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摘要: 针对助行机器人的柔顺性和安全性问题，基于多传感器系统融合技术，本文提出了一种能够兼具柔顺与安全的助行机器人运动控制方法.首先介绍了助行机器人的机械结构、控制原理以及多传感器系统，然后根据机器人多传感器系统，设计出各传感器相对应的用户意图估计方法，提出了一种基于多传感器融合的助行机器人柔顺运动控制算法.分析用户可能发生的跌倒模式，使用基于卡尔曼滤波（Kalman filter，KF）的序贯概率比检验（Sequential probability ratio test，SPRT）方法和决策函数来判断用户是否会跌倒，并判断处于哪种跌倒模式.最后，通过助行机器人柔顺运动控制实验和用户跌倒检测实验验证了算法的有效性.Abstract: Aimed at compliance and safety problems in motion control of walking-aid robot, a multi-sensor fusion based walking-aid robot motion control method with both compliance and safety is proposed. Firstly, the mechanism, control theory and multi-sensor system of the walking-aid robot are introduced. Then according to multi-sensor system, user motion intention estimation methods for each sensor are designed and a multi-sensor based compliance motion control for walking-aid robot is proposed. After analyzing user's possible falling modes, a Kalman filter (KF) based sequential probability ratio test (SPRT) method and decision function are used to detect the fall and falling mode. Finally, several compliance motion control experiments and fall detection experiments are described to show validity of the proposed algorithm.
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Key words:
- Walking-aid robot /
- fall detection /
- force sensor /
- laser range finder /
- compliance
1) 本文责任编委 程龙 -
图 2 机器人坐标系系统和交互力的测量
Fig. 2 Robot coordinate system (top view) and measurement of interaction forces
图 7 3个志愿者在向前跌倒时的意图速度分布图
Fig. 7 Distribution of intent velocities of three subjects during falling to forward
图 8 3个志愿者在向左跌倒时的意图速度分布图
Fig. 8 Distribution of intent velocities of three subjects during falling to left
图 9 3个志愿者在向右跌倒时的意图速度分布图
Fig. 9 Distribution of intent velocities of three subjects during falling to right
图 10 兼具柔顺与安全的助行机器人运动控制系统框图
Fig. 10 The system block diagram of the walking-aid robot motion control which has compliance and safety
图 11 基于多传感器融合的用户意图速度实验1
Fig. 11 Multi-sensors based human intent velocities Experiment 1
图 12 基于多传感器融合的用户意图速度实验2
Fig. 12 Multi-sensors based human intent velocities Experiment 2
图 15 志愿者1向前跌倒仿真实验结果图
Fig. 15 Subject 1 fall detection experiment results of falling to forward
图 16 志愿者1向左跌倒仿真实验结果图
Fig. 16 Subject 1 fall detection experiment results of falling to left
图 17 志愿者1向右跌倒仿真实验结果图
Fig. 17 Subject 1 fall detection experiment results of falling to right
图 21 基于穿戴式传感器的助行机器人用户跌倒检测实验(向前跌倒)
Fig. 21 Wearable sensor based user fall detection experiment of walking-aid robot (fall forward)
图 22 基于穿戴式传感器的助行机器人用户跌倒检测实验(向左跌倒)
Fig. 22 Wearable sensor based user fall detection experiment of walking-aid robot (fall to left)
表 1 3个志愿者在不同跌倒模式下的平均意图速度(cm/s)
Table 1 The intent velocities of three subjects in different falling modes (cm/s)
$\overline{{}{^h}{\dot{X}}_{H}}$ $\overline{{}{^h}{\dot{Y}}_{H}}$ $\overline{{}{^h}{\dot{Z}}_{H}}$ $\overline{{}{^h}{\dot{X}}_{L}}$ $\overline{{}{^h}{\dot{Y}}_{L}}$ A 向前 16 -0.031 0 -11.22 -2.40 向左 0 16 0 0.27 -7.82 向右 0 -16 0 -3.97 6.10 B 向前 16 0.051 0 -11.87 -2.09 向左 0.02 16 0 0.60 -5.83 向右 0 -16 0 -2.02 6.70 C 向前 16 -0.025 0 -13.95 -4.14 向左 0 16 0 -1.27 -8.32 向右 0 -16 0 -2.65 6.13 
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