基于 DDPG 算法强化学习的四足机器人能耗最小化研究

IF 2.2 3区 工程技术 Q2 ENGINEERING, MECHANICAL
Actuators Pub Date : 2024-01-02 DOI:10.3390/act13010018
Zhenzhuo Yan, Hongwei Ji, Qing Chang
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引用次数: 0

摘要

能耗是决定四足机器人运动性能的最关键因素之一。然而,现有的研究方法在快速有效地降低与四足机器人运动相关的能耗方面经常遇到挑战。本文采用深度确定性策略梯度(DDPG)算法来优化赛博狗四足机器人的能耗。首先,建立了机器人的运动学模型和能耗模型。其次,使用 DDPG 算法通过强化学习优化能耗。然后,在模拟实验中将优化后的足底轨迹与两种常见的足底轨迹进行比较,这两种轨迹的周期和同步次数相同,但速度不同。最后,使用原型机进行了实际实验,以验证模拟数据。分析结果表明,在相同条件下,与现有的最优轨迹方法相比,所提出的方法可减少 7% 至 9% 的能耗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Energy Consumption Minimization of Quadruped Robot Based on Reinforcement Learning of DDPG Algorithm
Energy consumption is one of the most critical factors in determining the kinematic performance of quadruped robots. However, existing research methods often encounter challenges in quickly and efficiently reducing the energy consumption associated with quadrupedal robotic locomotion. In this paper, the deep deterministic policy gradient (DDPG) algorithm was used to optimize the energy consumption of the Cyber Dog quadruped robot. Firstly, the kinematic and energy consumption models of the robot were established. Secondly, energy consumption was optimized by reinforcement learning using the DDPG algorithm. The optimized plantar trajectory was then compared with two common plantar trajectories in simulation experiments, with the same period and the number of synchronizations but varying velocities. Lastly, real experiments were conducted using a prototype machine to validate the simulation data. The analysis results show that, under the same conditions, the proposed method can reduce energy consumption by 7~9% compared with the existing optimal trajectory methods.
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来源期刊
Actuators
Actuators Mathematics-Control and Optimization
CiteScore
3.90
自引率
15.40%
发文量
315
审稿时长
11 weeks
期刊介绍: Actuators (ISSN 2076-0825; CODEN: ACTUC3) is an international open access journal on the science and technology of actuators and control systems published quarterly online by MDPI.
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