DEEC, room: I(-1)05

Date and time: Thursday, October 27, at 3 pm

Title: Applied cryptography and cybersecurity in autonomous vehicles

Petter Solnør, Norwegian University of Science and Technology

Abstract: With cloud-computing technology, we can outsource computations in feedback control to third-party providers, increasing scalability and enabling feedback control as a service. However, by hosting our systems on third-party infrastructure, we also reveal potentially confidential information which limits the practical use of such systems. We can solve this problem by using a cryptographic concept called homomorphic encryption to design encrypted control systems that only operate on encrypted data. The goal of this presentation is to describe how we can use homomorphic encryption to design encrypted feedback control systems. We cover the basics of homomorphic encryption before describing general design principles and limitations of encrypted control. We proceed by showing examples of an encrypted control system and an encrypted guidance system for an unmanned surface vehicle. Finally, we show a third application where we fuse encrypted state estimates and describe how we can use such an application to, for example, design a collaborative air defense surveillance system.

Title: Autonomous docking of unmanned surface vehicles aided by visual and acoustic measurements

Øystein Volden, Norwegian University of Science and Technology

Abstract: Docking is a safety-critical operation for autonomous surface vehicles and requires highly accurate navigation signals. Since Global Navigation Satellite Systems (GNSS) can be unreliable and inaccurate in urban environments, other sensors should be considered for increased redundancy and reliability. To this end, we present a low-cost visual-inertial navigation system that we can use for automatic docking of small vehicles. The proposed system produces state estimates of the vehicle, including position, velocity, and attitude, based on raw image and inertial data. To simplify the navigation task, we use easily identifiable tags as a reference on the dockside. When the vehicle approaches the dock, a visual fiducial system recognizes the tags and estimates the relative pose between the camera onboard the vehicle and the tags at the dockside. The camera-tag pose and inertial data are then fused using an error-state Kalman filter for robust state estimation of the vehicle. Since the visual system is constrained by distance to the dockside and the camera’s field of view, we also employ a doppler velocity log (DVL) to obtain low-drift velocity aiding. As such, the DVL can be used to minimize drift for short-endurance missions until a landmark is detected and used by the visual system.