detta projekt startades för att titta närmare på en aspekt av att planera en rutt; att köra om ett mänskligt styrt fordon. Model Predictive Control (MPC) har historiskt sett blivit använt i system med långsammare dynamik, men med framsteg inom datorers beräkningskraft kan det nu användas i system med snabbare dynamik. I detta projekt simulerades självkörande fordon, styrda av MPC, i Python. Fordonsmodellen som används var kinematic bicycle model. Begränsningar sattes på det omkörande fordonet så att de två fordonen inte kolliderar. Resultaten visar att en omkörning, som håller avstånd till det andra fordonet samt följer trafikregler, är möjligt i vissa scenarion. / Kandidatexjobb i elektroteknik 2020, KTH, Stockholm

Advanced Driver Assistance Systems (ADAS) and Autonomous Ground Vehicles (AGV) have the potential to increase road transportation safety, environmental gains, and passenger comfort. The advent of Electric Vehicles has also facilitated greater flexibility in powertrain architectures and control capabilities. Path Tracking controllers that provide steering input are used to execute lateral maneuvers or model the response of a vehicle during cornering. Direct Yaw Control using Torque Vectoring has the potential to improve vehicle's transient cornering stability and modify its steady state handling characteristics during lateral maneuvers. In the first part of this thesis, the transient dynamics of an existing baseline Path Tracking controller is improved using a transient Torque Vectoring algorithm. The existing baseline Path Tracking controller is evaluated, using a linearized system, for a range of vehicle and controller parameters. The effect of implementing transient Torque Vectoring along with the baseline Path Tracking controller is then studied for the same parameter range. The linear analysis shows, in both time and frequency domain, that the transient Torque Vectoring improves vehicle response and stability during cornering. A Torque Vectoring controller is developed in Linear Adaptive Model Predictive Control framework and it's performance is verified in simulation using Simulink and CarSim. The second part of the thesis analyzes the tradeoff enabled by steady state Torque Vectoring between improved limit handling capability through optimal tire force allocation and drivability demonstrated by understeer gradient. Optimal tire force allocation prescribes equal usage in all four tires during maneuvers. This is enabled using steering and Torque Vectoring. An analytical proof is presented which demonstrates that implementation of this optimal tire force allocation results in neutralsteering handling characteristics for the vehicle. The optimal tire force allocation strategy is formulated as a minimax optimization problem. A two-track vehicle model is simulated for this strategy, and it verified the analytical proof by displaying neutralsteering behavior. / Master of Science / Advanced Driver Assistance Systems (ADAS) and Autonomous Ground Vehicles (AGVs) have the potential to increase road transportation safety, environmental gains, passenger comfort and passenger productivity. The advent of Electric Vehicles (EVs) has also facilitated greater flexibility in powertrain configurations and capabilities that facilitate the implementation of Torque Vectoring (TV), which is a method of applying differential torques to laterally opposite wheels to enhance the cornering performance of ground vehicles. Path Tracking (PT) controllers that provide steering input to the vehicles are traditionally used for lateral control in AGVs and ADAS features. The goal of this thesis is to develop Torque Vectoring algorithms to improve a vehicle's stability and shape its steady state behaviour through a corner during low lateral acceleration maneuvers. An existing baseline Path Tracking controller is selected and evaluated. The effect of implementing Torque Vectoring along with this Path Tracking controller is studied and it is found to improve the stability of the vehicle during cornering. This is verified in simulation by designing and implementing the Torque Vectoring algorithm. Finally, a Torque Vectoring strategy is proposed to manage the handling of the vehicle during low acceleration cornering.

