Safe Human Robot Navigation in Warehouse Scenario
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866908288598147072 |
|---|---|
| author | Farrell, Seth Li, Chenghao Yu, Hongzhan Yoshimitsu, Ryo Gao, Sicun Christensen, Henrik I. |
| author_facet | Farrell, Seth Li, Chenghao Yu, Hongzhan Yoshimitsu, Ryo Gao, Sicun Christensen, Henrik I. |
| contents | The integration of autonomous mobile robots (AMRs) in industrial environments, particularly warehouses, has revolutionized logistics and operational efficiency. However, ensuring the safety of human workers in dynamic, shared spaces remains a critical challenge. This work proposes a novel methodology that leverages control barrier functions (CBFs) to enhance safety in warehouse navigation. By integrating learning-based CBFs with the Open Robotics Middleware Framework (OpenRMF), the system achieves adaptive and safety-enhanced controls in multi-robot, multi-agent scenarios. Experiments conducted using various robot platforms demonstrate the efficacy of the proposed approach in avoiding static and dynamic obstacles, including human pedestrians. Our experiments evaluate different scenarios in which the number of robots, robot platforms, speed, and number of obstacles are varied, from which we achieve promising performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_21141 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Safe Human Robot Navigation in Warehouse Scenario Farrell, Seth Li, Chenghao Yu, Hongzhan Yoshimitsu, Ryo Gao, Sicun Christensen, Henrik I. Robotics Machine Learning Multiagent Systems The integration of autonomous mobile robots (AMRs) in industrial environments, particularly warehouses, has revolutionized logistics and operational efficiency. However, ensuring the safety of human workers in dynamic, shared spaces remains a critical challenge. This work proposes a novel methodology that leverages control barrier functions (CBFs) to enhance safety in warehouse navigation. By integrating learning-based CBFs with the Open Robotics Middleware Framework (OpenRMF), the system achieves adaptive and safety-enhanced controls in multi-robot, multi-agent scenarios. Experiments conducted using various robot platforms demonstrate the efficacy of the proposed approach in avoiding static and dynamic obstacles, including human pedestrians. Our experiments evaluate different scenarios in which the number of robots, robot platforms, speed, and number of obstacles are varied, from which we achieve promising performance. |
| title | Safe Human Robot Navigation in Warehouse Scenario |
| topic | Robotics Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2503.21141 |