In modern industrial and laboratory environments, robotic arms often operate in complex, cluttered spaces. Ensuring reliable obstacle avoidance and efficient motion planning is therefore essential for safe performance. Motivated by the shortcomings of traditional path planning methods and the growing demand for intelligent automation, we propose a novel reinforcement learning framework that combines a modified artificial potential field (APF) method with the Deep Deterministic Policy Gradient algorithm. Our model is formulated in a continuous environment, which more accurately reflects real-world conditions compared to discrete models. This approach directly addresses the common local optimum issues of conventional APF, enabling the robot arm to navigate complex three-dimensional spaces, optimize its end-effector trajectory, and ensure full-body collision avoidance. Our main contributions include the integration of reinforcement learning factors into the APF framework and the design of a tailored reward mechanism with a compensation term to correct for suboptimal motion directions. This design not only mitigates the inherent limitations of APF in environments with closely spaced obstacles, but also improves performance in both simple and complex scenarios. