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The aim of this thesis was to develop an energy harvesting solution for the UWASA Node for wind power station applications. Energy harvesting is the process by which small amounts of ambient energy is collected for use by electronics such as wireless sensor nodes. The developed energy harvesting solution is capable of supplying enough energy for the UWASA Node to perform a wide variety of tasks indefinitely, without the need for changing its battery. The wireless sensor node can for example read sensors at high sampling rates and store or wirelessly transmit these readings. It can also perform complex computations and react to changes in its environment. One intended use was monitoring vibrations of wind turbines blades, but field tests have yet to be done. This master’s thesis builds on the findings of my bachelor’s thesis, Höglund (2014) in the references. In the bachelor’s thesis different methods of energy harvesting were in-vestigated to find the most suitable methods for this project. In this master’s thesis a prototype energy harvester and energy management circuit was developed and tested. The prototype is capable of harvesting tens of milliwatts from a small solar cell. It could also be modified to harvest another ambient energy source or several sources at once. Every part of the energy harvester and energy management circuit is discussed in detail and laboratory tests are presented. Different means of maximum power point tracking were tested and evaluated. The prototype energy harvester was built using a modular approach so that energy harvesting from multiple sources of energy easily can be ac-complished by adding a few components for each source to the harvesting circuit. The programming of the sensor node also needs to be adapted so that it runs optimally from the harvested energy by scheduling measurements and wireless communication. A low-power real-time clock and a latching switch were included on the prototype PCB for switching on and off the power to the sensor node completely in order to consume as little energy as possible when the sensor node is inactive.
This is a survey of research published on the subjects of telerobotics, haptic feedback, and mixed reality applied to surface finishing. The survey especially focuses on how visuo-haptic feedback can be used to improve a grinding process using a remote manipulator or robot. The benefits of teleoperation and reasons for using haptic feedback are presented. The use of genetic algorithms for optimizing haptic sensing is briefly discussed. Ways of augmenting the operator’s vision are described. Visual feedback can be used to find defects and analyze the quality of the surface resulting from the surface finishing process. Visual cues can also be used to aid a human operator in manipulating a robot precisely and avoiding collisions.
The use of virtual reality technology has become popular in modern theme parks across the world. Attractions using virtual reality technologies offer highly immersive experiences as if people are really staying in fictional worlds that the theme parks like to create. Haptic feedback is an important piece in virtual reality, where it can increase the immersion and enjoyment, but most virtual reality attractions in theme parks do not offer enough haptic feedback. In this paper, we present HapticDaijya, which is a waist-worn robot capable of giving various haptic and tactile feedback on the torso, neck, face, arms and hands. We present the design and implementation of HapticDaijya. Then, we show a user study and analyze the results for investigating its feasibility. Also, we present preliminary evaluation results that gauged the user’s accuracy in distinguishing the locations of taps applied on the chest, as well as general usability and user acceptance.
Rapid development of driving assistance technology and vehicular communication for intelligent transportation systems has proved that it can improve the safety, efficiency and sustainability of vehicles. This thesis endeavours to develop an experimental platform to demonstrate the use of cooperative adaptive cruise control (CACC) systems. The work analyzes existing longitudinal controllers and their string stability in homogeneous platoons. Furthermore field tests are carried out using the longitudinal controller with multiple autonomous experimental vehicle platforms to verify the effectiveness of the controller. According to the results of simulation and field tests, the proposed CACC platooning approach shows great benefits for longitudinal vehicle platooning.
One of the most significant global challenges faced during the COVID-19 pandemic was the need for efficient disinfection methods to prevent the spread of the virus. Ultraviolet-C (UV-C) disinfection robots became increasingly popular due to their ability to kill harmful bacteria and viruses using UV-C light. However, UV-C light can also be harmful to humans, so it is important to ensure that people are not exposed to it during disinfection operations. Existing object detection algorithms are limited to conventional images. These images can be stitched together to get a 360-degree view of surroundings but it can be computationally expensive and they may have different resolutions, lighting conditions, and noise levels. This thesis investigates the feasibility of using motion detection and YOLO (You Only Look Once) object detection to accurately detect individuals present within the disinfection environment using omnidirectional cameras. To achieve this goal, motion detection is implemented and analyzed as well as different YOLO models are trained on the 360 Indoor-dataset. These models are then evaluated on their ability to detect people in unseen environments. The results show that motion detection is not a feasible solution for our application because of its static background requirement, while YOLO demonstrates effectiveness with an omnidirectional camera achieving a mean Average Precision (mAP) of 75.34\%. The results also show that among all the YOLO models YOLOv5m achieved the best overall performance, with a precision of 0.80, a recall of 0.68, mAP of 0.73, and an inference speed of 8.2 ms. This indicates that the YOLOv5m model is a promising choice for person detection in real-time disinfection applications. In addition to the experimental results, this thesis also provides a discussion about how to use our trained YOLOv5m in the industry setting to avoid false detections. Overall, this thesis makes a notable contribution by evaluating motion detection for the disinfection robot and by demonstrating the feasibility of using YOLO with an omnidirectional camera through training on 360-degree images. Furthermore, the thesis identifies a more efficient version of YOLO for industrial deployment, with the potential for further development and improved performance.