Change search
Refine search result
123 1 - 50 of 121
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Bereza-Jarocinski, Robert
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Distributed Model Predictive Control for Rendezvous Problem2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    This thesis investigates the potential advantages and disadvantages of using adistributed control approach to land an autonomous drone on an autonomousboat. The expected advantages include better utilisation of computational resources,as well as increased robustness towards communication delays. Inthis context, distributed control means that separate computers on the droneand boat are both involved in computing the control inputs to the system. Thisstands in contrast to an existing centralised algorithm where all computationsfor finding the control input are performed on the drone. Two new algorithmsare proposed, one using distributed model predictive control (DMPC) and oneusing a combination of DMPC with linear state-space feedback. The followingproperties of all the algorithms are tested: what the longest possible predictionhorizon with sufficiently short solution time is, how long it takes to solve optimisationproblems for the algorithms, and how quickly and safely each algorithmcan land the drone. Finally, the DMPC algorithm is shown to in certainscenarios possess improved robustness towards communication delays.

  • 2.
    Aarflot, Ludvig
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Implementation of High Current Measurement Technology for Automotive Applications in Programmable Logic2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    At Inmotion Technologies, a special method of measuring phase currents is usedin the high power inverters for automotive applications. This method requiresa considerable amount of control logic, currently implemented with discretelogic gates distributed over a number of integrated circuits. In this thesis, thefeasibility of replacing this with programmable logic hardware in one singlepackage is investigated.The theory behind the current measurement method as well as the operationof the discrete implementation is analysed and described. Requirements ona programmable logic device to implement this was identified and a suitabledevice chosen accordingly. A prototype was developed and tested, interfacingan existing product.Benefits in terms of cost and size are evaluated as well as required changesto the existing system and the possibility for improvements brought by such achange is analysed. Since the products in question have high requirements onfunctional safety, possible impacts in this regard are discussed.

  • 3.
    Ferro, Federica
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Leveraging a service oriented architecture for automatic retrieval and processing of fault recordings to obtain information for maintenance of circuit breakers2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Maintenance of power system components is fundamental to ensure high qualityoperations and avoid malfunctioning. Given the crucial role of the circuitbreakers (CBs) in ensuring quality of the power systems operations, this thesisworks on the implementation of an automatic retrieval and processing offault recordings with the aim compute quantities relevant for maintenance andpreventive maintenance of the CBs. For the scope, a service oriented architecture(SOA) is developed on top of the power system and connected with twoapplications able to automatically retrieve, decode and use fault recordingsto obtain indicators on the health of the CBs. Even if the lack of a commonmetadata for fault recordings does not permit generalizations on the topic, theproject shows that the resulting layered architecture composed of power system,SOA and applications, allows to automatically obtain indicators on thestate of the CBs and consequently to improve maintenance of the analyzedarea of the substation.

  • 4.
    Zhang, Zihan
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Modeling of VSC HVDC System for Meshed DC Grid2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    This thesis work deals with the modeling of a Voltage Source Converter based on High Voltage DirectCurrent (VSC-HVDC) for a meshed DC network topology. The general concept of the modeling is tointerface a tool independent control structure called ‘Common Component’ to user defined models fordifferent power system simulation tools. The control principles are developed in the language C++ andlinked into the simulation process as external functions via user model interface mechanisms. The thesisdemonstrates the modeling both in PSS/E and Power Factory.Different from the linear characteristics of a two terminal DC system, the meshed multi-terminal DC gridcontains non-linear dynamics that will play an importance role in the integration of DC grids in large ACsystems. In the first part of this report, the mathematical representation of the meshed DC grid model bothfor stability and dynamic behavior is derived. This enables the interfacing of a sequential AC/DC systempower flow algorithm, which is adapted to the multi terminal VSC HVDC system especially developedfor the meshed DC grid topology. The Gauss-Seidel algorithm is used to solve the DC power balanceequations. The differential algebraic equations (DAE) for a meshed DC terminal dynamic system arepresented and prepared for the programing interface code of the DC grid system model.The VSC-HVDC model for meshed DC network in PSS/E simulation is presented in details. In the PSS/Eload flow analysis, the VSC HVDC transmission is modeled as generic generators to represent converterswith active and reactive power levels and voltage set points. The meshed DC grid system is not explicitlyrepresented in the PSS/E built-in load flow model. The model interface is responsible for thecommunications with the control principles in ‘Common Component’ as well as the transmissionprocedure between the AC system and DC grid for steady-state and dynamic simulation.Furthermore, the network setups of VSC-HVDC transmission system as well as the meshed DC systemtopology are graphically represented through the components library in Power Factory. The convertercontrol systems are implemented as DSL models and communicate between the converters and themeasurements signals.A number of case studies, including step changes in active power reference, ac voltage reference, dcvoltage reference, and three-phase ground fault, have been used to study the system performance of theimplemented models in the simulation tools. Two meshed DC grid topologies are represented, one is threeconverters with three DC cables and the other one is five converters with seven DC cables. In theverification, the response of the proposed VSC-HVDC meshed DC network model in PSS/E is almostsame as the behavior shown in PowerFactory model. Both models performance reach satisfactory levelfor electro-mechanical simulation studies.

  • 5.
    Khays, Samir
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Motion Prediction of Surrounding Vehicles in Highway Scenarios With Deep Learning2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Anticipating the future positions of the surrounding vehicles is a crucial task foran autonomous vehicle in order to drive safely. To foresee complex manoeuvresfor longer time horizons, a framework that relies on high-level properties ofmotion and is able to incorporate, e.g. contextual features, is needed. In thisthesis, the problem of predicting the trajectories of the surrounding vehicles ona highway is tackled by using machine learning. The objective is to evaluate theperformance of recurrent neural networks for trajectory prediction, specificallylong-short term memory neural networks. Moreover, the goal is to investigateif contextual features can improve the predictions.The problem of predicting future trajectories is solved by using two differentapproaches, which are compared by using the same framework. The firstapproach is based on the vehicle states of the surrounding vehicles relative tothe ego-vehicle, where the reference system is in the ego-vehicle. The secondapproach is based on the velocities of the vehicles relative to the ground, wherethe reference system is in the ground. The results show that, with the proposedarchitecture, the latter approach results in a lower RMSE in the longitudinaldirection compared with the former approach. The results also show that theproposed models, overall, outperform a simple model, which is based on polynomialfitting, particularly in the lateral direction where the proposed modelsare significantly better than the polynomial models. Furthermore, contextualfeatures do not improve the predictions significantly. However, the results indicatethat contextual information has a positive impact on the predictions inspecific scenarios.

