Improved DQN-Based Intelligent Trajectory Control for Coal Gangue Sorting Robotic Manipulators

In the context of intelligent motion control for a coal gangue picking robot's manipulator based on reinforcement learning, we develop an optimal neural network control model to govern the robot's arm motion trajectory through interaction with the environment. However, this model exhibits significant randomness, which results in limited training effectiveness and low control efficiency. To address these issues, this paper proposes an improvsd Deep Q-Network (IM-DQN) approach for intelligent control of the robot’s arm motion. A prioritized experience replay mechanism is employed in the construction of the motion control data experience pool. By utilizing an optimal experience retention strategy and exploration suppression rate, the approach effectively prevents the rapid expansion of the experience pool and avoids network collapse. Furthermore, a dual neural network architecture is introduced, where motion control data and experience pool data are processed separately, aligning the learning process with the reward structure. A Markov Decision Process is applied to establish the motion control model for the coal gangue picking robot’s manipulator, converting the robotic arm path planning problem into a Markov decision problem. Additionally, an Intrinsic Curiosity Module (ICM) is incorporated into the model to enhance motion control, thereby improving convergence speed, control accuracy, and overall performance. Comparative experimental analysis demonstrates that the proposed method outperforms traditional reinforcement learning approaches by preventing network collapse, ensuring control stability, generating shorter and more accurate motion paths for the robotic arm, and improving trajectory accuracy by 17.6% and reducing average path length by 26.2% versus DQN baselines.