Enhanced UAV Navigation Under Challenging Conditions
| dc.contributor.advisor | El-Sheimy, Naser | |
| dc.contributor.advisor | Sesay, Abu B. | |
| dc.contributor.author | Zahran, Shady Abd El-Kader | |
| dc.contributor.committeemember | Moussa, Adel M. | |
| dc.contributor.committeemember | Noureldin, Aboelmagd | |
| dc.contributor.committeemember | Costa Sousa, Mario | |
| dc.contributor.committeemember | Detchev, Ivan D. | |
| dc.contributor.committeemember | Toth, Charles K. | |
| dc.date | 2019-11 | |
| dc.date.accessioned | 2019-07-25T18:25:38Z | |
| dc.date.available | 2019-07-25T18:25:38Z | |
| dc.date.issued | 2019-07-23 | |
| dc.description.abstract | During the past decade, there has been an enormous increase in the applications utilizing fully autonomous Unmanned Aerial Vehicles (UAVs). Initially these applications were mostly restricted to military fields like border surveillance, troops enforcement, combat, target, and decoy. More recently they have been extended to many civilian applications, such as firefighting, traffic monitoring, and commercial UAVs. This wide variety of applications makes the UAVs autonomous navigation a challenging task. The UAVs mainly depend on the integration of Global Navigation Satellite Systems (GNSS) and Inertial Navigation System (INS) to navigate autonomously but during the lack or unavailability of the GNSS signals, the UAVs lose their ability to navigate autonomously, due to the massive drift exhibited by the INS when working in dead reckoning mode. GNSS signals may be unavailable due to jamming, spoofing, blockage, or multipath. So, another aiding system must be integrated with the INS, to yield a reliable navigation solution that makes the UAV capable of fulfilling its assigned tasks in a wider range of working conditions. Different sensors and methods were utilized to aid the navigation solution during GNSS outage. Light Detection and Ranging sensor (LiDAR) is one of the main sensors integrated with INS during these periods. The main drawback in the use of LiDAR with small and micro UAVs, is the weight, size, power consumption, and high computational cost. Camera is also considered as a viable solution to aid in navigation during the GNSS outage periods, as it is characterized by features that make it suitable for small and micro UAVs like light weight, small size, and low power consumption, also it provides rich information like features and colors. Visual information provided by a camera can be used for 3D surrounding construction, obstacle avoidance, and position estimation. One of the algorithms mostly used for exploiting camera in UAV navigation is Visual Odometry (VO), which depends on the extraction of the vehicle velocity from matching features of successive images. One of the biggest problems facing the use of a single camera is the loss of scale information. Although this issue can usually be resolved by using stereo/multiple cameras, such a solution is not so effective for small and micro UAVs, because its accuracy mainly depends on the base distance between the cameras, which is necessarily limited in such cases. Regardless of using single/multiple cameras, this sensor performance is still greatly sensitive to certain environmental factors, like light variation or featureless scenes. Radar sensor is much more superior to cameras or LiDAR, due to its immunity to environmental changes. Previously Radar’s weight, size, and power consumption were not adequate for small/micro UAVs. Ultra-Wide Band (UWB) devices have been recently considered in several UAV aided navigation works. Although their great potential to aid the UAV during loss of GNSS signals (absolute positions), due to their limited power, they can only be used in small areas (like indoors). In order to use them outdoors, a huge infrastructure is required, in addition to the availability of beacons with known positions in the flight area is not always guaranteed. So other methods or approaches that do not cost the UAV additional size, weight, cost, or power consumption is essential to achieve a robust and accurate navigation system that is able to fulfill different tasks in all circumstances with acceptable performance. First approach taken during the research is the Vehicle Dynamic Model (VDM) enhanced navigation system. The VDM is the relation between the actuators and vehicle states (accelerations and rotations). We accommodate such approach because it will not cost the UAV any additional weight, size, power, or cost. The main drawback of accommodating