  • 6.
    Tuul, Viktor
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Online Collaborative Radio-enhanced Visual-inertial SLAM2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Simultaneous localization and mapping (SLAM) allows robots and other devices to localize and navigate in environments by using a map which itself generates. SLAM for single agent applications has matured and is showing promising results, thus the interest for collaborative SLAM has increased.This thesis proposes a framework for online collaborative radio-enhanced visual-inertial (VI) SLAM where multiple agents can collaborate by having their individually built maps merged and shared amongst each other. The framework is centralized with the aim to allow multiple agents to be managed by a single machine, also rendering it feasible to use the framework with agents that have limited computational resources, e.g. nano drones. Furthermore, radio technology is implemented in the framework which augments the SLAM solution by fusing ultra-wideband (UWB) anchor information into the built maps. This enables agents to query relevant parts of potentially large maps based on their contemporary radio activity.Four individual experiments are conducted to thoroughly evaluate the proposed solution. The results show that the collaborative SLAM system successfully allow agents to localize on parts of a map that other agents have built, running simultaneously. Moreover, the results also show that fusing UWB information into a visual-inertial map allow agents to perform partial-map queries, restricting the search area for visual matches between camera images and the map, reducing the risk of false re-localizations.

  • 7.
    Masso, Gabriel
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Optimising energy consumption on straight roads using regression analysis2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Cloud computation together with robotics has opened up possibilitiesto process large amount of data (big data) generated by the greatnumber of robotic systems. Todays vehicles are equipped withhundreds of sensors generating a lot of data that needs to beprocessed. The data can further be analysed and used to obtainmodels predicting the dynamics of the vehicles. It is thereforepossible to optimise the vehicle performance by studying thepredictive behaviour and finding the best combination of the vehicleparameters. In this thesis, the energy efficiency of an electric racingvehicle is studied on straight road whereafter an optimal velocityprofile is to be found. By using a multiple linear regression togetherwith regularization methods on previously recorded data, apredictive model managed to be obtained with an accuracy of 79.1 %.Having used this model in optimisation process, a velocity profilewas obtained which is shown that can enhance the efficiency of thesystem by 4.08%.

  • 8.
    Lindståhl, Simon
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Reinforcement Learning with Imitation for Cavity Filter Tuning: Solving problems by throwing DIRT at them2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Cavity filters are vital components of radio base stations and networks.After production, they need tuning, which has proven to be a difficultprocess to do manually and even more so to automate. Previously, attemptsto automate this process with Reinforcement Learning have beenmade but have failed to reach consistent performance on anything butthe simplest filter models. This Master thesis builds upon these resultsand aims to improve them. Multiple methods are tested and evaluated,including introducing a pre-processing step, tuning hyperparameters anddividing the problem into multiple sub-tasks. In particular, by using Imitationlearning as an initial phase, a semi-realistic filter model with 13tuning screws is tuned, fulfilling both insertion loss and return loss requirements.On this problem, this algorithm has a greater efficiency thanany previously published results on Reinforcement Learning for Cavityfilter tuning.

  • 9.
    Stefansson, Thor
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.
    3D obstacle avoidance for drones using a realistic sensor setup2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Obstacle avoidance is a well researched area, however most of the works only consider a 2D environment. Drones can move in three dimensions. It is therefore of interest to develop a system that ensures safe flight in these three dimensions. Obstacle avoidance is of highest importance for drones if they are intended to work autonomously and around humans, since drones are often fragile and have fast moving propellers that can hurt humans. This project is based on the obstacle restriction algorithm in 3D, and uses OctoMap to conveniently use the sensor data from multiple sensors simultaneously and to deal with their limited field of view. The results show that the system is able to avoid obstacles in 3D.

  • 10.
    Peng, Ming
    KTH, School of Electrical Engineering and Computer Science (EECS).
    A Game Theoretic Approach to Power Control in Vehicula rCommunication Systems2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Vehicular-to-network (V2N) communications are a key enabler in various intelligenttransportation system services currently investigated by global standards bodiesand European projects. In this master thesis, we study the impact of pilot and datapower setting on the uplink performance of V2N communications. Specifically, weconsider the “urban” and “rural” scenarios of the 5GCAR project, using the recommendedbase station and vehicular user equipment parameters in the 5.9 GHz band.We study a distributed non-cooperative game theoretic algorithm to determine thepilot-data power ratio for each vehicle. Numerical results indicate that the proposeddistributed algorithm converges to the Nash equilibrium, and that the mean squarederror (MSE) of the received data symbols decreases by 0.6 − 1.2 dB as comparedwith the MSE obtained by a centralized benchmark algorithm. The proposed algorithmachieves this improvement over a centralized benchmarking algorithm at theexpense of only a few iterations.

  • 11.
    Lin, Tengfan
    KTH, School of Electrical Engineering and Computer Science (EECS).
    A GUI Design of Robot Motion and Task Planning Based on Linear Temporal Logic2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Recent approaches solve the problem of robot motion and task planningby using formal methods-based model checking algorithms. Inthis work, we consider the software package P-MAS-TG, an automatictool to generate correct-by-design controllers for robot motion and taskplanning. The robot motion can be modelled as a finite-state transitionsystem and the task can be represented by a linear temporal logic formula.Then, using a model checking algorithm, an accepting path forthe robot motion can be found so that the robot satisfies the specificlinear temporal logic task. In this thesis, the process of searching forthe accepting path of the robot motion by P-MAS-TG is visualized bymeans of a graphical user interface (GUI) in form of an rqt plug-inwhere all requested inputs for the P-MAS-TG package can be definedin the GUI so that the process is simplified and user-friendly. Moreover,the GUI provides a simple hybrid control mechanism betweenthe initial linear temporal logic task and a temporary task that is insertedby a human.All functions of the GUI are implemented and demonstrated onthe TurtleBot platform. The experiment shows that the GUI and theP-MAS-TG are successfully integrated. Features of the P-MAS-TG areperformed with the GUI to conduct motion planning and task planningon the TurtleBot.

