Exploring Deep Reinforcement Learning for Robust Target Tracking using Micro Aerial Vehicles
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916117439578112 |
|---|---|
| author | Dionigi, Alberto Leomanni, Mirko Saviolo, Alessandro Loianno, Giuseppe Costante, Gabriele |
| author_facet | Dionigi, Alberto Leomanni, Mirko Saviolo, Alessandro Loianno, Giuseppe Costante, Gabriele |
| contents | The capability to autonomously track a non-cooperative target is a key technological requirement for micro aerial vehicles. In this paper, we propose an output feedback control scheme based on deep reinforcement learning for controlling a micro aerial vehicle to persistently track a flying target while maintaining visual contact. The proposed method leverages relative position data for control, relaxing the assumption of having access to full state information which is typical of related approaches in literature. Moreover, we exploit classical robustness indicators in the learning process through domain randomization to increase the robustness of the learned policy. Experimental results validate the proposed approach for target tracking, demonstrating high performance and robustness with respect to mass mismatches and control delays. The resulting nonlinear controller significantly outperforms a standard model-based design in numerous off-nominal scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_17552 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Exploring Deep Reinforcement Learning for Robust Target Tracking using Micro Aerial Vehicles Dionigi, Alberto Leomanni, Mirko Saviolo, Alessandro Loianno, Giuseppe Costante, Gabriele Robotics The capability to autonomously track a non-cooperative target is a key technological requirement for micro aerial vehicles. In this paper, we propose an output feedback control scheme based on deep reinforcement learning for controlling a micro aerial vehicle to persistently track a flying target while maintaining visual contact. The proposed method leverages relative position data for control, relaxing the assumption of having access to full state information which is typical of related approaches in literature. Moreover, we exploit classical robustness indicators in the learning process through domain randomization to increase the robustness of the learned policy. Experimental results validate the proposed approach for target tracking, demonstrating high performance and robustness with respect to mass mismatches and control delays. The resulting nonlinear controller significantly outperforms a standard model-based design in numerous off-nominal scenarios. |
| title | Exploring Deep Reinforcement Learning for Robust Target Tracking using Micro Aerial Vehicles |
| topic | Robotics |
| url | https://arxiv.org/abs/2312.17552 |