Data-driven Target Tracking and Hybrid Path Planning Methods for Autonomous Operation of UAV
| dc.contributor.author | Choi, Jae-Young | en |
| dc.contributor.committeechair | Choi, Seongim C. | en |
| dc.contributor.committeemember | Woolsey, Craig A. | en |
| dc.contributor.committeemember | Lowe, K. Todd | en |
| dc.contributor.committeemember | Raj, Pradeep R. | en |
| dc.contributor.committeemember | Wang, Kevin G. W. | en |
| dc.contributor.department | Aerospace and Ocean Engineering | en |
| dc.date.accessioned | 2023-08-25T16:44:25Z | en |
| dc.date.available | 2023-08-25T16:44:25Z | en |
| dc.date.issued | 2023 | en |
| dc.description.abstract | The present study focuses on developing an efficient and stable unmanned aerial system traffic management (UTM) system that utilizes a data-driven target tracking method and a distributed path planning algorithm for multiple Unmanned Aerial Vehicle (UAV) operations with local dynamic networks, which can provide flexible scalability, enabling autonomous operation of a large number of UAVs in dynamically changing environment. Traditional dynamic motion-based target tracking methods often encounter limitations due to their reliance on a finite number of dynamic motion models. To address this, data-driven target tracking methods were developed based on the statistical model of the Gaussian mixture model (GMM) and deep neural networks of long-short term memory (LSTM) model, to estimate instant and future states of UAV for local path planning problems. The estimation accuracy of the data-driven target tracking methods were analyzed and compared with dynamic model-based target tracking methods. A hybrid dynamic path planning algorithm was proposed, which selectively employs grid-free and -based path search methods depending on the spatio-temporal characteristics of the environments. In static environment, the artificial potential field (APF) method was utilized, while the $A^*$ algorithm was applied in the dynamic state environment. Furthermore, the data-driven target tracking method was integrated with the hybrid path planning algorithm to enhance deconfliction. To ensure smooth trajectories, a minimum snap trajectory method was applied to the planned paths, enabling controller tracking that remains dynamically feasible throughout the entire operation of UAVs. The methods were validated in the Software-in-the-loop (SITL) demonstration with the simple PID controller of the UAVs implemented in the software program. | en |
| dc.description.abstractgeneral | This dissertation focuses on developing data-driven models for tracking and path planning of Unmanned Aerial Vehicle (UAV) in dynamic environments with multiple operations. The goal is to improve the accuracy and efficiency of Unmanned Aircraft System traffic management (UTM) under such conditions. The data-driven models are based on Gaussian mixture model (GMM) and long-short term memory (LSTM) and are used to estimate the instant and consecutive future states of UAV for local planning problems. These models are compared to traditional target tracking models, which use dynamic motion models like constant velocity or acceleration. A hybrid dynamic path planning approach is also proposed to solve dynamic path planning problems for multiple UAV operations at an efficient computation cost. The algorithm selectively employs a path planning method between grid-free and grid-based methods depending on the characteristics of the environment. In static state conditions, the system uses the artificial potential field method (APF). When the environment is time-variant, local path planning problems are solved by activating the $A^*$ algorithm. Also, the planned paths are refined by minimum snap trajectory to ensure that the path is dynamically feasible throughout a full operation of the UAV along with controller tracking. The methods were validated in the Software-in-the-loop (SITL) demonstration with the simple PID controller of the UAVs implemented in the software program. | en |
| dc.description.degree | Ph.D. | en |
| dc.format.medium | ETD | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.uri | http://hdl.handle.net/10919/116130 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | en |
| dc.subject | UTM | en |
| dc.subject | Trajectory modeling | en |
| dc.subject | Path planning | en |
| dc.subject | Gaussian mixture model | en |
| dc.subject | Deep neural network | en |
| dc.subject | Software-In-The-Loop (SITL) | en |
| dc.title | Data-driven Target Tracking and Hybrid Path Planning Methods for Autonomous Operation of UAV | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Aerospace Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Ph.D. | en |