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Hands to Hexapods, Wearable User Interface Design for Specifying Leg Placement for Legged Robots
Specifying leg placement is a key element for legged robot control, however current methods for specifying individual leg motions with human-robot interfaces require mental concentration and the use of both arm muscles. In this paper, a new control interface is discussed to specify leg placement for...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9048026/ https://www.ncbi.nlm.nih.gov/pubmed/35494545 http://dx.doi.org/10.3389/frobt.2022.852270 |
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author | Zhou, Jianfeng Nguyen, Quan Kamath, Sanjana Hacohen, Yaneev Zhu, Chunchu Fu, Michael J. Daltorio, Kathryn A. |
author_facet | Zhou, Jianfeng Nguyen, Quan Kamath, Sanjana Hacohen, Yaneev Zhu, Chunchu Fu, Michael J. Daltorio, Kathryn A. |
author_sort | Zhou, Jianfeng |
collection | PubMed |
description | Specifying leg placement is a key element for legged robot control, however current methods for specifying individual leg motions with human-robot interfaces require mental concentration and the use of both arm muscles. In this paper, a new control interface is discussed to specify leg placement for hexapod robot by using finger motions. Two mapping methods are proposed and tested with lab staff, Joint Angle Mapping (JAM) and Tip Position Mapping (TPM). The TPM method was shown to be more efficient. Then a manual controlled gait based on TPM is compared with fixed gait and camera-based autonomous gait in a Webots simulation to test the obstacle avoidance performance on 2D terrain. Number of Contacts (NOC) for each gait are recorded during the tests. The results show that both the camera-based autonomous gait and the TPM are effective methods in adjusting step size to avoid obstacles. In high obstacle density environments, TPM reduces the number of contacts to 25% of the fixed gaits, which is even better than some of the autonomous gaits with longer step size. This shows that TPM has potential in environments and situations where autonomous footfall planning fails or is unavailable. In future work, this approach can be improved by combining with haptic feedback, additional degrees of freedom and artificial intelligence. |
format | Online Article Text |
id | pubmed-9048026 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-90480262022-04-29 Hands to Hexapods, Wearable User Interface Design for Specifying Leg Placement for Legged Robots Zhou, Jianfeng Nguyen, Quan Kamath, Sanjana Hacohen, Yaneev Zhu, Chunchu Fu, Michael J. Daltorio, Kathryn A. Front Robot AI Robotics and AI Specifying leg placement is a key element for legged robot control, however current methods for specifying individual leg motions with human-robot interfaces require mental concentration and the use of both arm muscles. In this paper, a new control interface is discussed to specify leg placement for hexapod robot by using finger motions. Two mapping methods are proposed and tested with lab staff, Joint Angle Mapping (JAM) and Tip Position Mapping (TPM). The TPM method was shown to be more efficient. Then a manual controlled gait based on TPM is compared with fixed gait and camera-based autonomous gait in a Webots simulation to test the obstacle avoidance performance on 2D terrain. Number of Contacts (NOC) for each gait are recorded during the tests. The results show that both the camera-based autonomous gait and the TPM are effective methods in adjusting step size to avoid obstacles. In high obstacle density environments, TPM reduces the number of contacts to 25% of the fixed gaits, which is even better than some of the autonomous gaits with longer step size. This shows that TPM has potential in environments and situations where autonomous footfall planning fails or is unavailable. In future work, this approach can be improved by combining with haptic feedback, additional degrees of freedom and artificial intelligence. Frontiers Media S.A. 2022-04-14 /pmc/articles/PMC9048026/ /pubmed/35494545 http://dx.doi.org/10.3389/frobt.2022.852270 Text en Copyright © 2022 Zhou, Nguyen, Kamath, Hacohen, Zhu, Fu and Daltorio. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Robotics and AI Zhou, Jianfeng Nguyen, Quan Kamath, Sanjana Hacohen, Yaneev Zhu, Chunchu Fu, Michael J. Daltorio, Kathryn A. Hands to Hexapods, Wearable User Interface Design for Specifying Leg Placement for Legged Robots |
title | Hands to Hexapods, Wearable User Interface Design for Specifying Leg Placement for Legged Robots |
title_full | Hands to Hexapods, Wearable User Interface Design for Specifying Leg Placement for Legged Robots |
title_fullStr | Hands to Hexapods, Wearable User Interface Design for Specifying Leg Placement for Legged Robots |
title_full_unstemmed | Hands to Hexapods, Wearable User Interface Design for Specifying Leg Placement for Legged Robots |
title_short | Hands to Hexapods, Wearable User Interface Design for Specifying Leg Placement for Legged Robots |
title_sort | hands to hexapods, wearable user interface design for specifying leg placement for legged robots |
topic | Robotics and AI |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9048026/ https://www.ncbi.nlm.nih.gov/pubmed/35494545 http://dx.doi.org/10.3389/frobt.2022.852270 |
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