TY - GEN
T1 - Multimodal path planning using potential field for human–robot interaction
AU - Kawasaki, Yosuke
AU - Yorozu, Ayanori
AU - Takahashi, Masaki
N1 - Funding Information:
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.
Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Human–robot interaction
KW - Multimodal path planning
KW - Potential field
UR - http://www.scopus.com/inward/record.url?scp=85059884916&partnerID=8YFLogxK
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U2 - 10.1007/978-3-030-01370-7_47
DO - 10.1007/978-3-030-01370-7_47
M3 - Conference contribution
AN - SCOPUS:85059884916
SN - 9783030013691
T3 - Advances in Intelligent Systems and Computing
SP - 597
EP - 609
BT - Intelligent Autonomous Systems 15 - Proceedings of the 15th International Conference IAS-15
A2 - Dillmann, Rüdiger
A2 - Menegatti, Emanuele
A2 - Ghidoni, Stefano
A2 - Strand, Marcus
PB - Springer Verlag
T2 - 15th International Conference on Intelligent Autonomous Systems, IAS 2018
Y2 - 11 June 2018 through 15 June 2018
ER -