{'fraction_non_alphanumeric': 0.01827956989247312, 'fraction_numerical': 0.0, 'mean_word_length': 5.602836879432624, '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}}
Interacting multiple model-based human motion prediction for motion planning of companion robots

Motion planning of human-companion robots is a challenging problem and its solution has numerous applications. This paper proposes an autonomous motion planning framework for human-companion robots to accompany humans in a socially desirable manner, which takes into account the safety and comfort requirements. An Interacting Multiple Model-Unscented Kalman Filter (IMM-UKF) estimation and prediction approach is developed to estimate human motion states from sensor data and predict human position and speed for a finite horizon. Based on the predicted human states, the robot motion planning is formulated as a model predictive control (MPC) problem. Simulations have demonstrated the superior performance of the IMM-UKF approach and the effectiveness of the MPC planner in facilitating the socially desirable companion behavior.