  • 12.
    Nilsson, Mao-Wei
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Autonomous Docking and Navigation of ships using Model Predictive Control2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Autonomous shipping is a coming field, where it will be important tooperate a ship without manual intervention. Although there are manyissues yet to be solved, not the least the legal ones, it would be interestingto investigate functions that already now would be possibleto use in today’s ship operation. One such field is autonomous navigationin narrow areas. The purpose of this study is to implement amotion control system to navigate marine vessel autonomously, and aGuidance, Navigation and Control system (GNC) is implemented fordocking and navigating vessels. Voronoi diagram is used for generatinga waypoint list for waypoint tracking. MPC with integral actionis applied to control the vessel for reducing model mismatches andconstant disturbance from current and wind. We performed the GNCsystem for South Harbour of Helsinki, and shown that the vessel isnavigated and docked at port. Moreover, we studied the effects of disturbanceto keep the controller stabilized and suggested an upper limitfor the disturbance.

  • 13.
    Choudhary, Abhishek
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Autonomous Exploration and Data Gathering with a Drone2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Unmanned Aerial Vehicles (UAV) are agile and are able to fly in and out of areas that are either dangerous for humans or have complex terrains making ground robots unsuitable. For their autonomous operation, the ability to explore unmapped areas is imperative. This has applications in data gathering tasks, search and rescue etc. 

    The objective of this thesis is to ascertain that it is, in fact, possible and feasible to use UAVs equipped with 2D laser scanners to perform autonomous exploration tasks in indoor environments. The system is evaluated by testing it in different simulated and real environments. The results presented show that the system is capable of completely and safely exploring unmapped and/or unexplored regions.

  • 14.
    Mattsson, Filip
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Centralized Model Predictive Control of a Vehicle Platoon2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    A centralized model predictive controller for longitudinal control of a vehicleplatoon is designed based on previous work on a distributed platooningcontroller. The vehicles in the platoon track a varying speed referenceand a constant timegap to the preceding vehicle. The designed controller isimplemented and compared to the distributed controller in simulation experimentsand in a simple practical setup. The experiments show that thecontroller works but does in general not outperform the distributed controller.

  • 15.
    Roussel, Nicolas
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Denoising of Dual Energy X-ray Absorptiometry Images and Vertebra Segmentation2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Dual Energy X-ray Absorptiometry (DXA) is amedical imaging modality used to quantify bone mineral density and to detect fractures. It is widely used due to its cheap cost and low radiation dose, however it produces noisy images that can be difficult to interpret for a human expert or a machine. In this study, we investigate denoising on DXA lateral spine images and automatic vertebra segmentation in the resulting images. For denoising, we design adaptive filters to avoid the frequent apparition of edge artifacts (cross contamination), and validate our results with an observer experiment. Segmentation is performed using deep convolutional neural networks trained on manually segmented DXA images. Using few training images, we focus on depth of the network and on the amount of training data. At the best depth, we report a 94 % mean Dice on test images, with no post-processing. We also investigate the application of a network trained on one of our databases to the other (different resolution). We show that in some cases, cross contamination can degrade the segmentation results and that the use of our adaptive filters helps solving this problem. Our results reveal that even with little data and a short training, neural networks produce accurate segmentations. This suggests they could be used for fracture classification. However, the results should be validated on bigger databases with more fracture cases and other pathologies.

  • 16.
    Lotz, Max
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.
    Depth Inclusion for Classification and Semantic Segmentation2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The  majority  of  computer  vision  algorithms  only  use  RGB  images  to  make  inferencesabout  the  state  of  the  world.  With  the  increasing  availability  of  RGB-D  cameras  it  is  im-portant  to  examine  ways  to  effectively  fuse  this  extra  modality  for  increased  effective-ness.  This  paper  examines  how  depth  can  be  fused  into  CNNs  to  increase  accuracy  in  thetasks  of  classification  and  semantic  segmentation,  as  well  as  examining  how  this  depthshould  best  be  effectively  encoded  prior  to  inclusion  in  the  network.  Concatenating  depthas  a  fourth  image  channel  and  modifying  the  dimension  of  the  initial  layer  of  a  pretrainedCNN  is  initially  examined.  Creating  a  separate  duplicate  network  to  train  depth  on,  andfusing  both  networks  in  later  stages  is  shown  to  be  an  effective  technique  for  both  tasks.The  results  show  that  depth  concatenation  is  an  ineffective  strategy  as  it  clamps  the  ac-curacy  to  the  lower  accuracy  of  the  two  modalities,  whilst  late  fusion  can  improve  thetask  accuracy  beyond  that  of  just  the  RGB  trained  network  for  both  tasks.  It  is  also  foundthat  methods  such  as  HHA  encoding  which  revolve  around  calculating  geometric  prop-erties  of  the  depth,  such  as  surface  normals,  are  a  superior  encoding  method  than  sim-pler  colour  space  transformations  such  as  HSV.  This  only  holds  true  when  these  depthimages  are  normalised  over  the  maximum  depth  of  the  dataset  as  opposed  to  the  maxi-mum  depth  of  each  individual  image,  thus  retaining  geometric  consistency  between  im-ages.  The  reverse  holds  true  for  simpler  colour  space  transformations.

  • 17.
    Pontusson, Magnus
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Design and Implementation of the SAX, a Robotic Measurement System for On-Chip Antennas at 140-325 GHz2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    There is currently a demand of mm‑wave on‑chip antennas to enable all kinds of new applications in several different areas. But the development requires, among other things, special equipment used during the measurement phase due to the small dimensions and the high frequencies.

    In this project a robotic measurement system, SAX (Single Arm eXtra), was designed and constructed at Micro and Nanosystems (MST) department at KTH Royal Institute of Technology (Sweden). The purpose of the SAX is to enable radiation pattern measurements of on‑chip antennas ( 140 GHz to 325 GHz ), whether the boresight is vertical or horizontal along with other requirements, by moving a converter with the measurement antenna around the antenna in question.

    Several alternative designs for the basic construction, both from other works and invented by the author, were analyzed based on the requirements for this project and other limitations. The chosen unique design, the SAX, is very compact and uses only one stepper motor. Several parts have been developed in this project to ensure the proper functionality of the SAX. That includes a main operator program, a motor input signal generating program, a motor input signal executing system, a security system, and a system for controlled rotation of the SAX. For the input signal to the motor two different algorithms to generate the time delays were developed and tested. They were adapted to make the motor manage the sweeps of an ever‑changing load with high inertia during acceleration and deceleration. One of them was developed to make the time delay array generation much more efficient albeit with larger approximation error.