Ces travaux de recherche portent sur la commande et la planification de trajectoires pour la navigation de véhicules autonomes. Ils se situent dans le cadre d'un projet très ambitieux lancé par le laboratoire Heudiasyc sur la conduite autonome à grande vitesses (vitesse longitudinale supérieure à 5m/s ~= 18 km/h). Pour proposer des solutions à cette problématique, après avoir réalisé une large recherche bibliographique sur la commande et la planification des trajectoires des véhicules autonomes, plusieurs contributions ont été présentées. En ce qui concerne la commande des véhicules autonomes, un contrôleur latéral par mode glissant d'ordre supérieur a été proposé. Compte tenu de la ressemblance implicite entre le mode glissant et le principe d'immersion et d'invariance (I&I), deux contrôleurs utilisant le principe d'immersion et d'invariance ont été proposés par la suite pour améliorer les performances par rapport au mode glissant. Le développement de ces nouveaux contrôleurs nous a permis de garantir une stabilité robuste pour tous les gains positifs des contrôleurs I&I. Ce résultat nous a conduit à étudier les propriétés intrinsèques du système. Une étude des propriétés de passivité du système a révélé des caractéristiques de passivité intéressantes. Par la suite, nous avons développé un contrôleur robuste basé sur la passivité. Concernant la navigation, nous avons développé deux algorithmes de navigation basés sur la méthode des tentacules. Ceci dans le but d'améliorer la méthode de base. Les résultats de la simulation montrent que les algorithmes donnent de bons résultats vis-à-vis des objectifs attendus d'évitement d'obstacles et de suivi de la trajectoire globale de référence. Les algorithmes de commande et de planification de trajectoires développés ont été validés en simulation hors-ligne avec des données réelles après avoir été testés sur un simulateur réaliste. / My research focuses on trajectory planning and control of autonomous vehicles. This work is a part of an extremely ambitious project launched by the Heudiasyc laboratory about autonomous driving at high speed (longitudinal speed greater to 5m/s ~= 18 km/h). With regard to the control of autonomous vehicles at high speed, a lateral controler using higher-order sliding mode control is proposed. Given the implicit similarity between the sliding mode and the principle of immersion and invariance, two controllers using the principle of immersion and invariance have been subsequently proposed in order to improve the performance with respect to the sliding mode. The development of these new controllers shows very strong robust stability which leads us to study the intrinsic properties of the system. A study of the passivity properties of the system is also crried out, showing some interesting characteristics of the system. Hence, a robust passivity-based controller has been developed. Regarding the navigation, we have developed two navigation algorithms based on the tentacles method. Subsequently, a feasibility study of trajectory generation strategies for high speed driving is conducted. The outcome of the simulation proved that the algorithms gave out good results with respect to the expected ogjectives of obstacle avoidance and global reference path following. Control and motion planning algorithms developed were validated offline by simulation with real data. They have been also tested on a realistic simulator.

Cette thèse de doctorat s’intéresse au suivi de trajectoire basé sur la perception visuelle et la localisation en milieu urbain. L'approche proposée comprend deux systèmes. Le premier concerne la perception de l'environnement. Cette tâche est effectuée en utilisant des techniques d'apprentissage profond pour extraire automatiquement les caractéristiques visuelles 2D et utiliser ces derniers pour apprendre à distinguer les différents objets dans les scénarios de conduite. Trois techniques d'apprentissage approfondi sont adoptées : la segmentation sémantique pour assigner chaque pixel d’une image à une classe, la segmentation d'instance pour identifier les instances séparées de la même classe et la classification d'image pour reconnaître davantage les étiquettes spécifiques des instances. Ici, notre système considère 15 classes d'objets et reconnaît les panneaux de signalisation. Le deuxième système fait référence au suivi de chemin numérisé. Dans un premier temps, le véhicule équipé enregistre d'abord l'itinéraire avec un système de vision stéréo et un récepteur GPS (étape d'apprentissage ou numérisation du chemin). Ensuite, le système proposé analyse hors ligne la trajectoire GPS et identifie exactement les emplacements des courbes dangereuses (brusques) et les limitation de vitesse via les données visuelles. Enfin, une fois que le véhicule est capable de se localiser lui-même durant la phase de suivi de chemin, le module de contrôle du véhicule piloté avec notre algorithme de négociation de vitesse, prend en compte les informations extraites et calcule la vitesse idéale à exécuter. Grâce aux résultats expérimentaux