{'fraction_non_alphanumeric': 0.016728624535315983, 'fraction_numerical': 0.0006195786864931846, 'mean_word_length': 5.117424242424242, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Human Machine Cooperative Tele-Drive by Path Compensation for Long Range Traversability

In operation environment with communication time delay, it's difficult to compose a closed loop control structure between a master and a slave system, and some scheme to achieve the stability is thus necessary. In this paper, we discuss and evaluate our proposed human machine cooperative tele-drive system 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 by the on-board sensor of a rover. The data is 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 between each waypoint. In addition, a rover is continuously updating 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, a waypoint compensation can be achieved. Therefore, we have to compensate waypoints by using the latest measurement data which can be assumed more reliable than previous data, for corresponding to the difference automatically. Here, the difference is assumed as the distortion between each data set, and compensated by using a distortion compensation matrix which is the mapping between the old and new terrain data sets. Simulation and experimental results and its evaluations by using the rover test-bed are mentioned in the paper