    The SAX worked well and should be rather easy‑to‑use regarding the operation of the system, from the physical maneuvering to utilizing the sub‑systems to the running of the main operator program. It fulfilled the specific requirements by enable a cross pattern measurement from  -60° to +60°  both from above and from the side, adjustment of the radius between 15cm to 45cm , adjustment 10cm in height, to be rotated along the floor in steps of 1°, measurement steps of 1° with an accuracy of less than 0,5° (the largest error was measured to be ≤ 0,461°). However, some calibration work needs to be done before the optimal performance of the system is reached.

    As a verification of the operation of the system data from measurements of open‑ended waveguides was presented.

  • 18.
    Shilo, Albina
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Detection and tracking of unknown objects on the road based on sparse LiDAR data for heavy duty vehicles2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Environment perception within autonomous driving aims to provide a comprehensive and accurate model of the surrounding environment based on information from sensors. For the model to be comprehensive it must provide the kinematic state of surrounding objects. The existing approaches of object detection and tracking (estimation of kinematic state) are developed for dense 3D LiDAR data from a sensor mounted on a car. However, it is a challenge to design a robust detection and tracking algorithm for sparse 3D LiDAR data. Therefore, in this thesis we propose a framework for detection and tracking of unknown objects using sparse VLP-16 LiDAR data which is mounted on a heavy duty vehicle. Experiments reveal that the proposed framework performs well detecting trucks, buses, cars, pedestrians and even smaller objects of a size bigger than 61x41x40 cm. The detection distance range depends on the size of an object such that large objects (trucks and buses) are detected within 25 m while cars and pedestrians within 18 m and 15 m correspondingly. The overall multiple objecttracking accuracy of the framework is 79%.

  • 19.
    Drollinger, Nadine
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Developing a System for Robust Planning using Linear Temporal Logic2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Human robot-collaborative search missions have gotten more and more attention in recent years.Especially in scenarios where the robot first scouts the scene before sending in human agents. Thissaves time and avoids unnecessary risks for the human agents. One possible configuration of such arescue team is, a human operator instructing a unmanned aerial vehicle (UAV) via speech-commandshow to traverse through an environment to investigate areas of interest. A first step to address thisproblem is presented in this master thesis by developing a framework for mapping temporal logicinstructions to physical motion of a UAV.The fact that natural language has a strong resemblance to the logic formalism of Linear-TemporalLogic (LTL) is exploited. Constraints expressed as an LTL-formula are imposed on a provided labeledmap of the environment. An LTL-to-cost-map converter including a standard input-skeleton is developed.Respective cost maps are obtained and a satisfaction-measure of fulfilling these constraints ispresented. The input-skeleton and the map-converter are then combined with a cost-map-based pathplanning algorithm in order to obtain solution sets. A clarification request is created such that theoperator can choose which solution set should be executed. The proposed framework is successivelyvalidated, first by MATLAB-experiments to ensure the validity of the cost-map-creation followed bysimulation experiments in ROS incorporating the entire framework. Finally, a real-world experimentis performed at the SML to validate the proposed framework.

  • 20.
    Maloo, Shreyans
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Developing a voice-controlled home-assisting system for KTH Live-in Labs2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The following master thesis is conducted on behalf of KTH Royal Instituteof Technology and KTH Live-in Lab with the purpose of developing avoice-controlled home-assisting system for the KTH Live-in Lab. The labis being designed to serve as a testbed for products and services that canbe tested and veried within an optimal space which can simulate a reallifeusage. Being designed as a bridge between industry and academia, itaims to create a greater ease to which new products are tested and are researchedwhile involving KTH students in the process. Having innovationat its core the KTH Live-in Lab needs a mode of communication betweenthe user/occupant and the appliances in the space. That is why this thesisproposes to design a voice-controlled system that can control the appliancesand execute the commands provided by the user. The system will be createdaround a Speech to text service and improving its performance through variousmodications/integrations. The solution will be installed in the KTHLive-in Lab and integrated with the central controller once the sensor placementand controller installation is done.To make the system more robust and accurate, a new variable called,\Failure Factors" were dened for a voice-controlled system. The prototypeswere designed and improved with these factors as a basis. The main aimof the project is to make a system capable of handling a set of pre-denedsimple commands. For testing purpose, only 3 appliances were considered {light, heater and music. Also, the output is observed on LEDs rather thanon real appliances for the testing. These limitations were adapted to keepour focus on the prime motive of this project and that was to make the voicerecognitionas consistent and accurate as possible. Future work will consist ofmaking the system capable of handling complex user commands and havingan active feedback mechanism such that the user can have conversation withthe system.

  • 21.
    Elanjimattathil Vijayan, Aravind
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Dynamic Locomotion of Quadrupedal Robots over Rough Terrain2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Previous works have enabled locomotion of quadrupedal robots usingthe ZMP-based motion optimization framework on flat terrain withvarious gait patterns. Locomotion over rough terrain brings in newchallenges such as planning safe footholds for the robot, ensuring kinematicstability during locomotion and avoiding foot slippage over roughterrain etc. In this work, terrain perception is integrated into the ZMPbasedmotion optimization framework to enable robots to perform dynamiclocomotion over rough terrain.In a first step, we extend the foothold optimization framework touse processed terrain information to avoid planning unsafe footholdpositions while traversing over rugged terrain. Further, to avoid kinematicviolations during locomotion over rugged terrain, we presentadditional constraints to the ZMP-based motion optimization frameworkto solve for kinematically feasible motion plans in real-time. Weadd nonlinear kinematic constraints to existing nonlinear ZMP motionoptimization framework and solve a Sequential Quadratic Programming(SQP) problem to obtain feasible motion plans. Lastly, to avoidfoot contact slippage, we drop the approximated terrain normal anduse measured terrain normal at foot contact position to compute thefriction polygon constraints.The proposed algorithms are tested in simulation and on hardwarewith dynamic gaits to validate the effectiveness of this approach toenable quadrupedal robots to traverse rugged terrain safely. The computationaltime and performance of the proposed algorithms were analyzedunder various scenarios and presented as part of this thesis.

