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...

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Bibliographic Details
Published in:Frontiers in robotics and AI Vol. 9; p. 852270
Main Authors: Zhou, Jianfeng, Nguyen, Quan, Kamath, Sanjana, Hacohen, Yaneev, Zhu, Chunchu, Fu, Michael J, Daltorio, Kathryn A
Format: Journal Article
Language:English
Published: Switzerland Frontiers Media S.A 14-04-2022
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Summary: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.
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Pierluigi Arpenti, University of Naples Federico II, Italy
This article was submitted to Human-Robot Interaction, a section of the journal Frontiers in Robotics and AI
Edited by: Fanny Ficuciello, University of Naples Federico II, Italy
Reviewed by: Weiwei Wan, Osaka University, Japan
ISSN:2296-9144
2296-9144
DOI:10.3389/frobt.2022.852270