In this study, we present an innovative approach to robotic deburring path planning by combining deep reinforcement learning (DRL) with an inverse Jacobian strategy. Existing model-based path planning methods, including sampling-based approaches, often suffer from computational complexity and challenges in capturing the dynamics of deburring systems. To overcome these limitations, our novel DRL-based framework for path planning leverages experiential learning to identify optimal deburring trajectories without relying on predefined models. This model-free approach is particularly suited for complex deburring scenarios with unknown system dynamics. Additionally, we employ an inverse Jacobian technique with a time-varying gain module (η(t) = e^2t) during training, which yields remarkable benefits in terms of exploration–exploitation balance and collision avoidance, enhancing the overall performance of the DRL agent. Through a series of experiments conducted in a simulated environment, we evaluate the efficacy of our proposed algorithm for deburring path planning. Our modified DRL-based approach, utilizing inverse kinematics with a time-varying gain module, demonstrates superior performance in terms of convergence speed, optimality, and robustness when compared to conventional path planning methods. Notably, in comparison to algorithms like sampling-based strategies, our model-free DRL-based approach outperforms these methods, achieving an exceptional average success rate of 97%. The integration of the inverse Jacobian technique further enhances the effectiveness of our algorithm by effectively reducing the state space dimensionality, leading to improved learning efficiency and the generation of optimal deburring trajectories.

在这项研究中，我们通过将深度强化学习（DRL）与逆雅可比策略相结合，提出了一种创新的机器人去毛刺路径规划方法。现有的基于模型的路径规划方法，包括基于采样的方法，常常面临计算复杂性和捕获去毛刺系统动态的挑战。为了克服这些限制，我们基于 DRL 的新型路径规划框架利用体验式学习来识别最佳去毛刺轨迹，而无需依赖预定义的模型。这种无模型方法特别适合系统动力学未知的复杂去毛刺场景。此外，我们在训练过程中采用了具有时变增益模块（η(t) = e^2t）的逆雅可比技术，这在探索-利用平衡和避免碰撞方面产生了显着的好处，从而增强了DRL的整体性能代理人。通过在模拟环境中进行的一系列实验，我们评估了所提出的去毛刺路径规划算法的有效性。我们改进的基于 DRL 的方法利用具有时变增益模块的逆运动学，与传统的路径规划方法相比，在收敛速度、最优性和鲁棒性方面表现出卓越的性能。值得注意的是，与基于采样的策略等算法相比，我们的基于 DRL 的无模型方法优于这些方法，实现了 97% 的出色平均成功率。逆雅可比技术的集成通过有效降低状态空间维数进一步增强了算法的有效性，从而提高了学习效率并生成了最佳去毛刺轨迹。