Secure Encoded Instruction Graphs for End-to-End Data Validation in Autonomous Robots
This addresses security deficiencies in autonomous robots, particularly for ensuring correct sensor and actuator operation without external feedback, though it appears incremental as it builds on existing landmark-based navigation methods.
The paper tackles the problem of securing autonomous robot operations by introducing an encoding method and end-to-end validation framework for mission instructions, demonstrating its effectiveness with simulated and real robots using encoded maps that incorporate cryptographic hashes for validation.
As autonomous robots are becoming more widespread, more attention is being paid to the security of robotic operation. Autonomous robots can be seen as cyber-physical systems: they can operate in virtual, physical, and human realms. Therefore, securing the operations of autonomous robots requires not only securing their data (e.g., sensor inputs and mission instructions) but securing their interactions with their environment. There is currently a deficiency of methods that would allow robots to securely ensure their sensors and actuators are operating correctly without external feedback. This paper introduces an encoding method and end-to-end validation framework for the missions of autonomous robots. In particular, we present a proof of concept of a map encoding method, which allows robots to navigate realistic environments and validate operational instructions with almost zero {\it a priori} knowledge. We demonstrate our framework using two different encoded maps in experiments with simulated and real robots. Our encoded maps have the same advantages as typical landmark-based navigation, but with the added benefit of cryptographic hashes that enable end-to-end information validation. Our method is applicable to any aspect of robotic operation in which there is a predefined set of actions or instructions given to the robot.