Research

In this research, we proposed an active management method to tele-monitor and tele-operate more autonomous vehicles (AVs) with few observers by adjusting the movement of the AVs actively. A management system is created to get the status from the AVs and separate the monitoring requirement to the observers optimally. When the requirements might be intensive, the management system can also adjust the movements of the AVs actively to distribute the monitoring time, which can make the observers monitor more vehicles.

• M. Ding, E. Takeuchi, Y. Ishiguro, Y. Ninomiya, N. Kawaguchi, and K. Takeda, “How to monitor multiple autonomous vehicles remotely with few observers: An active management method,” in The 2021 IEEE Intelligent Vehicles Symposium (IV), July 2021, pp. 1168–1173.

In this research, we propose a novel method to prevent the time-delay when controlling a walking assist exoskeleton by predicting the future plantar force and walking status. By using Long Short-Term Memory and a fully-connected network, the plantar force can be predicted using only data measured by inertial measurement unit sensors, not only during the walking period but also at the start and end of walking. From the predicted plantar force, the walking status and the desired assistance timing can also be determined. By considering the time-delay and sending the control commands beforehand, the exoskeleton can be moved precisely on the desired assistance timing.

• M. Ding, M. Nagashima, S.-G. Cho, J. Takamatsu, and T. Ogasawara, “Control of walking assist exoskeleton with time-delay based on the prediction of plantar force,” IEEE Access, vol. 8, pp. 138642–138651, 2020.