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Bing Z, Rohregger A, Walter F, Huang Y, Lucas P, Morin FO, Huang K, Knoll A. Lateral flexion of a compliant spine improves motor performance in a bioinspired mouse robot. Sci Robot 2023; 8:eadg7165. [PMID: 38055804 DOI: 10.1126/scirobotics.adg7165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 11/07/2023] [Indexed: 12/08/2023]
Abstract
A flexible spine is critical to the motion capability of most animals and plays a pivotal role in their agility. Although state-of-the-art legged robots have already achieved very dynamic and agile movement solely relying on their legs, they still exhibit the type of stiff movement that compromises movement efficiency. The integration of a flexible spine thus appears to be a promising approach to improve their agility, especially for small and underactuated quadruped robots that are underpowered because of size limitations. Here, we show that the lateral flexion of a compliant spine can promote both walking speed and maneuver agility for a neurorobotic mouse (NeRmo). We present NeRmo as a biomimetic robotic mouse that mimics the morphology of biological mice and their muscle-tendon actuation system. First, by leveraging the lateral flexion of the compliant spine, NeRmo can greatly increase its static stability in an initially unstable configuration by adjusting its posture. Second, the lateral flexion of the spine can also effectively extend the stride length of a gait and therefore improve the walking speeds of NeRmo. Finally, NeRmo shows agile maneuvers that require both a small turning radius and fast walking speed with the help of the spine. These results advance our understanding of spine-based quadruped locomotion skills and highlight promising design concepts to develop more agile legged robots.
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Affiliation(s)
- Zhenshan Bing
- Chair of Robotics, Artificial Intelligence and Real-Time Systems, School of Computation, Information and Technology, Technical University of Munich, Boltzmannstrasse 3, 85748 Munich, Germany
| | - Alex Rohregger
- Chair of Robotics, Artificial Intelligence and Real-Time Systems, School of Computation, Information and Technology, Technical University of Munich, Boltzmannstrasse 3, 85748 Munich, Germany
| | - Florian Walter
- Chair of Robotics, Artificial Intelligence and Real-Time Systems, School of Computation, Information and Technology, Technical University of Munich, Boltzmannstrasse 3, 85748 Munich, Germany
- Machine Intelligence Lab, Department Engineering, University of Technology Nuremberg, Ulmenstrasse 52i, 90443 Nuremberg, Germany
| | - Yuhong Huang
- Chair of Robotics, Artificial Intelligence and Real-Time Systems, School of Computation, Information and Technology, Technical University of Munich, Boltzmannstrasse 3, 85748 Munich, Germany
| | - Peer Lucas
- Chair of Robotics, Artificial Intelligence and Real-Time Systems, School of Computation, Information and Technology, Technical University of Munich, Boltzmannstrasse 3, 85748 Munich, Germany
| | - Fabrice O Morin
- Chair of Robotics, Artificial Intelligence and Real-Time Systems, School of Computation, Information and Technology, Technical University of Munich, Boltzmannstrasse 3, 85748 Munich, Germany
| | - Kai Huang
- School of Computer Science and Engineering, Sun Yat-sen University, 510330 Guangzhou, China
- Pazhou Lab, 510335 Guangzhou, China
| | - Alois Knoll
- Chair of Robotics, Artificial Intelligence and Real-Time Systems, School of Computation, Information and Technology, Technical University of Munich, Boltzmannstrasse 3, 85748 Munich, Germany
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