A Survey of Deep Reinforcement Learning for Robotic Arm Trajectory Planning

Le résumé fourni par la source

With the continuous development of intelligent manufacturing and industrial automation, the accuracy of robotic arm trajectory planning has become increasingly crucial. This accuracy is decisive for performing complex tasks, achieving dynamic obstacle avoidance, and enhancing the safety of human-machine collaboration. However, traditional methods face obstacles when confronted with the complexity brought about by high-dimensional nonlinear dynamics and dynamic environmental disturbances. Based on a systematic review of the traditional trajectory planning paradigms, this paper analyzes how deep reinforcement learning simulates the nonlinear spatiotemporal complexity of robotic arm motion in a breakthrough way. The research discusses the situation where DRL relies on end-to-end optimization to integrate spatial joint coupling relationships and time series dependencies, constructing a powerful decision-making framework for intelligent mechanical systems. The discussion concludes with challenges such as sample efficiency bottlenecks, the stability of long-term planning under sparse rewards, and the interpretability of multimodal optimization. At the same time, for physical prior fusion, And the future development directions such as lightweight algorithm design have clarified the innovative approaches.

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