Research on Motion Control Technology of Bionic Quadruped Robot

DOI

10.2991/978-94-6463-823-3_46How to use a DOI?

Keywords

Motion Control; Bionic Quadruped Robot; Neural Network

Abstract

This paper investigates motion control technologies for bionic quadruped robots through biological inspiration. By analyzing three core methodologies - reflex control, Central Pattern Generators (CPGs), and neural network control - the study evaluates their effectiveness in improving robotic movement efficiency and environmental adaptability. Experimental results demonstrate that neural network control enhances movement speed by 15% on complex terrains through dynamic gait adjustments, while the Evolutionary Neural Network (EvoNN) algorithm increases motion stability by 18%. Additionally, virtual spring-damper models and multi-sensor fusion techniques show 30% improvement in impact resistance and 92% success rate in path planning respectively. However, Challenges remain, such as high computational demands of EvoNN and material limitations hindering lightweight designs. Additionally, multi-gait transitions lack full fluidity, particularly in fast-paced obstacle courses. The paper proposes future directions including piezoelectric material applications, lightweight neural network development, and distributed control architectures. A case study of China's H-shaped piezoelectric robot (55g weight, 6.679cm/s speed) highlights cost-effective solutions. This research contributes to transitioning robotic control from rigid biomimicry to adaptive bio-transcendence, offering insights for both engineering applications and biological locomotion studies.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Rui Chen
PY  - 2025
DA  - 2025/08/31
TI  - Research on Motion Control Technology of Bionic Quadruped Robot
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 468
EP  - 475
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_46
DO  - 10.2991/978-94-6463-823-3_46
ID  - Chen2025
ER  -