Work-in-Progress: Enabling Edge-based Self-Navigation in Earthquake-Struck Zones
2020 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS)
The role of unmanned vehicles for searching and localizing the victims in disaster impacted areas such as earthquake-struck zones is getting more important. Self-navigation on an earthquake zone has a unique challenge of detecting irregularly shaped obstacles such as cracks, puddles, and debris on the streets. In this paper, we present an edge-based self-navigation vehicle that can detect unique obstacles in earthquake-struck sites and discuss the performance and energy impact of various neural network structures, edge platforms, and optimizations. To enable vehicles to safely navigate earthquake-struck sites, we compiled a new image database of various earthquake impacted regions and developed semantic segmentation models that identify obstacles unique to earthquake-sites. The models are tested on an edge-based car platform. To our best knowledge, this is the first study that identifies unique challenges and discusses the performance and energy impact of edge-based self-navigation vehicles for earthquake-struck zones.
autonomous navigation, convolutional neural network, edge computing, semantic segmentation
Ryan Zelek, Vignesh K. Venkateshwar, Sai K. Duggineni, Renu Dighe, and Hyeran Jeon. "Work-in-Progress: Enabling Edge-based Self-Navigation in Earthquake-Struck Zones" 2020 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS) (2020): 37-39. https://doi.org/10.1109/CODESISSS51650.2020.9244030