  • 22.
    Eriksson, Urban
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Dynamic Path Planning for Autonomous Unmanned Aerial Vehicles2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    This thesis project investigates a method for performing dynamic path planning in three dimensions, targeting the application of autonomous unmanned aerial vehicles (UAVs).  Three different path planning algorithms are evaluated, based on the framework of rapidly-exploring random trees (RRTs): the original RRT, RRT*, and a proposed variant called RRT-u, which differs from the two other algorithms by considering dynamic constraints and using piecewise constant accelerations for edges in the planning tree. The path planning is furthermore applied for unexplored environments. In order to select a path when there are unexplored parts between the vehicle and the goal, it is proposed to test paths to the goal location from every vertex in the planning graph to get a preliminary estimate of the total cost for each partial path in the planning tree. The path with the lowest cost given the available information can thus be selected, even though it partly goes through unknown space. For cases when no preliminary paths can be obtained due to obstacles, dynamic resizing of the sampling region is implemented increasing the region from which new nodes are sampled. This method using each of the three different algorithms variants, RRT, RRT*, and RRT-u, is tested for performance and the three variants are compared against each other using several test cases in a realistic simulation environment.  Keywords

  • 23.
    Thai Do, Hoang
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Energy Management of Parallel Hydraulic Hybrid Wheel Loader2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Hybridization of driveline system is one possible solution to increase fuel eciency.In this thesis a parallel hybrid hydraulic wheel loader concept was studied. A highpressure accumulator was added to the system and acted as a second source of energy.By adding the high pressure accumulator, regenerative braking energy canbe stored for later utilization. A backward facing simulation model was developedwhere the high pressure accumulator's State Of Charge (SOC) as state variableand hydraulic pump/motor's displacement as control input. Furthermore, dierentenergy management strategies: Dynamic Programming (DP), rule-based andEquivalent Consumption Minimization Strategy (ECMS) were developed. Thesestrategies were evaluated and compared to each other all with respect to the fuelconsumption. The result from conventional machine acted as the benchmark forother strategies to compare with. From simulation results, rule-based strategiesshowed to be the most robust, resulted in lower fuel consumption in every testeddriving cycle. For ECMS, the performance varied from cycle to cycle. A reductionin fuel consumption was observed for short-loading cycles. Especially in one cycle,ECMS result outclassed rule-based and was almost the same as DP. However, asmall increment was observed for long-carry cycle. Here the introduction of lock-upfeature in the torque converter yielded instead the most fuel saving. These valuableconclusions acted perfectly as a good starting point for future product development.

  • 24.
    Chintha, Cheerudeep
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Facilitating Automatic Setup in a Robotised Test Framework for Autonomous Vehicles by Path Planning and Real-Time Trajectory Generation2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The research in the field of autonomous vehicles and self-driving carsis growing at a rapid pace and strong initiatives are being taken to verifythe capability and functionality of such autonomous vehicles.Withcontinuous development being carried out in the field of AdvancedDriver Assist Systems (ADAS) and Autonomous Drive (AD) functions,ensuring safety, robustness and reliability of these functions is challengingand it requires advanced ways of verification and testing beforethese functions are deployed on the vehicle and delivered to thecustomer. Testing of these modern features can be done either on testtrack, real driving roads or in simulations by Computer Aided Engineering(CAE) . But testing a high-risk scenario in the real-worldwould be challenging due to safety concerns. Also, high regressionand continuous testing requires a test framework where the developmentand testing can be done in an efficient way.At Volvo Cars, it is envisioned that the best approach to test theAD vehicles is by subjecting the vehicle under test to several high riskscenarios by simulation based engineering and replicate the subset ofthese tests on a closed-loop test framework developed on the test track.This thesis is a part of FFI Funded Research Project called CHRONOS2where Volvo Car Corporation and other project partners aim to developthe closed-loop test framework for verification of AD Vehicles.This thesis work focuses on ensuring efficient and reproducible testingin the said test framework by accurate path planning and trajectorygeneration to drive the multiple test objects to their starting positionsin an unstructured test environment. The algorithm developedfor path planning should also ensure the generation of a safe path inreal-time for the test objects in case of failure or error in the test framework.The path-planning algorithm has been successfully implementedtaking the unstructured environment and vehicle dimensions into considerationresulting in a safe path avoiding obstacles and satisfyingnonholonomic constraints of the vehicle. The implemented architectureutilizes the parallel-process framework of Robot Operation System(ROS) and results in a algorithm which can run in real-time.

  • 25.
    Borsub, Jatesada
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Hardened Registration Process for Participatory Sensing2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Participatory sensing systems need to gather information from a largenumber of participants. However, the openness of the system is a doubleedgedsword: by allowing practically any user to join, the system can beabused by an attacker who introduces a large number of virtual devices.This work proposes a hardened registration process for participatory sensingto raise the bar: registrations are screened through a number of defensivemeasures, towards rejecting spurious registrations that do not correspondto actual devices. This deprives an adversary from a relatively easytake-over and, at the same time, allows a flexible and open registrationprocess. The defensive measures are incorporated in the participatorysensing application.

  • 26.
    Elfving, Maria
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Hydraulic closed loop control2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The purpose of the thesis work is to investigate methods for closedloop control of hydraulic pressure in transmissions to make them bemore precise. This is desirable since it decreases the fuel consumptionas well as emissions, and improves the driving performance.To be able to study the behaviour of the transmission, a Simulink modelis designed with the parts relevant to the problem, and from this a linearmodel is obtained. Three different controllers are designed andimplemented in the Simulink model, to compare and analyze differentsolutions. The controllers implemented are a PI controller, a PIDcontroller and a LQR controller.The results from the simulation with the different controllers showstep responses to be able to evaluate their individual performance. Theresults show that all of the controllers meet the requirements for a stepreponse under better conditions, but under worse ones the LQR controllerperforms best of the three. The LQR controller is therefore themost suitable of the three controllers for this particular problem.

  • 27.
    Liu, Feiyang
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Implementation and verification of the Information Bottleneck interpretation of deep neural networks2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Although deep neural networks (DNNs) have made remarkable achievementsin various elds, there is still not a matching practical theory that is able toexplain DNNs' performances. Tishby (2015) proposed a new insight to analyzeDNN via the Information bottleneck (IB) method. By visualizing how muchrelevant information each layer contains in input and output, he claimed thatthe DNNs training is composed of tting phase and compression phase. Thetting phase is when DNNs learn information both in input and output, andthe prediction accuracy goes high during this process. Afterwards, it is thecompression phase when information in output is preserved while unrelatedinformation in input is thrown away in hidden layers. This is a tradeo betweenthe network complexity (complicated DNNs lose less information in input) andprediction accuracy, which is the same goal with the IB method.In this thesis, we verify this IB interpretation rst by reimplementing Tishby'swork, where the hidden layer distribution is approximated by the histogram(binning). Additionally, we introduce various mutual information estimationmethods like kernel density estimators. Based upon simulation results, we concludethat there exists an optimal bound on the mutual information betweenhidden layers with input and output. But the compression mainly occurs whenthe activation function is \double saturated", like hyperbolic tangent function.Furthermore, we extend the work to the simulated wireless model where thedata set is generated by a wireless system simulator. The results reveal that theIB interpretation is true, but the binning is not a correct tool to approximatehidden layer distributions. The ndings of this thesis reect the informationvariations in each layer during the training, which might contribute to selectingtransmission parameter congurations in each frame in wireless communicationsystems.

