This paper investigates the vulnerability of bilat-eral teleoperation systems to perfectly undetectable False Data Injection Attacks (FDIAs). Teleoperation, one of the major applications in robotics, involves a leader manipulator operated by a human and a follower manipulator at a remote site, connected via a communication channel. While this setup en-ables operation in challenging environments, it also introduces cybersecurity risks, particularly in the communication link. The paper focuses on a specific class of cyberattacks: perfectly un-detectable FDIAs, where attackers alter signals without leaving detectable traces at all. Compared to previous research on linear and first-order nonlinear systems, this paper examines bilateral teleoperation systems with second-order nonlinear manipulator dynamics. The paper derives mathematical conditions based on Lie Group theory that enable such attacks, demonstrating how an attacker can modify the follower manipulator's motion while the operator perceives normal operation through the leader device. This vulnerability challenges conventional detection methods based on observable changes and highlights the need for advanced security measures in teleoperation systems. To validate the theoretical results, the paper presents experimental demonstrations using a teleoperation system connecting robots in the US and Japan.

A self-driving system for an electric wheelchair must estimate its position, identify the travelable area, and determine the travel route. Indoors, self-location estimation can be performed by wireless LAN or beacon. However, for self-driving, more accurate position estimation is required. In previous research on self-driving of electric wheelchairs, there are methods using LIDAR (Light Detection and Ranging) and laser range scanner. However, the conventional research has a problem that the system configuration is complicated and the power consumption also increases. In this study, we developed position estimation by deep learning using smartphone images with a positioning accuracy that allowed self-driving at low cost. Also, when the smartphone image recognition does not work, such as when the wheelchair approaches a wall, position estimation by augmented reality (AR) markers was used. In addition, a Bluetooth low-energy beacon was used to approximate the position when no marker could be seen. The route from an initial position to a destination was calculated using map information stored within the system. Experiments confirmed that the proposed system allows self-driving of an electric wheelchair along the route from its initial position to a destination. In this prototype system, we used the GPU board to calculate deep learning, but in the future we plan to concentrate the system on smart phones by calculating with the AI chip embedded in the smartphone.