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基于改进的DDPG算法的机器人操纵器的轨迹规划

Dehai Yu¹, Weiwei Sun¹, Zhuangzhuang Luan¹

¹Institute of Automation, Qufu Normal University, Qufu, 273165, China.

ISA transactions

|March 12, 2026

概括

本研究介绍了一种改进的深度决定性政策梯度 (DDPG) 算法,用于机器人操纵器轨迹规划. 增强的DDPG算法实现了更快的学习融合和更高的成功率在时间优化规划任务.

科学领域:

机器人技术机器人技术机器人技术
人工智能的人工智能
机器学习机器学习

背景情况:

深度强化学习 (DRL) 越来越多地用于机器人操纵器规划.
传统的DRL方法在工业轨迹规划中面临着缓慢融合和低成功率的挑战.

研究的目的:

为时间最佳的机器人操纵器轨迹规划提出改进的深度决定性政策梯度 (DDPG) 算法.
与传统的DRL方法相比,提高学习融合速度和成功率.

主要方法:

在参数训练过程中集成辐射基函数神经网络用于非线性函数近似.
利用梯度下降算法用于神经网络重量更新.
实施SumTree样本库,以有效选择高质量的样本.

主要成果:

改进的DDPG算法显示了机器人操纵器关节扭矩和角度的稳定变化.
对轨迹规划任务的算法利用率进行了显著的改进.
与传统的DDPG算法相比,提高了学习效率.

结论:

提议的改进的DDPG算法有效地解决了机器人操纵器轨迹规划中的传统DRL的局限性.
该方法为工业机器人技术的时间最佳路径规划提供了更有效和更成功的策略.

关键词:

改进了深度决定性政策梯度 (DDPG).辐射基础函数神经网络神经网络机器人操纵器是一个机器人操纵器.在SumTree的样本池中,SumTree的样本池中.轨道规划轨道规划轨道规划

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