Robotic Arm Trajectory Planning in Dynamic Environments Based on Self-Optimizing Replay Mechanism

Robotic Arm Trajectory Planning in Dynamic Environments Based on Self-Optimizing Replay Mechanism

In complex dynamic environments, robotic arms face multiple challenges such as real-time environmental changes, high-dimensional state spaces, and strong uncertainties. Trajectory planning tasks based on deep reinforcement learning (DRL) suffer from difficulties in acquiring human expert strategies,...

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Bibliographic Details
Main Authors: Pengyao Xu, Chong Di, Jiandong Lv, Peng Zhao, Chao Chen, Ruotong Wang
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Sensors
Subjects:
experience replay mechanism
reward function design
dynamic environment
robotic manipulation
Online Access:https://www.mdpi.com/1424-8220/25/15/4681
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