Off-Road Autonomy Validation Using Scalable Digital Twin Simulations Within High-Performance Computing Clusters
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866914982242811904 |
|---|---|
| author | Samak, Tanmay Vilas Samak, Chinmay Vilas Binz, Joey Smereka, Jonathon Brudnak, Mark Gorsich, David Luo, Feng Krovi, Venkat |
| author_facet | Samak, Tanmay Vilas Samak, Chinmay Vilas Binz, Joey Smereka, Jonathon Brudnak, Mark Gorsich, David Luo, Feng Krovi, Venkat |
| contents | Off-road autonomy validation presents unique challenges due to the unpredictable and dynamic nature of off-road environments. Traditional methods focusing on sequentially sweeping across the parameter space for variability analysis struggle to comprehensively assess the performance and safety of off-road autonomous systems within the imposed time constraints. This paper proposes leveraging scalable digital twin simulations within high-performance computing (HPC) clusters to address this challenge. By harnessing the computational power of HPC clusters, our approach aims to provide a scalable and efficient means to validate off-road autonomy algorithms, enabling rapid iteration and testing of autonomy algorithms under various conditions. We demonstrate the effectiveness of our framework through performance evaluations of the HPC cluster in terms of simulation parallelization and present the systematic variability analysis of a candidate off-road autonomy algorithm to identify potential vulnerabilities in the autonomy stack's perception, planning and control modules. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_04743 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Off-Road Autonomy Validation Using Scalable Digital Twin Simulations Within High-Performance Computing Clusters Samak, Tanmay Vilas Samak, Chinmay Vilas Binz, Joey Smereka, Jonathon Brudnak, Mark Gorsich, David Luo, Feng Krovi, Venkat Robotics Distributed, Parallel, and Cluster Computing Off-road autonomy validation presents unique challenges due to the unpredictable and dynamic nature of off-road environments. Traditional methods focusing on sequentially sweeping across the parameter space for variability analysis struggle to comprehensively assess the performance and safety of off-road autonomous systems within the imposed time constraints. This paper proposes leveraging scalable digital twin simulations within high-performance computing (HPC) clusters to address this challenge. By harnessing the computational power of HPC clusters, our approach aims to provide a scalable and efficient means to validate off-road autonomy algorithms, enabling rapid iteration and testing of autonomy algorithms under various conditions. We demonstrate the effectiveness of our framework through performance evaluations of the HPC cluster in terms of simulation parallelization and present the systematic variability analysis of a candidate off-road autonomy algorithm to identify potential vulnerabilities in the autonomy stack's perception, planning and control modules. |
| title | Off-Road Autonomy Validation Using Scalable Digital Twin Simulations Within High-Performance Computing Clusters |
| topic | Robotics Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2405.04743 |