Using 3-D LiDAR Data for Safe Physical Human-Robot Interaction
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910469192679424 |
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| author | Arora, Sarthak Subramanian, Karthik Adamides, Odysseus Sahin, Ferat |
| author_facet | Arora, Sarthak Subramanian, Karthik Adamides, Odysseus Sahin, Ferat |
| contents | This paper explores the use of 3D lidar in a physical Human-Robot Interaction (pHRI) scenario. To achieve the aforementioned, experiments were conducted to mimic a modern shop-floor environment. Data was collected from a pool of seventeen participants while performing pre-determined tasks in a shared workspace with the robot. To demonstrate an end-to-end case; a perception pipeline was developed that leverages reflectivity, signal, near-infrared, and point-cloud data from a 3-D lidar. This data is then used to perform safety based control whilst satisfying the speed and separation monitoring (SSM) criteria. In order to support the perception pipeline, a state-of-the-art object detection network was leveraged and fine-tuned by transfer learning. An analysis is provided along with results of the perception and the safety based controller. Additionally, this system is compared with the previous work. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_00869 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Using 3-D LiDAR Data for Safe Physical Human-Robot Interaction Arora, Sarthak Subramanian, Karthik Adamides, Odysseus Sahin, Ferat Robotics This paper explores the use of 3D lidar in a physical Human-Robot Interaction (pHRI) scenario. To achieve the aforementioned, experiments were conducted to mimic a modern shop-floor environment. Data was collected from a pool of seventeen participants while performing pre-determined tasks in a shared workspace with the robot. To demonstrate an end-to-end case; a perception pipeline was developed that leverages reflectivity, signal, near-infrared, and point-cloud data from a 3-D lidar. This data is then used to perform safety based control whilst satisfying the speed and separation monitoring (SSM) criteria. In order to support the perception pipeline, a state-of-the-art object detection network was leveraged and fine-tuned by transfer learning. An analysis is provided along with results of the perception and the safety based controller. Additionally, this system is compared with the previous work. |
| title | Using 3-D LiDAR Data for Safe Physical Human-Robot Interaction |
| topic | Robotics |
| url | https://arxiv.org/abs/2406.00869 |