{'fraction_non_alphanumeric': 0.01343408900083963, 'fraction_numerical': 0.0, 'mean_word_length': 5.5136612021857925, '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}}
Robust Feedback Motion Primitives for Exploration of Unknown Terrains

Unknown properties of a robot’s environment are one of the sources of uncertainty in autonomous navigation. This uncertainty has to be accounted for when modelling robot dynamics. For ground vehicles in particular, terrain structure is one of the main environmental factors that can strongly influence the dynamics. Therefore, to ensure the ability of a robot to safely and efficiently navigate new environments, robust motion planning and control systems are needed. This paper investigates a data-driven approach to planning and control based on construction of robust motion primitives (MPs) and corresponding feedback rules that ensure a bounded error along the planned trajectory. The approach is tested in an exploration scenario in which a robot systematically inspects an area consisting of several terrain types with the aim of recognizing changes in dynamical properties, learning new dynamics models when such changes are detected and recording that information for future use. The advantage of incorporating the collected data into motion planning in multi-terrain environments is illustrated via simulation.