Closing the Perception-Action Loop for Semantically Safe Navigation in Semi-Static Environments
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911849774055424 |
|---|---|
| author | Qian, Jingxing Zhou, Siqi Ren, Nicholas Jianrui Chatrath, Veronica Schoellig, Angela P. |
| author_facet | Qian, Jingxing Zhou, Siqi Ren, Nicholas Jianrui Chatrath, Veronica Schoellig, Angela P. |
| contents | Autonomous robots navigating in changing environments demand adaptive navigation strategies for safe long-term operation. While many modern control paradigms offer theoretical guarantees, they often assume known extrinsic safety constraints, overlooking challenges when deployed in real-world environments where objects can appear, disappear, and shift over time. In this paper, we present a closed-loop perception-action pipeline that bridges this gap. Our system encodes an online-constructed dense map, along with object-level semantic and consistency estimates into a control barrier function (CBF) to regulate safe regions in the scene. A model predictive controller (MPC) leverages the CBF-based safety constraints to adapt its navigation behaviour, which is particularly crucial when potential scene changes occur. We test the system in simulations and real-world experiments to demonstrate the impact of semantic information and scene change handling on robot behavior, validating the practicality of our approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_14546 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Closing the Perception-Action Loop for Semantically Safe Navigation in Semi-Static Environments Qian, Jingxing Zhou, Siqi Ren, Nicholas Jianrui Chatrath, Veronica Schoellig, Angela P. Robotics Autonomous robots navigating in changing environments demand adaptive navigation strategies for safe long-term operation. While many modern control paradigms offer theoretical guarantees, they often assume known extrinsic safety constraints, overlooking challenges when deployed in real-world environments where objects can appear, disappear, and shift over time. In this paper, we present a closed-loop perception-action pipeline that bridges this gap. Our system encodes an online-constructed dense map, along with object-level semantic and consistency estimates into a control barrier function (CBF) to regulate safe regions in the scene. A model predictive controller (MPC) leverages the CBF-based safety constraints to adapt its navigation behaviour, which is particularly crucial when potential scene changes occur. We test the system in simulations and real-world experiments to demonstrate the impact of semantic information and scene change handling on robot behavior, validating the practicality of our approach. |
| title | Closing the Perception-Action Loop for Semantically Safe Navigation in Semi-Static Environments |
| topic | Robotics |
| url | https://arxiv.org/abs/2404.14546 |