Hands to Hexapods, Wearable User Interface Design for Specifying Leg Placement for Legged Robots

Jianfeng Zhou; Quan Nguyen; Sanjana Kamath; Yaneev Hacohen; Chunchu Zhu; Michael J Fu; Kathryn A Daltorio

doi:10.3389/frobt.2022.852270

Hands to Hexapods, Wearable User Interface Design for Specifying Leg Placement for Legged Robots

Front Robot AI. 2022 Apr 14:9:852270. doi: 10.3389/frobt.2022.852270. eCollection 2022.

Authors

Jianfeng Zhou¹, Quan Nguyen², Sanjana Kamath², Yaneev Hacohen², Chunchu Zhu¹, Michael J Fu^{2

3

4

5

6}, Kathryn A Daltorio^{1

2}

Affiliations

¹ Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, OH, United States.
² Department of Electrical, Computer, and Systems Engineering, Case Western Reserve University, Cleveland, OH, United States.
³ Department of Physical Medicine and Rehabilitation at the MetroHealth System, Cleveland, OH, United States.
⁴ Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH, United States.
⁵ Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States.
⁶ Cleveland FES Center, Louis Stokes Cleveland Department of Veterans Affairs Medical Center, Cleveland, OH, United States.

Abstract

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.

Keywords: data glove; human-robot interaction; integrated planning and control; legged robots; obstacle avoidance; teleoperation.