This project is the implementation and testing of a path tracker for a car-sized Unmanned Ground Vehicle. The vehicle, a Toyota Land Cruiser, was provided by SSC and hadpreviously been modified for remote operation. The developed path tracker uses a "follow the carrot" algorithm and has been writtenin C using the Robot Operating Software (ROS) framework and has been integrated intothe vehicles existing ROS powered software. During the implementation, the Gazebo rigid body simulator was used to simulate a simplified vehicle. Integration with the real sensors was performed using a small-scalecar, both indoors with the aid of a Vicon motion capture system and outdoors utilizingonly sensors available to the full-size car. The small-scale tests showed promise, howeverwhen full-scale field tests were performed the results showed some problems and reasonsfor these are discussed.

Energy efficiency is becoming an important objective for modern manufacturing industry. The aim of this work is to improve energy efficiency of an automated system. Since a majority of production processes are limited by an external bottleneck, the hypothesis of this work is that reducing the processing rate of the restricted processes can lead to saving in energy and resources. A methodology based on optimisation at process, cell and line levels is developed and evaluated over different scenarios.The developed methodology is then applied to a simulated production cell to study its efficacy quantitatively. In this particular case, the proposed approach yields a decrease in energy consumption of 49% at maximum production capacity. This decrease can be greater if there is an external factor such as low demand or another stage in the production line.

The vision of self driving cars has existed for a long time and the field of autonomous vehicles has been of great interest to researchers and companies. Volvo construction equipment presented their Electrical Site project in September 2016, with predictions of reducing carbon emission up to 95% and total cost of ownership by 25%. In the project, multiple autonomous haulers are intended to work in a fleet, loading, unloading and charging in a cyclic behavior. This masterthesis focus on the lateral control system of the automated hauler platform HX. The platform is modeled in an comprehensive simulation environment and three different control algorithms have been implemented and tested; An adaptive Proportional, Integral and Derivative (PID) controller, Stanley and the Proportional Integral + Proportional controller. The PID controller is tuned using the Nyquist stability criterion and the other two algorithms are tuned using a Genetic Algorithm. Results indicate that, to reach the optimal performance of the tested algorithms, manual tuning from experimental testing is required.

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Detta projekt handlar om att effektivisera EMC-testning genom att undersöka hur en modväxlare konstrueras samt hur denna styrs och ifall det behövs återkoppling för denna ska uppnå funktionell nivå. Syftet med modväxlare är att man enklare ska kunna testa emissions- och immunitetsnivåer av nya tekniska uppfinningar och på så sätt värna om framtidens elektromagnetiska milj¨o. Under arbetets g˚ang har en modv¨axlare designats med CAD varpå modellen har skalats ner för att testa positionsnogrannhet, acceleration och tidsåtgång för körning. Avslutningsvis har man dragit slutsatser baserat på testerna och med hjälp av dessa framfört anvisningar till hur en fullskalig modväxlare bör konstrueras.

This thesis investigates solutions to automate a lab process and give the customer basis for investment. An analysis of the current lab process and interviews with the staff were performed to set requirements for an automated solution. The customer wants to perform an analysis on a large number of combinations between antibiotics and bacteria. The issues that an automated solution is not able to handle were identified for the customer. A market analysis of existing solutions was preformed and contact was established with different suppliers. The suggested products were evaluated by performance and if they would be able to adapt to the process. The evaluation shows that with the same number of working hours the customer can increase the productivity with 6.7 times using a semi-automated system and a fully-automated system will result in an increase of 18.3 times. A guidance of how the customer will implement the system is described in the report. The report shows that an automated solution will make their process more efficient.

Using UAVs in everyday life has been increasing in recent years. UAV is an agile vehicle and often comes integrated with a camera and sensors which makes it suitable for object detection and tracking. In this thesis, we present a subsystem with a limited hardware setup only consisting of an on-board computer and a camera that is mounted on a UAV. The subsystem provides techniques to maneuver, detect and descend to an object, all executed autonomously. The system is implemented in Robotic Operating System (ROS). The object detection is implemented as a convolutional neural network provided by TensorFlow Object Detection API. This thesis covers the necessary steps to adopt a pre-trained TensorFlow model to specific needs and compares three different TensorFlow models considering accuracy, frames per second and energy efficiency. Additionally, methodologies to cover a predefined area and position an object in relation to the camera is proposed. Experiments are executed both in a real-world and simulated environment and the results are promising for the implemented system. / Användandet av UAVs i det vardagliga livet har ökat markant de senaste åren. En UAV är ett agilt fordon som ofta kommer integrerat med en kamera samt sensorer som gör det till ett lämpligt fordon för objektigenkänning och spårning. I den här avhandligen presenterar vi ett delsystem med en hårdvaruplattform endast bestående av en inbyggd dator och en kamera. Delsystemet tillhandahåller metoder som gör det möjligt för UAV:en att styras, känna igen objekt och landa på det detekterade objektet autonomt. Systemet implementeras i Robotic Operating System (ROS). Objektigenkänningen är implementerat som ett konvolutionellt neuralt nätverk tillhandahållt av TensorFlow Object Detection API. Avhandlingen omfattar stegen nödvändiga att ta för att anpassa en TensorFlow model till sina egna behov och gör jämförelser mellan tre olika Tensorflow modeller med avseende på precision, bildrutor per sekund och energi effektivitet. Dessutom presenteras metoder för att söka av ett fördefinierat område och positionering av ett objekt relativt komeran. Under experiment, både i simulering och verkliga världen, har lovande resultat framkommit.

General autonomy is at the forefront of robotic research and practice. Earlier research has enabled robots to learn movement and manipulation within the context of a specific instance of a task and to learn from large quantities of empirical data and known dynamics. Reinforcement learning (RL) tackles generalisation, whereby a robot may be relied upon to perform its task with acceptable speed and fidelity in multiple---even arbitrary---task configurations. Recent research has advanced approximate policy search methods of RL, in which a function approximator is used to represent an optimal policy while avoiding calculation across the large dimensions of the state and action spaces of real robots. This thesis details the implementation and testing, on a lightweight industrial robot arm, of guided policy search (GPS), an RL algorithm that seeks to avoid the typical need, in machine learning, for lots of empirical behavioural samples, while maximising learning speed. GPS comprises a local optimal policy generator, here based on a linear-quadratic regulator, and an approximate general policy representation, here a feedforward neural network. A controller is written to interface an existing back-end implementation of GPS and the robot itself. Experimental results show that the GPS agent is able to perform basic reaching tasks across its configuration space with approximately 15 minutes of training, but that the local policies generated fail to be fully optimised within that timescale and that post-training operation suffers from oscillatory actions under perturbed initial joint positions. Further work is discussed and recommended for better training of GPS agents and making locally optimal policies more robust to disturbance while in operation.

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No description available.