Abstract
dc:description.abstract<p>This project shows a combination for drones autonomous navigation in dynamic environments. The algorithm combine Social GAN SGAN for human trajectory prediction with Rapidly-exploring Random Tree Star RRT* for path planning. The objective is to efficiently and safe navigate in the area with human. Drones would avoid moving human and maintaining optimal flight path. During training SGAN model, we use both public datasets and dataset collected in the lab, which improve its adaptability in the lab. This experiment was tested through simulations and real-word experiment. SGAN provided a good prediction of human trajectories, which help drones to adjust their path. RRT* would replan the path when potential collisions are detected. The combined algorithm had a high success rate in collision avoidance(80% in real-word experiments). Additionally, incorporating lab collected data improved the accuracy and reduce the average displacement error (ADE) and final displacement error (FDE). This study also faces some limitation. In real-world, the use of high-precision OptiTrack motion capture is not avaliable. Besides, the experiment area is small and in real world the environments are more complex. Future work should focus on improving the algorithm in larger and more complex environment. In conclusion, this research demonstrates the potential of combining deep learning-based path prediction with real-time path planning. It provide a robust solution for drones to safely navigate in dynamic environment.</p>
Degree
thesis:*- Name thesis:degree_name
- Master of Science (MS)
- Level thesis:degree_level
- Thesis
- Discipline thesis:degree_discipline
- Electrical & Systems Engineering
- Year dc:date.available
- 2024
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Zhou, Beichen
- Contributors dc:contributor
-
- Ioannis (Yiannis) Kantaros
- ShiNung Ching, Shen Zeng
Subjects
dc:subject × 7Rights
dc:rights- Statement dc:rights
-
- I have not registered my thesis with the U.S. Copyright Office, and do not intend to.
- Language dc:language
- English (en)
Identifiers
dc:identifier.*- OAI identifier oai:identifier
- oai:openscholarship.wustl.edu:eng_etds-2182