  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.
