Liu Hongyan, Zhang Hanwen, Lee Junghee, Xu Peilong, Shin Incheol, Park Jongchul
Department of Marine Convergence Design Engineering, Pukyong National University, 45, Yongso-ro, Nam-Gu, Busan 48513, Republic of Korea.
Department of Artificial Intelligence Convergence, Pukyong National University, 45, Yongso-ro, Nam-Gu, Busan 48513, Republic of Korea.
Biomimetics (Basel). 2024 Mar 1;9(3):150. doi: 10.3390/biomimetics9030150.
The current motion interaction model has the problems of insufficient motion fidelity and lack of self-adaptation to complex environments. To address this problem, this study proposed to construct a human motion control model based on the muscle force model and stage particle swarm, and based on this, this study utilized the deep deterministic gradient strategy algorithm to construct a motion interaction control model based on the muscle force model and the deep reinforcement strategy. Empirical analysis of the human motion control model proposed in this study revealed that the joint trajectory correlation and muscle activity correlation of the model were higher than those of other comparative models, and its joint trajectory correlation was up to 0.90, and its muscle activity correlation was up to 0.84. In addition, this study validated the effectiveness of the motion interaction control model using the depth reinforcement strategy and found that in the mixed-obstacle environment, the model's desired results were obtained by training 1.1 × 10 times, and the walking distance was 423 m, which was better than other models. In summary, the proposed motor interaction control model using the muscle force model and deep reinforcement strategy has higher motion fidelity and can realize autonomous decision making and adaptive control in the face of complex environments. It can provide a theoretical reference for improving the effect of motion control and realizing intelligent motion interaction.
当前的运动交互模型存在运动逼真度不足以及对复杂环境缺乏自适应能力的问题。为解决这一问题，本研究提出构建基于肌肉力模型和阶段粒子群的人体运动控制模型，并在此基础上，利用深度确定性梯度策略算法构建基于肌肉力模型和深度强化策略的运动交互控制模型。对本研究提出的人体运动控制模型进行实证分析表明，该模型的关节轨迹相关性和肌肉活动相关性高于其他对比模型，其关节轨迹相关性高达0.90，肌肉活动相关性高达0.84。此外，本研究使用深度强化策略验证了运动交互控制模型的有效性，发现在混合障碍物环境中，该模型通过训练1.1×10次获得了期望的结果，行走距离为423米，优于其他模型。综上所述，所提出的使用肌肉力模型和深度强化策略的运动交互控制模型具有更高的运动逼真度，能够在面对复杂环境时实现自主决策和自适应控制。它可为提高运动控制效果和实现智能运动交互提供理论参考。
