When multiple objects are positioned close together or stacked, pre-grasp operations such as pushing objects can be used to create space for the grasp, thereby improving the grasping success rate. This study develops a model based on a deep Q-learning network architecture and introduces a fully convolutional network to accurately identify pixels in the workspace image that correspond to target locations for exploration. In addition, this study incorporates image masking to limit the exploration area of the robotic arm, ensuring that the agent consistently explores regions containing objects. This approach effectively addresses the sparse reward problem and improves the convergence rate of the model. Experimental results from both simulated and real-world environments show that the proposed method accelerates the learning of effective grasping strategies. When image masking is applied, the success rate in the grasping task reaches 80% after 600 iterations. The time required to reach 80% success rate is 25% shorter when image masking is used compared to when it is not used. The main finding of this study is the direct integration of image masking technique with a deep reinforcement learning (DRL) algorithm, which offers significant advancement in robotic arm control. Furthermore, this study shows that image masking technique can substantially reduce training time and improve the object grasping success rate. This innovation enables the robotic arm to better adapt to scenarios that conventional DRL methods cannot handle, thereby improving training efficiency and performance in complex and dynamic industrial applications.

For heart disease patients with an implanted artificial pacemaker, monitoring the operational status of the pacemaker is a crucial and challenging issue. This study aims to design a transcutaneous optic telemetry (TOT) system that transmits light signals from a human body through the skin to a receiving circuit and carries pacemaker status information via the light signals to achieve transcutaneous telemetry. Petri net method is used to analyze the simulated pacemaker. Petri nets are constructed to illustrate the process of the pacemaker. Furthermore, the Petri nets for the process with four checkpoints (CPs) deployed on the pacemaker are also constructed. The TOT system uses near-infrared signals generated by a light-emitting diode as a transmission medium, and the pork skin animal model is used to validate the TOT system’s feasibility and efficiency. The correct sequence of CPs determines the normal state of the pacemaker, and the information from these CPs is transmitted in vitro for interpretation through the TOT system. This study designed and implemented the TOT system. Experiments showed that the TOT system’s design is effective in transmitting the CPs information through the pigskin layer without any significant interference. The results proved that the TOT system can transmit CPs information outside the body to achieve the transcutaneous telemetry diagnosis of the pacemaker. This study provides an alternative means to surgical operation for implanted pacemakers examination.