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1.杭州申昊科技股份有限公司,杭州 311100
2.中国电子科技集团公司第五十二研究所,杭州 310061
花聪聪,男,1990年生,河南焦作人,硕士研究生;主要研究方向为智能机器人系统;hcongcong9010@163.com。
收稿日期:2024-04-20,
修回日期:2024-07-01,
纸质出版日期:2025-09-15
移动端阅览
花聪聪,杨子赫,夏燕挺,等. 四足机器人跳跃功能的研究现状[J]. 机械传动,2025,49(9):162-174.
HUA Congcong,YANG Zihe,XIA Yanting,et al. Research status on jumping function of quadruped robots[J]. Journal of Mechanical Transmission,2025,49(9):162-174.
花聪聪,杨子赫,夏燕挺,等. 四足机器人跳跃功能的研究现状[J]. 机械传动,2025,49(9):162-174. DOI: 10.16578/j.issn.1004.2539.2025.09.020.
HUA Congcong,YANG Zihe,XIA Yanting,et al. Research status on jumping function of quadruped robots[J]. Journal of Mechanical Transmission,2025,49(9):162-174. DOI: 10.16578/j.issn.1004.2539.2025.09.020.
意义
2
四足机器人的跳跃功能是实现机器人在复杂环境中的高机动性和适应性的重要能力。【分析】综述了四足机器人跳跃功能的研究现状,包括结构设计、控制模型设计和控制算法设计3个方面的最新进展。在基于跳跃功能的结构设计方面,通过优化四足机器人的骨架结构、关节设计和材料选择,来提高跳跃能力和稳定性。在基于跳跃功能的控制模型设计方面,提出多种动力学模型来描述四足机器人的跳跃行为,这些模型可用于预测和优化跳跃性能,并为后续控制算法设计奠定基础。准确的控制模型可以保证稳定和精确地控制,从而提高机器人跳跃性能和适应性。在基于跳跃功能的控制算法设计方面,传统的控制方法,如PID控制、模糊控制和自适应控制等,被广泛应用于跳跃功能的控制;此外,先进的控制方法,如强化学习、神经网络和遗传算法等,也被探索应用。这些方法能够提高跳跃性能、稳定性和适应性,使机器人能够在动态和复杂环境中实现高效跳跃。
Significance
2
The jumping function of quadruped robots is an important ability to achieve high maneuverability and adaptability in complex environments. [Analysis] The current research status of the jumping function of quadruped robots was reviewed
including the latest progress in the structural design
the control model design
and the control algorithm design. In terms of the structural design based on the jumping function
the skeleton structure
joint design
and material selection of quadruped robots were optimized to improve the jumping ability and the stability. In the design of control models based on the jumping function
various dynamic models were proposed to describe the jumping behavior of quadruped robots. These models can be used to predict and optimize the jumping performance
and provide a foundation for the subsequent control algorithm design. An accurate control model can ensure the stable and precise control
thereby improving the jumping performance and adaptability of robots. In the design of control algorithms based on the jumping function
traditional control methods such as PID control
fuzzy control
and adaptive control were widely used in the control of jumping function. In addition
advanced control methods such as reinforcement learning
neural networks
and genetic algorithms also were explored and applied. These methods can improve the jumping performance
the stability
and the adaptability
enabling robots to achieve efficient jumping in dynamic and complex environments.
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