In such operation environments with long communication time delay as the moon or planets, it is difficult to compose a closed loop control between a master and a slave system. A new scheme is required to achieve the stability for tele-operation. This paper discusses and evaluates the proposed tele-operational driving system based on human machine cooperation that consists of global and local path-planning for long range traversability. The operator can command any desired path as a sequence of waypoints by using a 3D terrain model measured as DEM (Digital Elevation Model) by the on-board sensors of a rover. The data are transmitted to the ground and evaluated to obtain a dangerousness map. To cope with an unknown obstacle, a conventional autonomous path-planning algorithm is applied to the interval between waypoints. In addition, a rover continuously updates its knowledge about the environment. By continuously recalculating the difference between the original terrain data used for initial path generation on the ground and the most recent data acquired by the rover, waypoints compensation can be achieved. Therefore, a rover has to compensate waypoints by using the latest measurement data which would be more reliable than the previous data, for corresponding to the difference automatically. Here, the difference is compensated by using a distortion compensation matrix which is the mapping between the old and new terrain data sets. This paper shows the simulation and experimental results and also its evaluation results by using the rover test-bed.
Path compensation and hierarchical autonomous functions for long range exploration rover
