基于机器学习与视觉的四足机器人步态精确监测方法

为保证四足机器人在复杂地形中能准确行进,对其步态实时监测方法进行了研究,提出一种基于机器学习和双目视觉的步态精确监测方法。首先为降低视觉残影误差,从已知步态轨迹“动中取静”确定对足端的最佳观测点位。此外,为补偿视觉系统误差所致足端位姿测量误差,提出一种基于深度神经网络的足端位姿精确预测方法。最后仿真结果表明所设计神经网络有99.68%的概率能达到0.025 mm足端位置预测精度,可满足实时、高精度监测要求。此方法将机器学习的泛化能力与视觉系统复杂误差来源相结合,使视觉方法实现了高精度测量,为足式机器人步态实时精确监测提供了新思路,进而为其足端精确定位及步态周期性保持提供了有益的方法参考。

Accurate Gait Monitoring Method for Quadruped Robot Based on Machine Learning and Vision

In order to ensure the accuracy of motion in complex terrains for quadruped robots, a real-time gait monitoring method based on machine learning and binocular vision was proposed. The residual visual error was addressed by identifying the optimal observation point for the foot end along the known gait trajectory, which is referred to as "capturing stillness amid motion." To compensate for measurement errors in foot position and posture due to inaccuracies in the vision system, an accurate prediction method based on a depth neural network was utilized. The results indicate that the designed neural network achieves a 99.68% probability of attaining 0.025 mm foot position prediction accuracy, thus fulfilling the requirements for real-time and high-precision monitoring. It is concluded that this approach combines the generalization capabilities of machine learning with the complex error sources of the vision system to achieve high-precision measurement. Consequently, it provides a novel perspective for real-time and accurate gait monitoring of foot robots and serves as a valuable reference for foot end positioning and periodic gait maintenance.