Abstract
In a human–robot interaction, a robot must move to a position where the robot can obtain precise information of people, such as positions, postures, and voice. This is because the accuracy of human recognition depends on the positional relation between the person and robot. In addition, the robot should choose what sensor data needs to be focused on during the task that involves the interaction. Therefore, we should change a path approaching the people to improve human recognition accuracy for ease of performing the task. Accordingly, we need to design a path-planning method considering sensor characteristics, human recognition accuracy, and the task contents simultaneously. Although some previous studies proposed path-planning methods considering sensor characteristics, they did not consider the task and the human recognition accuracy, which was important for practical application. Consequently, we present a path-planning method considering the multimodal information which fusion the task contents and the human recognition accuracy simultaneously.
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Acknowledgment
This study was supported by “A Framework PRINTEPS to Develop Practical Artificial Intelligence” of the Core Research for Evolutional Science and Technology (CREST) of the Japan Science and Technology Agency (JST) under Grant Number JPMJCR14E3.
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Kawasaki, Y., Yorozu, A., Takahashi, M. (2019). Multimodal Path Planning Using Potential Field for Human–Robot Interaction. In: Strand, M., Dillmann, R., Menegatti, E., Ghidoni, S. (eds) Intelligent Autonomous Systems 15. IAS 2018. Advances in Intelligent Systems and Computing, vol 867. Springer, Cham. https://doi.org/10.1007/978-3-030-01370-7_47
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DOI: https://doi.org/10.1007/978-3-030-01370-7_47
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