Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2309.15405 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929364870889472 |
|---|---|
| author | Nourizadeh, Payam Milford, Michael Fischer, Tobias |
| author_facet | Nourizadeh, Payam Milford, Michael Fischer, Tobias |
| contents | Robot navigation requires an autonomy pipeline that is robust to environmental changes and effective in varying conditions. Teach and Repeat (T&R) navigation has shown high performance in autonomous repeated tasks under challenging circumstances, but research within T&R has predominantly focused on motion planning as opposed to motion control. In this paper, we propose a novel T&R system based on a robust motion control technique for a skid-steering mobile robot using sliding-mode control that effectively handles uncertainties that are particularly pronounced in the T&R task, where sensor noises, parametric uncertainties, and wheel-terrain interaction are common challenges. We first theoretically demonstrate that the proposed T&R system is globally stable and robust while considering the uncertainties of the closed-loop system. When deployed on a Clearpath Jackal robot, we then show the global stability of the proposed system in both indoor and outdoor environments covering different terrains, outperforming previous state-of-the-art methods in terms of mean average trajectory error and stability in these challenging environments. This paper makes an important step towards long-term autonomous T&R navigation with ensured safety guarantees. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_15405 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Teach and Repeat Navigation: A Robust Control Approach Nourizadeh, Payam Milford, Michael Fischer, Tobias Robotics Systems and Control Robot navigation requires an autonomy pipeline that is robust to environmental changes and effective in varying conditions. Teach and Repeat (T&R) navigation has shown high performance in autonomous repeated tasks under challenging circumstances, but research within T&R has predominantly focused on motion planning as opposed to motion control. In this paper, we propose a novel T&R system based on a robust motion control technique for a skid-steering mobile robot using sliding-mode control that effectively handles uncertainties that are particularly pronounced in the T&R task, where sensor noises, parametric uncertainties, and wheel-terrain interaction are common challenges. We first theoretically demonstrate that the proposed T&R system is globally stable and robust while considering the uncertainties of the closed-loop system. When deployed on a Clearpath Jackal robot, we then show the global stability of the proposed system in both indoor and outdoor environments covering different terrains, outperforming previous state-of-the-art methods in terms of mean average trajectory error and stability in these challenging environments. This paper makes an important step towards long-term autonomous T&R navigation with ensured safety guarantees. |
| title | Teach and Repeat Navigation: A Robust Control Approach |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2309.15405 |