Accurate landing detection is crucial for humanoid robots performing high dynamic motions.Unlike common methods that rely on redundant force-torque sensors and low-precision observers to estimate landing states,this p...Accurate landing detection is crucial for humanoid robots performing high dynamic motions.Unlike common methods that rely on redundant force-torque sensors and low-precision observers to estimate landing states,this paper proposes a novel landing detection method characterized by high precision and low noise,synthesizing a learning-based Improved Momentum Observer(IMO-Net)for the ankles’external torque estimation with a Gated Recurrent Unit(GRU)-based network for state judgment.Since the movement and external torque of the ankle undergo drastic changes during high dynamic motions,achieving accurate and real-time estimation presents a challenge.To address this problem,IMO-Net employs a new Improved Momentum Observer(IMO),which does not depend on acceleration data derived from second-order differentials or friction model,and significantly reduces noise effects from sensors data and robot foot wobble.Furthermore,an Elman network is utilized to accurately calculate the ankle output torque(IMO input),significantly reducing the estimation error.Finally,leveraging IMO-Net and extensive experimental data,we developed and optimized a GRU-based landing detection network through comprehensive ablation experiments.This refined network reliably determines the robot’s landing states in real-time.The effectiveness of our methods has been validated through experiments.展开更多
基金supported in part by the Beijing Natural Science Foundation under Grant L243004in part by the National Natural Science Foundation of China under Grant 62073041in part by the“111”Project under Grant B08043.
文摘Accurate landing detection is crucial for humanoid robots performing high dynamic motions.Unlike common methods that rely on redundant force-torque sensors and low-precision observers to estimate landing states,this paper proposes a novel landing detection method characterized by high precision and low noise,synthesizing a learning-based Improved Momentum Observer(IMO-Net)for the ankles’external torque estimation with a Gated Recurrent Unit(GRU)-based network for state judgment.Since the movement and external torque of the ankle undergo drastic changes during high dynamic motions,achieving accurate and real-time estimation presents a challenge.To address this problem,IMO-Net employs a new Improved Momentum Observer(IMO),which does not depend on acceleration data derived from second-order differentials or friction model,and significantly reduces noise effects from sensors data and robot foot wobble.Furthermore,an Elman network is utilized to accurately calculate the ankle output torque(IMO input),significantly reducing the estimation error.Finally,leveraging IMO-Net and extensive experimental data,we developed and optimized a GRU-based landing detection network through comprehensive ablation experiments.This refined network reliably determines the robot’s landing states in real-time.The effectiveness of our methods has been validated through experiments.
