摘要
在工业机器人离线编程中,复杂环境带来了路径规划的重大挑战。文章提出了一种基于深度学习的离线路径优化方法。首先,构建了包含坐标系设定与马尔可夫决策过程的机器人运动学模型,为路径规划提供理论基础。其次,详细介绍了深度强化学习优化算法,包括算法原理、价值函数估计及经验回放与优先采样等关键技术。在特定栅格地图场景下的仿真实验结果表明,该方法在路径长度、稳定性及收敛速度方面均优于传统算法(如A^(*)和Dijkstra),验证了其在复杂环境下的有效性,但仍存在进一步优化的空间。
In offline programming of industrial robots,complex environments pose significant challenges for path planning.The article proposes an offline path optimization method based on deep learning.Firstly,a robot kinematic model was constructed that includes coordinate system setting and Markov decision process,providing a theoretical basis for path planning.Subsequently,a detailed introduction was given to deep reinforcement learning optimization algorithms,including algorithm principles,value function estimation,and key technologies such as experience replay and priority sampling.The simulation results in specific grid map scenarios show that this method outperforms traditional algorithms such as A^(*)and Dijkstra in terms of path length,stability,and convergence speed,verifying its effectiveness in complex environments.However,there is still room for further optimization.
作者
谢乐华
XIE Lehua(Yulin Municipal Political Consultative Conference Information Center,Yulin,Guangxi 537000,China)
关键词
工业机器人
离线编程
深度学习
路径优化
强化学习
industrial robots
offline programming
deep learning
path optimization
reinforcement learning
作者简介
谢乐华(1983-),本科,工程师,研究方向:计算机技术。
