论文标题
在不使用力传感器的情况下对四足机器人的冲击强度估计
Impact Intensity Estimation of a Quadruped Robot without Using a Force Sensor
论文作者
论文摘要
估计影响强度是腿部机器人的重要任务之一。撞击的准确反馈可能会支持机器人计划合适而有效的轨迹以适应未知的复杂地形。通常，此任务是由机器人脚上的力传感器执行的。在这封信中，提出了不使用力传感器的冲击强度估计。人工神经网络模型旨在预测腿部瞬时位置的腿部扭矩，而无需使用复杂的运动运动模型。在轨迹期间，使用无气味的卡尔曼过滤器，以平滑和稳定测量。根据预测信息和过滤值之间的差异，估计机器人脚影响的状态和强度估计。进行四足机器人的模拟和实验以验证所提出的方法的有效性。
Estimating the impact intensity is one of the significant tasks of the legged robot. Accurate feedback of the impact may support the robot to plan a suitable and efficient trajectory to adapt to unknown complex terrains. Ordinarily, this task is performed by a force sensor in the robot's foot. In this letter, an impact intensity estimation without using a force sensor is proposed. An artificial neural network model is designed to predict the motor torques of the legs in an instantaneous position in the trajectory without utilizing the complex kinematic and dynamic models of motion. An unscented Kalman filter is used during the trajectory to smooth and stabilize the measurement. Based on the difference between the predicted information and the filtered value, the state and intensity of the robot foot's impact with the obstacles are estimated. The simulation and experiment on a quadruped robot are carried out to verify the effectiveness of the proposed method.
