To address the common issues in traditional robotic arm path planning methods, which include significant discrepancies between simulation and reality, low search efficiency, and limitations in path reliability and executability, a digital twin-based robotic arm path planning method is proposed. Firstly, a digital twin platform is constructed according to the actual operating environment of an industrial robotic arm, enabling bidirectional virtual-real mapping and real-time data interaction between the physical robotic arm and its digital twin model, thus providing a real-time and accurate digital twin simulation platform for the simulation verification and practical execution of robotic arm path planning algorithms. Secondly, at the path planning algorithm level, an adaptive gradient-based bidirectional rapidly-exploring random tree* (AG-BI-RRT*) algorithm is proposed. The algorithm incorporates an adaptive conical sampling method based on historical gradient feedback, three expansion strategies (target bias expansion, improved artificial potential field expansion, and random direction expansion), and a multi-factor parent node reselection strategy. These improvements comprehensively optimize the algorithm in terms of search efficiency, obstacle avoidance capability, and path quality, effectively improving the efficiency and quality of path planning. Finally, a path optimization method is introduced to generate a smooth collision-free path through greedy pruning and B-spline smoothing optimization. Comprehensive simulation experiments and physical robotic arm experiments verify the feasibility and effectiveness of the proposed method. Compared with existing methods, AG-BI-RRT* achieves superior performance in path length, iteration time, number of search nodes, and path steering angle. The joint angle deviation between the digital twin model and the physical robotic arm does not exceed ±0.01°, and the average response time between the physical robotic arm and the twin model is 176.721 ms, satisfying the real-time and consistency requirements of digital twin systems. This work provides an effective solution for robotic arm path planning in a digital twin environment.