  • 28.
    Lewenhaupt, Johan
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Learning Operational Goals for Propulsion System Using Reinforcement Learning2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    This degree project, conducted at ABB, aims to analyze and solve differentsituations that a crew on board a vessel might face by controllingits propulsion system. The propulsion system is viewed as static,transition-deterministic, as well as stochastic when measuring data.This system is then used to formulate a decision problem using a finiteMarkov Decision Process, which is attempted to be tackled usingQ-learning, Speedy Q-learning and Double Q-learning for three differentobjectives that are relevant to the system’s behaviour and performance.The objective policies found from experiments are clearlyworking as intended and from the looks of experiments it seems thatmore training very much does affect the performance, which should bethe case knowing that there is a proof of convergence for Q-learningbased algorithms. The convergence rates for the three different algorithmsare then compared to a solution that is seen as optimal, to seehow fast they converge and try to determine the time needed to solveproblems similar to the ones stated in this thesis.

  • 29.
    Verrax, Paul
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Model Predictive Control Applied to Ground Source Heat Pumps2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Building heating is one of the most important sources of energy consumption. GroundSource Heat Pumps (GSHP) are efficient heating systems, particularly popular in the Nordiccountries. However, the GSHPs available for the consumer market today typically only utilizebasic control schemes that are relatively inflexible. More advanced strategies such as ModelPredictive Control (MPC) appear as a promising approach to improve comfort while reducingconsumption. The present thesis considers a typical user case of a single family house heatedby a ground source heat pump willing to reduce its environmental impact. We design a MPCcontroller to be used on top of the existing heat pump system and with almost no additionalhardware needed. Specific attention is dedicated to the system’s efficiency in order to reflectthe real working performances of a ground source heat pump. The controller is evaluated insimulation on different scenarios using an identified model of a single family house. The resultsshow the MPC strategy becomes most beneficial when including time varying prices or reducedcomfort during certain hours of the day. When both are conjugated the economic savings areup to 8% despite the loss of efficiency of the heat pump. The controller was implemented andtested on a real system with promising results.

  • 30.
    Voisin-Denoual, Maxime
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Monocular Visual Odometry for Underwater Navigation: An examination of the performance of two methods2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    This thesis examines two methods for monocular visual odometry, FAST + KLT and ORBSLAM2, in the case of underwater environments.This is done by implementing and testing the methods on different underwater datasets. The results for the FAST + KLT provide no evidence that this method is effective in underwater settings. However, results for the ORBSLAM2 indicate that good performance is possible whenproperly tuned and provided with good camera calibration. Still, thereremain challenges related to, for example, sand bottom environments and scale estimation in monocular setups. The conclusion is therefore that the ORBSLAM2 is the most promising method of the two tested for underwater monocular visual odometry.

  • 31.
    Regard, Mikael
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Process Chain Optimization in a Smart Factory2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The purpose with this thesis is to reduce downtime of machines androbots in a serial production line by improving communication betweenoperators and machines. Modeling a serial production line asa queuing system makes Markov chain optimization methods possible.The concept is to make machines and robots adapt its service ratesbased on status of its surrounding machines and the position of operators.In a pharmaceutical industry, a production rate of 50-60% of itsmaximum capacity is considered as normal. Unnecessary downtimecaused by machine breakdowns is one factor which reduces the productionrate. Two optimization methods were investigated, the workallocation problem and the targeting problem. It was found that thework allocation problem does not provide an optimal solution whenmodeling with a saturated model. The targeting problem provides anoptimal solution, which is a trade-off between the average amount ofproducts in a system and the cost for keeping this level.

  • 32.
    Goran, Alan
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Reinforcement Learning for Uplink Power Control2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Uplink power control is a resource management functionthat controls the signal’s transmit power from a userdevice, i.e. mobile phone, to a base-station tower. It isused to maximize the data-rates while reducing the generatedinterference.Reinforcement learning is a powerful learning techniquethat has the capability not only to teach an artificial agenthow to act, but also to create the possibility for the agentto learn through its own experiences by interacting with anenvironment.In this thesis we have applied reinforcement learningon uplink power control, enabling an intelligent softwareagent to dynamically adjust the user devices’ transmit powers.The agent learns to find suitable transmit power levelsfor the user devices by choosing a value for the closed-loopcorrection signal in uplink. The purpose is to investigatewhether or not reinforcement learning can improve the uplinkpower control in the new 5G communication system.The problem was formulated as a multi-armed banditat first, and then extended to a contextual bandit. We implementedthree different reinforcement learning algorithmsfor the agent to solve the problem. The performance ofthe agent using each of the three algorithms was evaluatedby comparing the performance of the uplink power controlwith and without the agent. With this approach we coulddiscover whether the agent is improving the performanceor not. From simulations, it was found out that the agentis in fact able to find a value for the correction signal thatimproves the data-rate, or throughput measured in Mbps,of the user devices average connections. However, it wasalso found that the agent does not have a significant contributionregarding the interference.

  • 33.
    Nowak, Jakub
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Robust Control of Coupled Multirobot Systems under Signal Temporal Logic Specifications with Collision Avoidance2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Multi-agent systems have been gaining attention among robotics researchersdue to their capabilities, in terms of which they surpass individualrobots. Such systems, however, entail an increase in complexity,especially to the task planning problem. Therefore, a more sophisticatedplanning approach is needed, one that is able to resolve conflictsbetween local tasks, in particular. These conflicts arise from couplingsbetween robots expressed in their task specifications.A computationally inexpensive collaborative control scheme is implementedin a setup consisting of three omni-directional robots. Therobots execute tasks encoded in Signal Temporal Logic, using PrescribedPerformance Control. They are guaranteed to satisfy the specificationsor, if that is not possible, reach a least violating solution, evenin the face of a task conflict. What is more, collision-free trajectoriesare produced.A series of simulations and experiments is carried out to verify theutility of the method. The approach is shown to be effective despiterestrictive assumptions imposed on the system. Finally, directions forfuture work, that would allow the method to perform well in moredemanding experimental scenarios, are suggested.

