Increasing demands in areas such as security, surveillance, search and rescue, and communication, has promoted the research and development of unmanned aerial vehicles (UAVs) as such technologies can replace manned flights in dangerous or unfavorable conditions. Lighter-than-air UAVs such as blimps can carry higher payloads and can stay longer in the air compared to typical heavier-than-air UAVs such as aeroplanes or quadrotors. One purpose of this thesis is to develop a sensor suite basis for estimating the position and orientation of a blimp UAV in development with respect to a reference point for safer landing procedures using minimal on-board sensors. While the existing low-cost sensor package, including inertial measurement unit (IMU) and Global Navigation System (GPS) module, could be sufficient to estimate the pose of the blimp to a certain extent, the GPS module is not as precise in the short term, especially for altitude. The proposed system combines GPS and inertial data with information from a grounded infrared (IR) camera. Image frames are processed to identify three IR LEDs located on the UAV and each LED coordinate is estimated using a Perspective-n-Point (PnP) algorithm. Then the results from the PnP algorithm are fused with the GPS, accelerometer and gyroscope measurements using an Extended Kalman Filter (EKF) to get a more accurate estimate of the position and the orientation. Tests were conducted on a simulated blimp using the experimental avionics.

The work is focused on localization in wireless networks and localization using inertial measurement units. There is also included a theoretical analysis of used localization techniques. The work also describes how to create application for data collection, application for receiving and processing data and used database. In conclusion of this work is verifying the functionality of whole system.

The use and practical applications of small UAV systems has continually grown in the past several years in both the public and private sectors. These UAV systems are used for not only defensive purposes, but for commercial applications such as exterior bridge and home inspections, wildlife/wildfire management and observation, conservation exercises, law-enforcement, radio-repeating operations, and a wide variety of other uses that may not warrant the use, expense, space constraints, or risk of a manned aircraft. This thesis focuses on the design of a fixed pitch multirotor UAV system for use in furthering research projects and facilitating payload data collection from a flying platform without the expense or risk of testing with available larger UAV systems. The design of a multirotor UAV system with a flight control scheme, communication architecture and hardware, electrical architecture and hardware, and mechanical design is presented. An Extended Kalman Filter (EKF) strategy is implemented aboard a developed Inertial Measurement Unit (IMU) to estimate vehicle state. Experiments then validated the estimates from the EKF through a comparative approach between the developed unit and a commercial unit. A nonlinear flight control system is implemented based on an Integral-Backstepping control strategy. The flight control strategy was then fully simulated and exhaustively tested under a variety of external disturbances and initial conditions from a fully dynamic modeled environment. Parameters about the vehicle were experimentally determined to increase the accuracy of the model which would increase the chances of successful flight operations. Flight demonstrations were conducted to evaluate the abilities and performance of the control system, along with testing the interface abilities and reliability between a universal ground control station (UGCS) and the aircraft. Lastly, the model was revisited with the input data from the flight control experiment and the output captured was evaluated against the output of the model system to evaluate effectiveness, reliability, and accuracy of the model. The results of the comparison showed that the computer simulation was accurate in predicting attitude and altitude of the vehicle to that of the realized system. / Master of Science

This paper presents to find a solution, by designing a tool that could be used as a framework for the development of Motion Capture applications (Mocap) which is using with inertial Measurement Units (IMUs). Our main objective is how to find a tool that would be extremely fascinated and improved the accessibility to the development of Motion Capture applications. the methodology we applied for the literature review and we described and presented the factors about relevance of the technology and a different area of use and application, as well as to discover characteristics that need to be considered for the creation of a tool that could be utilized in the development of Mocap Applications. The second part of our thesis is certainly based on the design science research methodology; an artifact is planned through a designing a tool and prototype. We created a framework known as IMOTRAF which stands for Inertial Motion Tracking Framework that targeted more platforms than aREST framework, is to facilitate The Mocap application using by IMUs. The IMOTRAF framework should only focus in MCU/IMU platform. Throughout the collection data for the empirical analysis, we have learnt two protocols commonly used in wireless communication such as MQTT and Firmata. In this thesis we have implemented and tested to a new artifact that is made possible to connect many devices simultaneously via WIFI using by Firmata protocol.

No description available.