this approach is the requirement for a special equipment to estimate the mathematical model parameter of the UAV. Any perturbations during this modelling process will make this approach unable to aid the UAV during GNSS signals outage. In order to avoid such drawback a Machine Learning (ML) approach is adopted to take advantage of the available data from previous flights. Although this VDM- machine learning approach greatly enhanced the navigation solution compared to low-cost INS solution, but the model formed with the aid of the ML approach is specific for this UAV with this configuration, this leads to the second accommodated approach, which is enhanced UAV navigation using micro-Radar as a way to find a more robust aiding approach that’s suitable for most UAVs. Radar as a sensor is more immune to environmental changes (e.g. rain, fog, light conditions), but its weight, size, and power requirement makes it not suitable for such small UAVs. Nowadays micro Radars is available due to the advancement in technologies, which makes it a good candidate to aid small UAVs navigation system during GNSS signals outage. However, low-cost Radars come with more challenges with regards to the extraction of useful information, due to its poor performance, and the existence of large amount of clutters especially at low altitudes. Typical radar targets extractions algorithms are not suitable, especially in our case where the Radar is mounted on board of the UAV, so there is no significant difference between the targets and the background. In order to efficiently extract the targets and estimate the UAV’s velocity a computer vision-based approach is accommodated instead of the typical Radar approaches, our approach consider the output of the radar (range and velocity) as image. This approach greatly enhances the navigation solution during six minutes of complete GNSS signal outage, which reached a 2D RMSE of 5.81 m compared to INS RMSE which reached hundreds of meters. Although micro-Radar system greatly enhance the navigation solution but it’s power requirement, weight, and cost are still a burden on small UAVs. So, the Radar based navigation is followed by other approach that respect all the UAVs limitation and efficiently estimate the UAVs velocity. This final approach is based on manipulating the typical use of two sensors (Hall-effect magnetic sensor, and Mass-flow meter). Both sensors are used to efficiently estimate the velocity of a drone while respecting small drones’ limitations. By utilizing these two sensors the navigation solution is greatly enhanced during GNSS signal outage compared to low-cost INS. These two sensors specifications facilitate the ability to be merged with other sensors like camera or LiDAR to enhance the navigation solution even more. | en_US |
| dc.identifier.citation | Zahran, S. A. E-K. (2019). Enhanced UAV Navigation Under Challenging Conditions (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. | en_US |
| dc.identifier.doi | http://dx.doi.org/10.11575/PRISM/36771 | |
| dc.identifier.uri | http://hdl.handle.net/1880/110671 | |
| dc.language.iso | eng | en_US |
| dc.publisher.faculty | Schulich School of Engineering | en_US |
| dc.publisher.institution | University of Calgary | en |
| dc.rights | University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. | en_US |
| dc.subject | Navigation | en_US |
| dc.subject | Unmanned Aerial Vehicle | en_US |
| dc.subject | Vehichle Dynamic Model | en_US |
| dc.subject | Machine Learning Regression | en_US |
| dc.subject | Extended Kalman Filter | en_US |
| dc.subject | Global Navigation Satellite System | en_US |
| dc.subject | Inertial Navigation System | en_US |
| dc.subject | Radar Navigation | en_US |
| dc.subject | Radar Odometry | en_US |
| dc.subject | Hall Effect Magnetic Sensor | en_US |
| dc.subject | Mass Flow Sensor | en_US |
| dc.subject | Enhanced Scan Matching | en_US |
| dc.subject | ICP | en_US |
| dc.subject | SLAM | en_US |
| dc.subject | Hector SLAM | en_US |
| dc.subject.classification | Artificial Intelligence | en_US |
| dc.subject.classification | Engineering--Electronics and Electrical | en_US |
| dc.subject.classification | Robotics | en_US |
| dc.title | Enhanced UAV Navigation Under Challenging Conditions | en_US |
| dc.type | doctoral thesis | en_US |
| thesis.degree.discipline | Engineering – Geomatics | en_US |
| thesis.degree.grantor | University of Calgary | en_US |
| thesis.degree.name | Doctor of Philosophy (PhD) | en_US |
| ucalgary.item.requestcopy | true | en_US |