Autonomous Ground Vehicle Roll-Over Avoidance Control

Subject: Autonomous Unmanned Aerial Vehicle Swarms: Optimal Self-Organization and Control for Search and Hunt Missions
Labs: Cooperative Autonomous Systems (CASY) Lab
Faculties: Faculty of Aerospace Engineering
  Faculty of Civil and Environmental Engineering
Researchers: Associate Professor Tal Shima and Professor Itzhak Shmulevich

Abstract:

Tripped roll-over is a major cause of truck accidents: when the wheel of a truck hits the
pavement stone lining, or conversely, when a truck tries to get back on a road and hits the elevated asphalt rim, roll-over may occur. Untripped roll-over accidents with SUVs driven off-road with inexperienced drivers is also a serious problem. Warning and prevention of untripped roll-over accidents can save lives. The proposed research will investigate and develop more reliable roll-over indicators. An extended active safety Medlinger roll-over avoidance algorithms will be tested for the first time. The extensions will include new kinds and combinations of sensors and actuators improving roll-over avoidance control performance and stability. A novel approach, using combined quantitative feedback theory (QFT) and model predictive control (MPC) will be investigated to cope with a wider spectrum of roll-over scenarios. The algorithms will be tested first in simulation and then on an actual autonomous unmanned ground vehicle (UGV) driven in the cooperative autonomous systems (CASY) lab.