Modeling, Control and Automatic Code Generation for a Two-Wheeled Self-Balancing Vehicle Using Modelica

The main goal of this project was to use the Modelica features on embedded systems, real-time systems and basic mechanical modeling for the control of a two-wheeled self-balancing personal vehicle. The Elektor Wheelie, a Segway-like vehicle, was selected as the process to control. Modelica is an object-oriented language aimed at modeling of complex systems. The work in the thesis used the Modelica-based modeling and simulation tool Dymola. The Elektor Wheelie has an 8-bit programmable microcontroller (Atmega32) which was used as control unit. This microcontroller has no hardware support for floating point arithmetic operations and emulation via software has a high cost in processor time. Therefore fixed-point representation of real values was used as it only requires integer operations. In order to obtain a linear representation which was useful in the control design a simple mechanical model of the vehicle was created using Dymola. The control strategy was a linear quadratic regulator (LQR) based on a state space representation of the vehicle. Two methods to estimate the platform tilt angle were tested: a complementary filter and a Kalman filter. The Kalman filter had a better performance estimating the platform tilt angle and removing the gyroscope drift from the angular velocity signal. The state estimators as well as the controller task were generated automatically using Dymola; the same tasks were programmed manually using fixed-point arithmetic in order to evaluate the feasibility of the Dymola automatically generated code. At this stage, it was shown that automatically generated fixed-point code had similar results compared to manual coding after slight modifications were made. Finally, a simple communication application was created which allowed real-time plotting of state variables and remote controlling of the vehicle, using elements of Modelica EmbeddedSystems library

Terrängkartläggning för autonoma fordon

Autonomous vehicles have become the forefront of the automotive industry nowadays, looking to have safer and more efficient transportation systems. One of the main issues for every autonomous vehicle consists in being aware of its position and the presence of obstacles along its path. The current project addresses the pose and terrain mapping problem integrating a visual odometry method and a mapping technique. An RGB-D camera, the Kinect v2 from Microsoft, was chosen as sensor for capturing information from the environment. It was connected to an Intel mini-PC for real-time processing. Both pieces of hardware were mounted on-board of a four-wheeled research concept vehicle (RCV) to test the feasibility of the current solution at outdoor locations. The Robot Operating System (ROS) was used as development environment with C++ as programming language. The visual odometry strategy consisted in a frame registration algorithm called Adaptive Iterative Closest Keypoint (AICK) based on Iterative Closest Point (ICP) using Oriented FAST and Rotated BRIEF (ORB) as image keypoint extractor. A grid-based local costmap rolling window type was implemented to have a two-dimensional representation of the obstacles close to the vehicle within a predefined area, in order to allow further path planning applications. Experiments were performed both offline and in real-time to test the system at indoors and outdoors scenarios. The results confirmed the viability of using the designed framework to keep tracking the pose of the camera and detect objects in indoor environments. However, outdoor environments evidenced the limitations of the features of the RGB-D sensor, making the current system configuration unfeasible for outdoor purposes.Autonoma fordon har blivit spetsen för bilindustrin i dag i sökandet efter säkrare och effektivare transportsystem. En av de viktigaste sakerna för varje autonomt fordon består i att vara medveten om sin position och närvaron av hinder längs vägen. Det aktuella projektet behandlar position och riktning samt terrängkartläggningsproblemet genom att integrera en visuell distansmätnings och kartläggningsmetod. RGB-D kameran Kinect v2 från Microsoft valdes som sensor för att samla in information från omgivningen. Den var ansluten till en Intel mini PC för realtidsbehandling. Båda komponenterna monterades på ett fyrhjuligt forskningskonceptfordon (RCV) för att testa genomförbarheten av den nuvarande lösningen i utomhusmiljöer. Robotoperativsystemet (ROS) användes som utvecklingsmiljö med C++ som programmeringsspråk. Den visuella distansmätningsstrategin bestod i en bildregistrerings-algoritm som kallas Adaptive Iterative Closest Keypoint (AICK) baserat på Iterative Closest Point (ICP) med hjälp av Oriented FAST och Rotated BRIEF (ORB) som nyckelpunktsutvinning från bilder. En rutnätsbaserad lokalkostnadskarta av rullande-fönster-typ implementerades för att få en tvådimensionell representation av de hinder som befinner sig nära fordonet inom ett fördefinierat område, i syfte att möjliggöra ytterligare applikationer för körvägen. Experiment utfördes både offline och i realtid för att testa systemet i inomhus- och utomhusscenarier. Resultaten bekräftade möjligheten att använda den utvecklade metoden för att spåra position och riktning av kameran samt upptäcka föremål i inomhusmiljöer. Men utomhus visades begränsningar i RGB-D-sensorn som gör att den aktuella systemkonfigurationen är värdelös för utomhusbruk