El-Hussieny Haitham
Department of Mechatronics and Robotics Engineering, Egypt-Japan University of Science and Technology, E-JUST, Alexandria, 21934, Egypt.
Sci Rep. 2024 Jul 15;14(1):16243. doi: 10.1038/s41598-024-66104-y.
Our research utilized deep learning to enhance the control of a 3 Degrees of Freedom biped robot leg. We created a dynamic model based on a detailed joint angles and actuator torques dataset. This model was then integrated into a Model Predictive Control (MPC) framework, allowing for precise trajectory tracking without the need for traditional analytical dynamic models. By incorporating specific constraints within the MPC, we met operational and safety standards. The experimental results demonstrate the effectiveness of deep learning models in improving robotic control, leading to precise trajectory tracking and suggesting potential for further integration of deep learning into robotic system control. This approach not only outperforms traditional control methods in accuracy and efficiency but also opens the way for new research in robotics, highlighting the potential of utilizing deep learning models in predictive control techniques.
我们的研究利用深度学习来增强对一个三自由度双足机器人腿部的控制。我们基于一个详细的关节角度和执行器扭矩数据集创建了一个动态模型。然后将该模型集成到模型预测控制（MPC）框架中，无需传统的解析动态模型即可实现精确的轨迹跟踪。通过在MPC中纳入特定约束，我们满足了操作和安全标准。实验结果证明了深度学习模型在改进机器人控制方面的有效性，实现了精确的轨迹跟踪，并表明了将深度学习进一步集成到机器人系统控制中的潜力。这种方法不仅在准确性和效率方面优于传统控制方法，还为机器人技术的新研究开辟了道路，突出了在预测控制技术中利用深度学习模型的潜力。
