She Jinbo, Feng Xiang, Xu Bao, Chen Linyang, Wang Yuan, Liu Ning, Zou Wenpeng, Ma Guoliang, Yu Bin, Ba Kaixian
School of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, China.
The State Key Laboratory of Crane Technology, Yanshan University, Qinhuangdao 066004, China.
Biomimetics (Basel). 2025 Jun 14;10(6):403. doi: 10.3390/biomimetics10060403.
Hydraulic legged robots, with advantages such as high load capacity and power density, have become a strategic driving force in advancing intelligent mobile platform technologies. However, their high energy consumption significantly limits long-duration endurance and efficient operational performance. In this paper, inspired by the excellent autonomous energy-efficient consciousness of mammals endowed by natural evolution, a bionic energy-efficient inverse kinematics method based on neural networks (EIKNN) is proposed for the energy-efficient motion planning of hydraulic legged robots with redundant degrees of freedom (RDOFs). Firstly, the dynamic programming (DP) algorithm is used to solve the optimal joint configuration with minimum energy loss as the goal, and the training data set is generated. Subsequently, the inverse kinematic model of the leg with minimum energy loss is learned based on neural network (NN) simulation of the autonomous energy-efficient consciousness endowed to mammals by natural evolution. Finally, extensive comparative experiments validate the effectiveness and superiority of the proposed method. This method not only significantly reduces energy dissipation in hydraulic legged robots but also lays a crucial foundation for advancing hydraulic legged robot technology toward high efficiency, environmental sustainability, and long-term developmental viability.
液压腿式机器人具有高负载能力和功率密度等优点，已成为推动智能移动平台技术发展的战略驱动力。然而，其高能耗显著限制了长时间续航能力和高效运行性能。本文受自然进化赋予哺乳动物的卓越自主节能意识启发，针对具有冗余自由度（RDOF）的液压腿式机器人的节能运动规划，提出了一种基于神经网络的仿生节能逆运动学方法（EIKNN）。首先，使用动态规划（DP）算法以最小能量损失为目标求解最优关节配置，并生成训练数据集。随后，基于对自然进化赋予哺乳动物的自主节能意识的神经网络（NN）模拟，学习能量损失最小的腿部逆运动学模型。最后，大量对比实验验证了所提方法的有效性和优越性。该方法不仅显著降低了液压腿式机器人的能量耗散，还为推动液压腿式机器人技术朝着高效、环境可持续性和长期发展可行性方向发展奠定了关键基础。
