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31	Vizuální systém pro detekci obsazenosti parkoviště pomocí hlubokých neuronových sítí / Visual Car-Detection on the Parking Lots Using Deep Neural Networks Stránský, Václav January 2017 (has links) The concept of smart cities is inherently connected with efficient parking solutions based on the knowledge of individual parking space occupancy. The subject of this paper is the design and implementation of a robust system for analyzing parking space occupancy from a multi-camera system with the possibility of visual overlap between cameras. The system is designed and implemented in Robot Operating System (ROS) and its core consists of two separate classifiers. The more successful, however, a slower option is detection by a deep neural network. A quick interaction is provided by a less accurate classifier of movement with a background model. The system is capable of working in real time on a graphic card as well as on a processor. The success rate of the system on a testing data set from real operation exceeds 95 %.
32	Lidar-based SLAM : Investigation of environmental changes and use of road-edges for improved positioning Karlsson, Oskar January 2020 (has links) The ability to position yourself and map the surroundings is an important aspect for both civilian and military applications. Global navigation satellite systems are very popular and are widely used for positioning. This kind of system is however quite easy to disturb and therefore lacks robustness. The introduction of autonomous vehicles has accelerated the development of local positioning systems. This thesis work is done in collaboration with FOI in Linköping, using a positioning system with LIDAR and IMU sensors in a EKF-SLAM system using the GTSAM framework. The goal was to evaluate the system in different conditions and also investigate the possibility of using the road surface for positioning. Data available at FOI was used for evaluation. These data sets have a known sensor setup and matches the intended hardware. The data sets used have been gathered on three different occasions in a residential area, a country road and a forest road in sunny spring weather on two occasions and one occasion in winter conditions. To evaluate the performance several different measures were used, common ones such as looking at positioning error and RMSE, but also the number of found landmarks, the estimated distance between landmarks and the drift of the vehicle. All results pointed towards the forest road providing the best positioning, the country road the worst and the residential area in between. When comparing different weather conditions the data set from winter conditions performed the best. The difference between the two spring data sets was quite different which indicates that there may be other factors at play than just weather. A road edge detector was implemented to improve mapping and positioning. Vectors, denoted road vectors, with position and orientation were adapted to the edge points and the change between these road vectors were used in the system using GTSAM in areas with few landmarks. The clearest improvements to the drift in the vehicle direction was in the longer country area where the error was lowered with 6.4 % with increase in the error sideways and in orientation as side effects. The implemented method has a significant impact on the computational cost of the system as well as requiring precise adjustment of uncertainty to have a noticeable improvement and not worsen the overall results. lidar-slam environmental road-edges gtsam isam evaluation road-edge detector ros robot operating system weather rain snow rosnodes slam imu ekf extended kalmar filter evaluation measures Control Engineering Reglerteknik
33	Autonomous Skills for Remote Robotic Assembly Haberbusch, Matthew Gavin 01 June 2020 (has links) No description available. Robotics Robots Computer Science Electrical Engineering Engineering robotic assembly autonomous skills virtual attractor natural admittance control force control admittance control autonomy ROS robot operating system remote robotic assembly robot robotics robotic arm robot control robotic skills
34	A Deep Learning Approach To Coarse Robot Localization Bettaieb, Luc Alexandre 30 August 2017 (has links) No description available. Robotics Computer Science Electrical Engineering robotics localization deep learning neural networks machine learning state estimation robots robot robot operating system ROS AMCL monte carlo localization particle filter ConvNets convolutional neural networks
35	Service robot for the visually impaired: Providing navigational assistance using Deep Learning Shakeel, Amlaan 28 July 2017 (has links) No description available. Electrical Engineering Robotics Computer Science Assistive technology Deep learning Robotics Indoor navigation Computer vision Robot Operating System ROS Caffe Faster R-CNN Convolutional Neural Networks CNN Microsoft Kinect Service robots visually impaired mobility depth perception
36	MULTI-DRONE COLLABORATION FOR SEARCH AND RESCUE MISSIONS Forsslund, Patrik, Monié, Simon January 2021 (has links) Unmanned Aerial Vehicle (UAV), also called drones, are used for Search And Rescue (SAR) missions, mainly in the form of a pilot manoeuvring a single drone. However, the increase in labour to cover larger areas quickly would result in a very high cost and time spent per rescue operation. Therefore, there is a need for an easy to use, low-cost, and highly autonomous swarm of drones for SAR missions where the detection and rescue times are kept to a minimum. In this thesis, a Subsumption-based architecture is proposed, which combines multiple behaviours to create more complex behaviours. An investigation of (1) what are the critical aspects of controlling a swarm of drones, (2) how can a combination of different behavioural algorithms increase the performance of a swarm of drones, and (3) what benchmarks are necessary when evaluating the fitness of the behavioural algorithms. The proposed architecture was simulated in AirSim using the SimpleFlight flight controller through experiments that evaluated the individual layers and missions that simulated real-life scenarios. The results validate the modularity and reliability of the architecture, where the architecture has the potential for improvements in future iterations. For the search area of 400×400meters, the swarm consistently produced an average area coverage of at least 99.917% and found all the missing people in all missions, with the slowest average being 563 seconds. Compared to related work, the result produced similar or better times when scaled to the same proportions and higher area coverage. As comparisons of results in SAR missions can be difficult, the introduction of Active time can serve as a benchmark for others in future swarm performance measurements. robotics drone multi-drone robot drones collaborative collaboration ros robot operating system agent subsumption airsim simulation layer behaviour layers behaviours missing sar search and rescue search rescue task allocation task tasks automation quadrotor quadcopter uav unmanned aerial vehicle unmanned vehicle mission missions experiment experiments navigation suppressor inhibitor Robotics Robotteknik och automation Computer Sciences Datavetenskap (datalogi)

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