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The Global ETD Search service is a free service for researchers
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Showing 31 to 40 of 40 (0.095 seconds)Spelling suggestions: "subject:"robot operating atemsystem"" "subject:"robot operating systsystem""
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Stereo vision-based system for detection, track and capture of intruder flying dronesMaria Nieves Brunet Avalos (8800964) 06 May 2020 (has links)
<div>In this thesis, the design and implementation of an autonomous system that will equip a multi-rotor unmanned aerial vehicle (UAV) for visual detection and tracking of other UAVs is presented. The results from detection and tracking are used for real-time motion planning.</div><div><br></div><div>The goal is to effectively detect unwanted UAVs, track them and finally capture them with a net. Having a net that traps the UAVs and enables dragging intruders to another location is of great importance, since these could be carrying dangerous loads.</div><div><br></div><div>The project consists of three main tasks: object detection using a stereo camera, video tracking using a Kalman filter based algorithm, and lastly executing an optimal flight plan to aim a net at the detected intruder UAV. The computer vision, motion tracking and planning algorithms are implemented as ROS nodes what makes them executable on a reduced size onboard computer that is installed on the aerial vehicle.</div><div><br></div><div>Previous work related to this project consists of either a UAV detection system with computationally heavy algorithms or a tracking algorithm that does not include information about the dynamics of the UAVs. For the capture methods, previous ideas do not consider autonomous decisions or an optimized method to guarantee capture. In this thesis, these three aspects are considered to develop a simple solution that can be mounted on any commercially available UAV.</div>
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Integration of a visual perception pipeline for object manipulation / Integration av en visuell perceptionssystem för objektmanipuleringShi, Xiyu January 2020 (has links)
The integration of robotic modules is common both in industry and in academia, especially when it comes to robotic grasping and object tracking. However, there are usually two challenges in the integration process. Firstly, the respective fields are extensive, making it challenging to select a method in each field for integration according to specific needs. Secondly, because the integrated system is rarely discussed in academia, there is no set of metrics to evaluate it. Aiming at the first challenge, this thesis reviews and categorizes popular methods in the fields of robotic grasping and object tracking, summarizing their advantages and disadvantages. This categorization provides the basis for selecting methods according to the specific needs of application scenarios. For evaluation, two well-established methods for grasp pose detection and object tracking are integrated for a common application scenario. Furthermore, the technical, as well as the task-related challenges of the integration process are discussed. Finally, in response to the second challenge, a set of metrics is proposed to evaluate the integrated system. / Integration av robotmoduler är vanligt förekommande både inom industrin och i den akademiska världen, särskilt när det gäller robotgrepp och objektspårning. Det finns dock vanligtvis två utmaningar i integrationsprocessen. För det första är både respektive fält omfattande, vilket gör det svårt att välja en metod inom varje fält för integration enligt relevanta behov. För det andra, eftersom det integrerade systemet sällan diskuteras i den akademiska världen, finns det inga etablerade mätvärden för att utvärdera det. För att fokusera på den första utmaningen, granskar och kategoriserar denna avhandling populära metoder inom robotgreppning och spårning av objekt, samt sammanfattar deras fördelar och nackdelar. Denna kategorisering utgör grunden för att välja metoder enligt de specifika behoven i olika applikationsscenarion. Som utvärdering, integreras samt jämförs två väletablerade metoder för posdetektion och objektspårning för ett vanligt applikationsscenario. Vidare diskuteras de tekniska och uppgiftsrelaterade utmaningarna i integrationsprocessen. Slutligen, som svar på den andra utmaningen, föreslås en uppsättning mätvärden för att utvärdera det integrerade systemet.
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Ovládání robota s Ackermannovým podvozkem / Controlling of Robot with Ackermann SteeringFryč, Martin January 2017 (has links)
In this paper is described creation of a robot in Robot Operating system (ROS) withAckermann steering. It contains the principle of Ackermann steering geometry, search ofcontroller boards and basics of ROS structure. A RC car with connected PixHawk controlleris used as a basis of the robot. On the robot is placed an onboard computer Raspberry Pi3 with running ROS. This computer is connected to a laptop through Wi-Fi network. Theprocedure of starting up the robot and ROS is also described in this paper, as well asdesign of the graphical user interface (GUI) that will display sensory data and allow otherfunctionality. Another part of thesis explains principle of an optical encoder and how tocreate your own encoder which can detect rotation of a wheel. This is used to implementrobot odometry. The structure of ROS navigation library is analyzed with regards to itscommissioning. Implementation of the GUI and navigation library will follow in the masterthesis.
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Vizuální systém pro detekci obsazenosti parkoviště pomocí hlubokých neuronových sítí / Visual Car-Detection on the Parking Lots Using Deep Neural NetworksStránský, 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 %.
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Lidar-based SLAM : Investigation of environmental changes and use of road-edges for improved positioningKarlsson, 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.
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Autonomous Skills for Remote Robotic AssemblyHaberbusch, Matthew Gavin 01 June 2020 (has links)
No description available.
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A Deep Learning Approach To Coarse Robot LocalizationBettaieb, Luc Alexandre 30 August 2017 (has links)
No description available.
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Service robot for the visually impaired: Providing navigational assistance using Deep LearningShakeel, Amlaan 28 July 2017 (has links)
No description available.
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MULTI-DRONE COLLABORATION FOR SEARCH AND RESCUE MISSIONSForsslund, 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.
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Path Planning and Collision Avoidance for a 6-DOF Manipulator : A Comparative Study of Path Planning and Collision Avoidance Algorithms for the Saab Seaeye eM1-7 Electric ManipulatorOhlander, Hampus, Johnson, David January 2024 (has links)
This project investigated the implementation and evaluation of various collision-free path planning algorithms for the Saab Seaeye eM1-7 6-DOF Electric Manipulator (eManip). The primary goal was to enhance the autonomous performance of the eManip by integrating efficient path planning methodologies, ultimately ensuring the avoidance of collisions and manipulator singularities during underwater operations. Key algorithms examined included the Rapidly-exploring Random Trees (RRT) algorithm and its enhanced variants. Through simulation tests in MATLAB and Gazebo, metrics such as planning time, path length, and the number of explored nodes were evaluated. The results highlighted the robustness of Goal-biased and Bidirectional RRT* (Gb-Bd-RRT*), which consistently performed well across various environments. The research also highlighted the correlation between algorithm effectiveness and specific task attributes, emphasizing their adaptability to complex environments. This research contributes valuable insights into the effectiveness of path planning algorithms, informing the selection and integration of viable strategies for 6-DOF robotic manipulators.
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