References & Citations
Computer Science > Robotics
Title: Off-Road Autonomy Validation Using Scalable Digital Twin Simulations Within High-Performance Computing Clusters
(Submitted on 8 May 2024 (v1), last revised 5 Jun 2024 (this version, v2))
Abstract: 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.
Submission history
From: Tanmay Samak [view email][v1] Wed, 8 May 2024 01:14:24 GMT (41655kb,D)
[v2] Wed, 5 Jun 2024 11:57:44 GMT (41655kb,D)
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