des deux systèmes, nous prouvons que le premier est capable de détecter et de reconnaître précisément les objets d'intérêt dans les scénarios urbains, tandis que le suivi de trajectoire réduit significativement les erreurs latérales entre le trajet appris et le trajet parcouru. Nous soutenons que la fusion des deux systèmes améliorera le suivi de chemin pour prévenir les accidents ou assurer la conduite autonome. / This PhD thesis focuses on developing a path tracking approach based on visual perception and localization in urban environments. The proposed approach comprises two systems. The first one concerns environment perception. This task is carried out using deep learning techniques to automatically extract 2D visual features and use them to learn in order to distinguish the different objects in the driving scenarios. Three deep learning techniques are adopted: semantic segmentation to assign each image pixel to a class, instance segmentation to identify separated instances of the same class and, image classification to further recognize the specific labels of the instances. Here our system segments 15 object classes and performs traffic sign recognition. The second system refers to path tracking. In order to follow a path, the equipped vehicle first travels and records the route with a stereo vision system and a GPS receiver (learning step). The proposed system analyses off-line the GPS path and identifies exactly the locations of dangerous (sharp) curves and speed limits. Later after the vehicle is able to localize itself, the vehicle control module together with our speed negotiation algorithm, takes into account the information extracted and computes the ideal speed to execute. Through experimental results of both systems, we prove that, the first one is capable to detect and recognize precisely objects of interest in urban scenarios, while the path tracking one reduces significantly the lateral errors between the learned and traveled path. We argue that the fusion of both systems will ameliorate the tracking approach for preventing accidents or implementing autonomous driving.

In this thesis, a combined feedforward and feedback controller for improved path tracking on autonomous heavy-duty vehicles is designed and implemented. The steering wheel is controlled in order to follow a reference curvature, computed by a higher-level MPC, responsible for minimizing the distance to a planned path. The steering dynamics, from steering wheel via wheel angles, to a measurable vehicle curvature, is modeled, and a conversion from desired curvature gain to input angle to the steering wheel is derived. Tests with an autonomous Scania R580 show that the desired curvature can be followed with satisfactory small error, both in a designed slalom path and on a more generic test track. By utilizing future curvature values computed by the MPC, a non-causal feedforward controller can reduce the delay from input to the steering wheel to a measured response in curvature, by almost two thirds, compared to the currently implemented solution. Compared to an open-loop control design, tests in simulation show that a feedback controller can reduce errors in curvature gain. However, with the identified steering dynamics and the improved conversion from steering wheel angle to curvature, no further improvement in the curvature gain was seen when implementing the feedback controller in the test vehicle. Care must also be taken not to introduce instability in the system when the feedback controller is implemented in series with a high-level MPC. / Den här rapporten beskriver design och implementering av en regulator med kombinerad framkoppling och återkoppling för förbättrad banföljning av autonoma tunga fordon. Fordonets ratt styrs för att följa en kurvaturreferens beräknad av en överordnad MPC, ansvarig för att minimera avståndet till en planerad bana. Dynamiken i styrningen, från ratten via hjulvinklarna till en mätbar kurvatur för fordonet, är modellerad. En översättning från önskad förstärkning av kurvatur till insignal för rattvinkeln är också framtagen. Tester utförda med en autonom Scania R580 visar att den önskade kurvaturen kan följas med tillfredsställande litet fel, både i en egendesignad slalombana och i en mer generisk testbana. Genom att utnyttja framtida referensvärden för kurvatur beräknade av MPC:n, kan en icke-kausal framkopplande regulator minska fördröjningen från insignal till ratten till en mätbar respons i fordonets kurvatur. Jämfört med den nuvarande lösningen minskas fördröjningen med nästan två tredjedelar. Jämfört med en öppen styrning visar tester i simulering att en återkoppling i regulatorn kan minska stationära fel i kurvatur. Med implementeringen av den identifierade styrdynamiken och den förbättrade översättningen från rattvinkel till kurvatur, syntes dock med återkoppling ingen ytterliggare förbättring i testfordonet. Implementering av den återkopplande regulatorn i serie med den överordnade MPC:n behöver också göras med omsorg för att inte introducera instabilitet i systemet.