  • 34.
    Andre do Nascimento, Allan
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Robust Model Predictive Control for Marine Vessels2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    This master thesis studies the implementation of a Robust MPC controllerin marine vessels on different tasks. A tube based MPC is designed based onsystem linearization around the target point guaranteeing local input to statestability of the respective linearized version of the original nonlinear system.The method is then applied to three different tasks: Dynamic positioningon which recursive feasibility of the nominal MPC is also guaranteed, Speed-Heading control and trajectory tracking with the Line of sight algorithm.Numerical simulation is then provided to show technique’s effectiveness.

  • 35.
    Ohnishi, Motoya
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Safey-aware Adaptive Reinforcement Learning with Applications to Brushbot Navigation2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    This thesis presents a safety-aware learning framework that employs an adaptivemodel learning method together with barrier certificates for systems withpossibly nonstationary agent dynamics. To extract the dynamic structure ofthe model, we use a sparse optimization technique, and the resulting modelwill be used in combination with control barrier certificates which constrainfeedback controllers only when safety is about to be violated. Under somemild assumptions, solutions to the constrained feedback-controller optimizationare guaranteed to be globally optimal, and the monotonic improvementof a feedback controller is thus ensured. In addition, we reformulate the(action-)value function approximation to make any kernel-based nonlinearfunction estimation method applicable. We then employ a state-of-the-artkernel adaptive filtering technique for the (action-)value function approximation.The resulting framework is verified experimentally on a brushbot,whose dynamics is unknown and highly complex.

  • 36.
    Sigonius, Marc
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Speed and yaw rate estimation in autonomous vehicles using Doppler radar measurements2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    One of the key elements for model-based autonomous drivingis the ability of the vehicle to accurately predict its ownmotion. Knowledge about the motion can then be widelyused, e.g. for localization, planning and control.This thesis presents an algorithm that estimates thevelocity and the yaw rate based on Doppler radar measurements.This system uses an Unscented Kalman filterto extract the motion of the vehicle from multiple Dopplerradar sensors mounted on the vehicle. The estimation ofthese quantities is shown to be critically dependent on outlierdetection and the vehicle’s center of rotation. Thiswork presents a framework for detecting dynamical objects,as well as estimating the center of rotation of the vehicleeffectively.In tests, the proposed implementation shows better rootmeansquared error performance than the current employedalgorithm by 28.8% and 22.4% for velocity and yaw rate,respectively.

  • 37.
    Guin, Agneev
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.
    Terrain Classification to find Drivable Surfaces using Deep Neural Networks: Semantic segmentation for unstructured roads combined with the use of Gabor filters to determine drivable regions trained on a small dataset2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Autonomous vehicles face various challenges under difficult terrain conditions such as marginally rural or back-country roads, due to the lack of lane information, road signs or traffic signals. In this thesis, we investigate a novel approach of using Deep Neural Networks (DNNs) to classify off-road surfaces into the types of terrains with the aim of supporting autonomous navigation in unstructured environments. For example, off-road surfaces can be classified as asphalt, gravel, grass, mud, snow, etc.

    Images from the camera mounted on a mining truck were used to perform semantic segmentation and to classify road surface types. Camera images were segmented manually for training into sets of 16 and 9 classes, for all relevant classes and the drivable classes respectively. A small but diverse dataset of 100 images was augmented and compiled along with nearby frames from the video clips to expand this dataset. Neural networks were used to test the performance for the classification under these off-road conditions. Pre-trained AlexNet was compared to the networks without pre-training. Gabor filters, known to distinguish textured surfaces, was further used to improve the results of the neural network.

    The experiments show that pre-trained networks perform well with small datasets and many classes. A combination of Gabor filters with pre-trained networks can establish a dependable navigation path under difficult terrain conditions. While the results seem positive for images similar to the training image scenes, the networks fail to perform well in other situations. Though the tests imply that larger datasets are required for dependable results, this is a step closer to making the autonomous vehicles drivable under off-road conditions.

  • 38.
    Papasideris, Spyridon
    KTH, School of Electrical Engineering (EES), Automatic Control.
    A practical approach to cooperative aerial transportation of a load by UAVs2017Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    A path following controller for a system, composed of a single cylindrical load and connected totwo unmanned aerial vehicles by two cables, is designed. The load is modeled as a uniformlydistributed mass while the aerial vehicles are assumed to be fully actuated point masses. Theinputs to the UAVs-load system are the 3D force inputs of each UAV. During this report theassumption that there are not input disturbances has been made. An additional assumption isthat the load and the aerial vehicles are connected by cables of xed length, which is equivalent toconnection by massless rigid links while under tensile forces. Next, the state space is transformedto another description that is locally full state controllable. The system is linearized around aNominal Equilibrium in which the bar is horizontal and the cables vertical. The linearized systemundergoes a similarity transformation which decouples it into three 2nd and three 4nd ordersubsystems. A stabilizing feedback controller is then designed for each subsystem independently.This stabilizing controller can be applied to every nominal equilibrium. Then the controller istested over model uncertainties and integral actions are also added to it. A practical controlpolicy for trajectory tracking is designed based around a zero equilibrium. In detail, a trajectorythat comprises of a series of Nominal Equilibria and respects the stability characteristics of thecontroller can then be tracked. All the proposed control algorithms are validated by simulation.

  • 39.
    Trichon, Vincent
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.
    A Singing Drone Choir2017Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Drones have a new emerging use case: performing in shows and live events. This master thesis has been driven by an artistic project invited to take part in a full-scale operatic performance in the Croatian National Theatre Ivan Zajc in Rijeka, Croatia, in 2019. This project merges technological research with ancient theatrical and operatic traditions by using drones as an opera choir. After describing the process of designing and building a fleet of quadrotors equipped with speakers, we present a reacting and interacting motion planning strategy based on potential fields. We analyse and evaluate our drone design with its control strategy on simulation and on a real drone.