Hand-intensive work is closely related to the prevalence of upper body and hand/wrist work-related musculoskeletal disorders (WMSDs) in office work, manufacturing, service industries, as well as the healthcare industry. Some risk factors include vibrations, forceful exertions, heavy manual handling, repetitive motions, and prolonged nonneutral wrist postures. To address the growing WMSD epidemic among various occupational groups, simple-to-use exposure measurements are required. However, common quantitative measurement methods for the hand/wrist, such as electrogoniometry and optical motion capture, are both costly and challenging to use. Small, portable inertial measurement units (IMUs) may therefore be considered as a potentially good, affordable wearable option for measuring hand/wrist posture. However, it is difficult to track the position and orientation of a rigid body due to, among other factors, the IMU sensors' sensitivity to ambient magnetic disturbances. As a result, despite advancements in hardware quality, there is still no widely accepted standard for IMU-based motion capture. This study attempted to address this issue by analysing various orientation algorithms to estimate wrist angle from two IMU sensors and compare them to the electrogoniometer-derived measures, i.e., the gold-standard method in field measurements. Five hand-intensive simulated work tasks, each lasting 4–10 minutes, were completed by six participants. These tasks were chosen to resemble some difficult real-world work conditions closely. The wrist posture of the participants was measured using an electrogoniometer and two IMU sensors that were mounted on top of the electrogoniometer's end blocks. The IMU signal of each sensor was processed using seven different orientation algorithms, and the flexion/extension and radial/ulnar deviation angles between them were extracted and compared to the corresponding electrogoniometer angles. For the best-performing orientation algorithm, which was a first-order complementary filter, the mean cross-correlation coefficient between the two measurements was between 0.41 and 0.90 for the flexion/extension and between 0.19 and 0.53 for the radial/ulnar deviation. The mean absolute error (standard deviation) of the best-performing algorithm for the 10th, 50th, and 90th percentile flexion/extension was 8.38 (8.5), 3.99 (3.4), and 11.93 (10) degrees and for the corresponding percentiles of radial/ulnar deviation it was 9.6 (6.5), 5.5 (4.8), and 10.21 (7.1) degrees.  This result can likely be further improved by applying a better orientation algorithm and reducing measurement artifacts such as sensor vibration. However, this experiment demonstrates the potential of IMU-based wrist angle estimation as a simple measurement tool for occupational risk assessment. / Manuellt fysiskt arbete kan orsaka arbetsrelaterade besvär i rörelseorganen i överkropp och hand/handled inom kontorsarbete, tillverkning, industri samt inom hälso- och sjukvårdssektorn. Vibrationer, kraftfulla ansträngningar, tung manuell hantering, upprepade rörelser och långvariga icke-neutrala handledsställningar är några av riskfaktorerna. För att komma till rätta med de arbetsrelaterade besvären bland olika yrkesgrupper, krävs lättanvända exponeringsmätmetoder, eftersom observationsmetoder har en låg tillförlitlighet och de sedvanliga objektiva kvantitativa mätmetoderna för hand/handled, såsom elektrogoniometri och optiska rörelsemätningar, är både dyra och svåra att använda. Små, bärbara s.k. inertial measurement units (IMUs) är därför ett utmärkt, prisvärt och praktiskt alternativ för att mäta hand-/handledsrörelse. Men att estimera positionen och orienteringen av en kroppsdel med hjälp av IMU-sensorer medför stora utmaningar inte minst på grund av sensorernas känslighet för omgivande magnetiska störningar. Trots framsteg i hårdvarukvalitet, finns det fortfarande ingen allmänt accepterad standardmetod för IMU-baserad rörelsemätning. Syftet med det här projektet var att öka kunskapen i det här området genom att analysera olika orienteringsalgoritmer för att uppskatta den absoluta handledsvinkeln från två IMU-sensorer och sedan jämföra den med motsvarande ifrån den etablerade standardmätmetoden med en elektrogoniometer. Fem simulerade handintensiva arbetsuppgifter, var och en mellan 4–10 minuter, genomfördes av sex deltagare. Dessa uppgifter valdes för att härma några arbetsförhållanden som har rapporterats ge risk för besvär. Deltagarnas handledsställning mättes med hjälp av en elektrogoniometer och två IMU-sensorer som monterades ovanpå elektrogoniometers ändblock. IMU-signalen från varje sensor analyserades med sju olika, tidigare framtagna, orienteringsalgoritmer, vinklarna för flexion/extension samt radial/ulnar deviation beräknades och jämfördes sedan med motsvarande elektrogoniometervinklar. För den bäst presterande algoritmen, en första ordningens komplementfilter, varierade den genomsnittliga korrelationskoefficienten mellan 0,41 och 0,90 för flexion/extension och mellan 0,19 och 0,53 för radial/ulnar deviation. Det genomsnittliga absoluta felet (standardavvikelse), för den bäst presterande algoritmen, för den 10:e, 50:e och 90:e percentilen flexion/extension var 8,38 (8,5), 3,99 (3,4) och 11,93 (10) grader. Motsvarande percentiler av radial/ulnar deviation var 9,6 (6,5), 5,5 (4,8) och 10,21 (7,1) grader. Detta resultat kan sannolikt förbättras ytterligare genom att tillämpa en bättre orienteringsalgoritm och minska mätartefakter från sensorvibrationer. Detta experiment visar dock potentialen för IMU-baserad handledsvinkeluppskattning som ett enkelt mätverktyg vid riskbedömningar inom manuella arbeten.

Animals have evolved over billions of years and understanding these complex and intertwined systems have potential to advance the technology in the field of sports science, robotics and more. As such, a gait analysis using Motion Capture (MOCAP) technology is the subject of a number of research and development projects aimed at obtaining quantitative measurements. Existing MOCAP technology has limited the majority of studies to the analysis of the steady-state locomotion in a controlled (indoor) laboratory environment. MOCAP systems such as the optical, non-optical acoustic and non-optical magnetic MOCAP systems require predefined capture volume and controlled environmental conditions whilst the non-optical mechanical MOCAP system impedes the motion of the subject. Although the non-optical inertial MOCAP system allows MOCAP in an outdoor environment, it suffers from measurement noise and drift and lacks global trajectory information. The accuracy of these MOCAP systems are known to decrease during the tracking of the transient locomotion. Quantifying the manoeuvrability of animals in their natural habitat to answer the question “Why are animals so manoeuvrable?” remains a challenge. This research aims to develop an outdoor MOCAP system that will allow tracking of the steady-state as well as the transient locomotion of an animal in its natural habitat outside a controlled laboratory condition. A number of researchers have developed novel MOCAP systems with the same aim of creating an outdoor MOCAP system that is aimed at tracking the motion outside a controlled laboratory (indoor) environment with unlimited capture volume. These novel MOCAP systems are either not validated against the commercial MOCAP systems or do not have comparable sub-millimetre accuracy as the commercial MOCAP systems. The developed DGPS/MEMS-IMU multi-receiver fusion MOCAP system was assessed to have global trajectory accuracy of _0:0394m, relative limb position accuracy of _0:006497m. To conclude the research, several recommendations are made to improve the developed MOCAP system and to prepare for a field-testing with a wild animal from a family of a terrestrial megafauna.

Introduktion: Ryggproblematik inom längdskidåkning har blivit ett omdiskuterat ämne inom idrottssektorn, detta i samband med att deltekniken stakning tagit allt större plats inom den klassiska skidåkningen. En regel om maximal stavlängd på 83% av åkarens kroppslängd har införts, samtidigt som studier visar att en längre stav än 83% visat fördelar för prestation. Förutom de prestationsrelaterade effekterna har forskning visat på att längre stavar ger mindre ledvinklar. En ökad grad flexion av ländryggen antas öka risken för att drabbas av diskbråck. Om stavlängden har betydelse för denna skulle optimering av stavlängd kunna bidra till att förebygga skador. Syfte: Syftet med denna experimentella pilotstudie var att jämföra rörelseomfång i ländryggen vid stakning med stavar i olika längder, samt hur stavlängder påverkar ledvinklar i knä, fot, höft, bäcken och thorakalrygg. Metod: En försöksperson rekryterades. Mätningen utfördes i en snöhall på uppmätt sträcka om 50m, vid stakning med stavlängderna 83%, 86% och 89% av kroppslängden. För mätning av rörelseomfång användes ett IMU-system. Insamlad data extraherades via Noraxon MR3 och bearbetades i Matlab för att få fram medelvärden av ledvinklar. Resultat: Lumbalflexionen var som störst vid 89% stavlängd (41,5°) och lägst vid 86% (36,3°). Minst ledvinklar i fot, knä, höft och bäcken uppmättes samtliga vid stavlängd 89%, där även thorakalflexionen var som minst. Konklusion: Resultaten indikerar att stavlängder inom 83-86% av kroppslängden minskar rörelseomfånget och flexionsgraden av ländryggen vid stakning. Ytterligare forskning behövs för att bekräfta sambandet mellan ländryggsflexion och stavlängd och om en optimal stavlängd skulle kunna verka skadeförebyggande.

