面向伴随型机器人同步运动的递进式步态时相检测算法

Progressive gait phase detection algorithm targeting to synchronous motion of companion robots

在面向伴随型机器人的研究中,步态时相检测是保持人机同步运动的关键。然而,提高检测精度需要收集和分析更多步态时相信息,这导致检测延时冗长,无法满足实时性需求。针对此问题提出一种面向伴随型机器人同步运动的递进式步态时相检测算法,主要依托惯性测量单元和贝叶斯信息准则构建概率生成模型的物理层和决策层,对步态时相进行初步的快速检测;当检测达不到判决阈值时,在决策层中引入记忆网络,预测下一段时间的步态时相参数,从而为概率生成模型提供更多的决策信息,并依据多次判决结果递进地完成步态时相精准的增量检测。实验结果表明:算法的步态时相检测准确率达97.8%;决策时间为28.3 ms,相较于自适应贝叶斯算法降低了约30%。

In the research of companion robots, gait phase detection is the key to maintaining man-machine synchronous motion. However, improving detection accuracy requires collecting and analyzing more gait phase information, which results in a long detection delay and is unable to meet the real-time requirements. In this paper, a progressive gait phase detection algorithm targeting to synchronous motion of companion robots is proposed. The algorithm mainly constructs the physical layer and decision layer of the probabilistic generative model based on the inertial measurement unit and Bayesian information criterion, and performs preliminary rapid gait phase detection;when the detection fails to reach the decision threshold, a memory network is introduced in the decision layer to predict the gait phase parameters for next period of time, thereby provide more decision information for the probabilistic generative model, and progressively complete the accurate incremental detection of the gait phase based on multiple decision results. The experiment results show that the proposed gait phase detection algorithm achieves an accuracy of 97.8%;the decision time is 28.3 ms, which is about 30% reduction compared with the adaptive Bayesian algorithm.