  • 40.
    Lilja, Joakim
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Combined Attitude and Orbital MPC for Thruster Based Spacecrafts2017Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    A spacecraft needs to simultaneously provide orbital and attitude controlbut these are in general treated as separate systems. Normally the attitudecontrol is conducted via reaction wheels but can in scenarios with high manoeuvrabilitydemands be handed over to pure thruster control. In specificcases the reaction wheels are removed from the spacecraft to save mass. Ifboth the orbital and attitude control is regulated with thrusters, there is apotential to save fuel in a combined control strategy. Model predictive controlhas been shown to be a viable method for orbital control with a fuel minimisingobjective. This thesis investigates a combined orbital and attitude model predictivecontrol strategy. Three test cases are simulated with a specific thrusterconfiguration; maintaining a passive orbit relative to a target, large-angle reorientationand repositioning, and rendezvous. Preliminary results show thatincluding the coupled dynamics lowers the overall fuel consumption while satisfyingrequirements on position and attitude in scenarios where the timescaleof the orbital and attitude control is similar.

  • 41.
    Holesovsky, Ondrej
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    Compact ConvNets with Ternary Weights and Binary Activations2017Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Compact architectures, ternary weights and binary activations are two methods suitable for making neural networks more efficient. We introduce a) a dithering binary activation which improves accuracy of ternary weight networks with binary activations by randomizing quantization error, and b) a method of implementing ternary weight networks with binary activations using binary operations. Despite these new approaches, training a compact SqueezeNet architecture with ternary weights and full precision activations on ImageNet degrades classification accuracy significantly more than when training a less compact architecture the same way. Therefore ternary weights in their current form cannot be called the best method for reducing network size. However, the effect of weight decay on ternary weight network training should be investigated more in order to have more certainty in this finding.

  • 42.
    Khan, Imran
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Decentralized Navigation of Multiple Quad-rotors using Model Predictive Control2017Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    In this thesis, we develop a model predictive control (MPC) scheme for the navigationof multiple quadrotors in an environment with obstacles. The overall controlscheme is decentralized, since each quadrotor calculates its own signal based on localinformation. The MPC constraints take care of collision with the static obstacles,inter-agent collisions as well as input saturations. Firstly, we formulate and solvethe problem using a nonlinear MPC framework, where the agents and the obstaclesare modelled as 3D spheres. Secondly, to deal with complexity issues, we linearizethe model and constraints by employing polyhedral sets, and we solve the problemwith linear MPC. Thirdly, we use a mixed logical dynamical (MLD) framework tosolve our problem, which is then incorporated into a hybrid MPC problem. The performanceof the proposed solutions is demonstrated through computer simulationsand real-time experiments.

  • 43.
    Tilaveridis, Ioannis
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Detection of friction variations in bolted joints during tightening2017Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Tightening describes the process of rotating a screw with the purpose of binding two surfaces together. It is widely applied in the assembly process of structures, where bolted joints are used to connect the different parts with each other, e.g. robot arms, vehicles, aircrafts. A certain torque is applied with a tool in order to develop the desired clamping force that keeps the surfaces together. A challenge during this process is the fact that friction variations occur unexpectedly, thus increasing the risk of not achieving the necessary clamping force to ensure successful tightening. In this thesis, a diagnosis method is implemented in order to detect friction variations during highly dynamic tightening. Different detection algorithms are investigated (e.g. CUSUM, Particle Filter, Linear regression), and an approach that makes use of the torque and angle signals while estimating the clamping force is implemented. Investigations of signal noise and filtering operations during data extraction are conducted, and the signal channels are evaluated with regards to accuracy and noise bias. An approach using a sliding window is used to estimate the torque rate, and the CUSUM detection algorithm is implemented to indicate variations and provide a diagnostic report. The analysis is performed using a highly dynamic tightening strategy programmed in an electrical tightening tool, allowing for the process to be conducted in milliseconds. Investigations of the tuning parameters of the detection algorithm are also conducted, and value thresholds are identified. Finally, a statistical analysis of the system’s behaviour, as well as the influence of the operator holding the tool, is performed for evaluation.

  • 44.
    Stefan, Silviu Nicolae
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Develop healthy building infrastructure for KTH LIVE-IN-LAB2017Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The following master thesis is conducted on behalf of The Royal Institute ofTechnology and KTH Live-in Lab with the purpose of proposing a healthybuilding infrastructure for the KTH Live-in Lab. The Lab will serve as atestbed for products and services that can be tested and veried within anoptimal space that can simulate a real life usage of the tested products or services.Since the KTH Live-in Lab proposes to create a smart environment inorder to fulll its goal, this thesis proposes to design a system that measuresthe key factors that inuence the user's health while living in the building.The problem that this thesis is solving is that of understanding the relevantmetrics that aect the person living in the building, then identify andplace the sensors that can measure the health metrics and nally encapsulatethe result in a WSN, paying close attention to the topology and the communicationprotocols used, capable of monitoring and collecting all the relevantdata for further use.The most dicult part of the thesis is translating the health parametersinto the optimal quantiable metrics so that a developed system couldbecome a feasible solution for a home automation. The attempted way ofsolving this problem is through literature review of health studies in order tounderstand which are the quintessential parameters that should be measured.The system considers dierent health factors from 9 dierent domainsVentilation, Air Quality, Thermal Comfort, Moisture, Dust and Pests, Safetyand Security, Water Quality, Noise and Lighting and Views. Each of thesedomains will be analyzed and the best metrics for monitoring will be selected.The solution will be tailored on the KTH Live-in Lab as the sensor placementis done on the schematic of the Housing Design, of the Fall Semester 2017.In order to choose the optimal way to implement the wireless sensor network,several topologies and communication protocols are compared, the chosen onebeing ZigBee as protocol while the topology was separated in how sensorsare organized in every room which will be a mesh topology and how they areorganized in the whole building for which the chosen topology is the Two-tierhierarchical cluster topology. The system also proposes a security encryptionalgorithm for data protection and a way to evaluate the system based on thestandard of the WELL Building Institute.Future work will consist in implementing all the features that are designedin this paper while nding the perfect trade-o between the cost andtechnology accuracy when this project will be scaled for a whole apartmentbuilding.As a conclusion, there are certain variations that one can follow whenimplementing the designed system as the implementation will be a trade-obetween the quality of the equipment used which translates into the accuracyof the measurements and the nancial and social constraints. This thesisproposes a set of core elements that cannot be replaced in monitoring andalso provides approximations for other less common metrics.

  • 45.
    Carlsson, Hannes
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Development of a Depth Controller for the SEAL Carrier2017Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]