{'fraction_non_alphanumeric': 0.016722408026755852, 'fraction_numerical': 0.0, 'mean_word_length': 5.032258064516129, '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}}
Grasping strategy for moving object using Net-Structure Proximity Sensor and vision sensor

This study presents a robot-hand-arm system with high robustness and responsiveness by using a “Net-Structure Proximity Sensor.” The sensor, which we have developed and specially designed for a robot hand, directly detects an object being to be grasped and outputs analog voltage signals according to the position/posture error between the robot hand and the object. It has been confirmed that the robot hand is able to quickly adjust to and grasp an unknown object by applying a feed-back control method based on the sensor signals. This paper focuses on the integration of the proximity-based feedback control to a commonly-used vision-based control. These sensors work in complementary manner: a vision sensor is available for planning an approaching path of a robot hand by detecting large area, and a Net-Structure Proximity Sensor enables the robot hand to adjust the approaching error before grasping and to improve the certainty of the grasping. Two objective velocities are derived independently by the sensors. By adding the velocities with considering the reliability of the sensor information, the robot hand becomes to be able to perform approaching and adjustment to the target object simultaneously. Experimental results showed that the robot hand grasped a moving object with high success rate even in conditions where it was difficult to predict the trajectory of the object accurately.