Yue Min, Jiang Xiaoyun, Zhang Liqiang, Zhang Yujin
School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China.
School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China.
Biomimetics (Basel). 2024 Nov 29;9(12):729. doi: 10.3390/biomimetics9120729.
To tackle the challenges of poor stability during real-time random gait switching and precise trajectory control for hexapod robots under limited stride and steering conditions, a novel real-time replanning gait switching control strategy based on an omnidirectional gait and fuzzy inference is proposed, along with an attitude control method based on the single-neuron adaptive proportional-integral-derivative (PID). To start, a kinematic model of a hexapod robot was developed through the Denavit-Hartenberg (D-H) kinematics analysis, linking joint movement parameters to the end foot's endpoint pose, which formed the foundation for designing various gaits, including omnidirectional and compound gaits. Incorporating an omnidirectional gait could effectively resolve the challenge of precise trajectory control for the hexapod robot under limited stride and steering conditions. Next, a real-time replanning gait switching strategy based on an omnidirectional gait and fuzzy inference was introduced to tackle the issue of significant impacts and low stability encountered during gait transitions. Finally, in view of further enhancing the stability of the hexapod robot, an attitude adjustment algorithm based on the single-neuron adaptive PID was presented. Extensive experiments confirmed the effectiveness of this approach. The results show that our approach enabled the robot to switch gaits seamlessly in real time, effectively addressing the challenge of precise trajectory control under limited stride and steering conditions; moreover, it significantly improved the hexapod robot's dynamic stability during its motion, enabling it to adapt to complex and changing environments.
为应对六足机器人在实时随机步态切换过程中稳定性差以及在有限步幅和转向条件下精确轨迹控制的挑战，提出了一种基于全方位步态和模糊推理的新型实时重规划步态切换控制策略，以及一种基于单神经元自适应比例积分微分（PID）的姿态控制方法。首先，通过Denavit-Hartenberg（D-H）运动学分析建立了六足机器人的运动学模型，将关节运动参数与末端足部的端点姿态联系起来，这为设计包括全方位步态和复合步态在内的各种步态奠定了基础。引入全方位步态可以有效解决六足机器人在有限步幅和转向条件下精确轨迹控制的挑战。其次，引入了一种基于全方位步态和模糊推理的实时重规划步态切换策略，以解决步态转换过程中遇到的冲击大、稳定性低的问题。最后，为了进一步提高六足机器人的稳定性，提出了一种基于单神经元自适应PID的姿态调整算法。大量实验证实了该方法的有效性。结果表明，我们的方法使机器人能够实时无缝切换步态，有效解决了有限步幅和转向条件下精确轨迹控制的挑战；此外，它显著提高了六足机器人运动过程中的动态稳定性，使其能够适应复杂多变的环境。
