Real-time prediction of fall and collision of tracked vehicle for remote-control support

This thesis describes a new method that in real time predicts fall and collision in order to support remote control of a tracked vehicle with sub-tracks. A tracked vehicle has high ability of getting over rough terrain. However, it is difficult for an operator at a remote place to control the vehicle's moving direction and speed. Hence, we propose a new path evaluation system based on the measurement of environmental shapes around the vehicle. In this system, the candidate paths are generated by operator inputs and terrain information. For evaluating the traversability of the path, we estimate the pose of the robot on the path and contact points with the ground. Then, the combination of translational and rotational velocity is chosen.