Safety risk evaluation is critical in autonomous vehicle applications. This research aims to develop, implement, and validate new safety monitoring methods for navigation in Global Navigation Satellite System (GNSS)-denied environments. The methods quantify uncertainty in sensors and algorithms that exploit the complementary properties of light detection and ranging (LiDAR) and inertial measuring units (IMU). This dissertation describes the following four contributions. First, we focus on sensor augmentation for landmark-based localization. We develop new IMU/LiDAR integration methods that guarantee a bound on the integrity risk, which is the probability that the navigation error exceeds predefined acceptability limits. IMU data improves LiDAR position and orientation (pose) prediction and LiDAR limits the IMU error drift over time. In addition, LiDAR return-light intensity measurements improve landmarks recognition. As compared to using the sensors individually, tightly-coupled IMU/LiDAR not only increases pose estimation accuracy but also reduces the risk of incorrectly associating perceived features with mapped landmarks. Second, we consider algorithm improvements. We derive and analyze a new data association method that provides a tight bound on the risk of incorrect association for LiDAR feature-based localization. The new data association criterion uses projections of the extended Kalman filter's (EKF) innovation vector rather than more conventional innovation vector norms. This method decreases the integrity risk by improving our ability to predict the risk of incorrect association. Third, we depart from landmark-based approaches. We develop a spherical grid-based localization method that leverages quantization theory to bound navigation uncertainty. This method is integrated with an iterative EKF to establish an analytical bound on the vehicle's pose estimation error. Unlike landmark-based localization which requires feature extraction and data association, this method uses the entire LiDAR point cloud and is robust to extraction and association failures. Fourth, to validate these methods, we designed and built two testbeds for indoor and outdoor experiments. The indoor testbed includes a sensor platform installed on a rover moving on a figure-eight track in a controlled lab environment. The repeated figure-eight trajectory provides empirical pose estimation error distributions that can directly be compared with analytical error bounds. The outdoor testbed required another set of navigation sensors for reference truth trajectory generation. Sensors were mounted on a car to validate our algorithms in a realistic automotive driving environment. / Doctor of Philosophy / Advances in computing and sensing technologies have enabled large scale demonstrations of autonomous vehicle operations including pilot programs for self-driving cars on public roads. However, a key question that has yet to be answered is about how safe these vehicles really are. "Autonomously" driving millions of miles (with a trained safety driver taking over control to prevent potential collisions) is insufficient to prove fatality rates matching human performance, i.e., lower than 1 per 100,000,000 miles driven. The safety of an autonomous vehicle depends on the safety of its individual subsystems, components, connected infrastructure, etc. In this research, we evaluate the safety of the navigation subsystem which uses sensor information to determine the vehicle's location and orientation. We focus on light detection and ranging (LiDAR)and inertial measuring units (IMU). A LiDAR provides a point cloud representation of the environment by measuring distances to surrounding objects using beams of infrared light (laser beams) sent at regular angular intervals. An IMU measures the acceleration and angular velocity of the vehicle. We assume that a map of the environment is available. In the first part of this research, we extract recognizable objects from the LiDAR point cloud and match them with those in the map: this process helps estimate the vehicle's position and orientation. We identify the process' limitations that include incorrectly matching sensed and mapped landmarks. We develop new methods to quantify their impacts on localization errors, which we then reduce by incorporating additional IMU data. In the second part of this dissertation, we design and evaluate a new approach specifically aimed at provably increasing confidence in landmark matching, thereby improving vehicle navigation safety. Third, instead of isolating individual landmarks, we use the LiDAR point cloud as a whole and match it directly with the map. The challenge with this approach was in efficiently and accurately quantifying the confidence that can be placed in the vehicle's navigation solution. We tested these navigation methods using experimental data collected in a controlled lab environment and in a real-world scenario.

Foot placement prediction can be important for exoskeleton and prosthesis controllers, human-robot interaction, or body-worn systems to prevent slips or trips. Previous studies investigating foot placement prediction have been limited to predicting foot placement during the swing phase, and do not fully consider contextual information such as the preceding step or the stance phase before push-off. In this study, a deep learning-based foot placement prediction approach was proposed, where the deep learning models were designed to sequentially process data from three IMU sensors mounted on pelvis and feet. The raw sensor data are pre-processed to generate multi-variable time-series data for training two deep learning models, where the first model estimates the gait progression and the second model subsequently predicts the next foot placement. The ground truth gait phase data and foot placement data are acquired from a motion capture system. Ten healthy subjects were invited to walk naturally at different speeds on a treadmill. In cross-subject learning, the trained models had a mean distance error of 5.93 cm for foot placement prediction. In single-subject learning, the prediction accuracy improved with additional training data, and a mean distance error of 2.60 cm was achieved by fine-tuning the cross-subject validated models with the target subject data. Even from 25-81% in the gait cycle, mean distance errors were only 6.99 cm and 3.22 cm for cross-subject learning and single-subject learning, respectively / Master of Science / This study proposes a new approach for predicting where a person's foot will land during walking, which could be useful in controlling robots and wearable devices that work with humans to prevent events such as slips and falls and allow for more smooth human-robot interactions. Although foot placement prediction has great potential in various domains, current works in this area are limited in terms of practicality and accuracy. The proposed approach uses data from inertial sensors attached to the pelvis and feet, and two deep learning models are trained to estimate the person's walking pattern and predict their next foot placement. The approach was tested on ten healthy individuals walking at different speeds on a treadmill, and achieved state-of-the-arts results. The results suggest that this approach could be a promising method when sufficient data from multiple people are available.