Full metadata
Title
A Deep Reinforcement Learning Approach for Robotic Bicycle Stabilization
Description
Bicycle stabilization has become a popular topic because of its complex dynamic behavior and the large body of bicycle modeling research. Riding a bicycle requires accurately performing several tasks, such as balancing and navigation which may be difficult for disabled people. Their problems could be partially reduced by providing steering assistance. For stabilization of these highly maneuverable and efficient machines, many control techniques have been applied – achieving interesting results, but with some limitations which includes strict environmental requirements. This thesis expands on the work of Randlov and Alstrom, using reinforcement learning for bicycle self-stabilization with robotic steering. This thesis applies the deep deterministic policy gradient algorithm, which can handle continuous action spaces which is not possible for Q-learning technique. The research involved algorithm training on virtual environments followed by simulations to assess its results. Furthermore, hardware testing was also conducted on Arizona State University’s RISE lab Smart bicycle platform for testing its self-balancing performance. Detailed analysis of the bicycle trial runs are presented. Validation of testing was done by plotting the real-time states and actions collected during the outdoor testing which included the roll angle of bicycle. Further improvements in regard to model training and hardware testing are also presented.
Date Created
2020
Contributors
- Turakhia, Shubham (Author)
- Zhang, Wenlong (Thesis advisor)
- Yong, Sze Zheng (Committee member)
- Ren, Yi (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
67 pages
Language
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.62990
Level of coding
minimal
Note
Masters Thesis Mechanical Engineering 2020
System Created
- 2021-01-14 09:18:17
System Modified
- 2021-08-26 09:47:01
- 3 years ago
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