As small-form factor drones grow more intelligent, they increasingly require more sophisticated capabilities to record sensor data and system state, ensuring safe and improved operation. Already regulations for black boxes, electronic data recorders (EDRs), for determining liabilities and improving the safety of large-form factor autonomous vehicles are becoming established. Conventional techniques use hardened memory storage units that conserve all sensor (visual) and system operational state; and N-way redundant models for detecting uncertainty in system operation. For small-form factor drones, which are highly limited by weight, power, and computational resources, these techniques become increasingly prohibitive. In this paper, we propose a safety architecture for resource constrained autonomous vehicles that enables the development of safer and more efficient nano-drone systems. The insight for the proposed safety architecture is that the regular structure of data-driven models used to control drones can be exploited to efficiently compress and identify key events that should be conserved in the EDR subsystem. We describe an implementation of the architecture, including hardware and software support, and quantify the benefits of the approach. We show that the proposed techniques can increase the amount of recorded flight time by over 10x and reduce energy usage by over 10x for high-resolution systems.
| Identifer | oai:union.ndltd.org:CALPOLY/oai:digitalcommons.calpoly.edu:theses-4120 |
| Date | 01 June 2022 |
| Creators | Sexton, Connor J |
| Publisher | DigitalCommons@CalPoly |
| Source Sets | California Polytechnic State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Master's Theses |
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