However, the extensive operational space of robotic arms makes it challenging to quickly obtain successful samples, causing DRL algorithms to expend significant time on ineffective exploration and resulting in slow convergence rates. To mitigate this issue, researchers have proposed leveraging \"expert guidance\" to accelerate the convergence of DRL algorithms. [PERSON] et al. [26] developed a strategy for axis-hole assembly problems guided by impedance control algorithms assisted by the DDPG for the correction of trajectories. [PERSON] et al. [27] used a deep neural network controller based on a Long Short-Term Memory (LSTM) network to generate trajectories. At the same time, a mixture density network (MDN) was used to calculate the error signal to adapt to multimodal demonstration data. Through imitation learning, the robotic arms were able to reasonably plan paths to complete the picking and placing of objects. [PERSON] et al. [28] designed a comprehensive reward function that combines dynamic obstacle avoidance and target proximity, which was integrated with the priority experience replay (PER) approach to improve the utilization of samples, finally allowing for the realization of real-time path-planning. [PERSON] et al. [29] integrated the APF and DDPG algorithms, using approximate poses derived from the APF algorithm as expert guidance. They implemented a phased approach to obstacle-avoidance trajectory planning, where the DDPG algorithm dominated training during the obstacle-avoidance phase and the approximate poses from APF guided DDPG training during the target-planning phase. Additionally, they proposed a guiding mechanism to connect the control outputs of the two phases, thereby efficiently training a policy model tailored to address the research problem at hand. However, trajectory-planning approaches for robotic arms based on DRL methods still encounter challenges in achieving convergence and conducting efficient explorations, particularly within unstructured work environments that feature both dynamic and static obstacles. The core issue lies in managing a substantial amount of failed explorations and sparse rewards, which result in training failure and make it difficult to learn effective strategies. In order to solve the problem in which robot arms face difficulties in avoiding obstacles and reaching the target point in environments characterized by dynamic obstacles, we propose a new path-planning method based on reinforcement learning. Through designing a dynamic exploration guidance mechanism and an adaptive obstacle avoidance reward function, we optimize the robotic arm's performance in terms of path-planning. The overall environmental structure is illustrated in Figure 1. During the training process, the policy network receives the state vector as input and outputs the selected action; the critic network receives the state vector and action as input and outputs a value function that evaluates the expected reward or value of selecting the action in the state. During the testing process, the state vector obtained from the simulation environment is input into the policy network to obtain the selected action. This study implements a path planning strategy that ensures that the robotic arm remains stable during learning, and evaluates the effectiveness of this strategy in environments featuring dynamic obstacles.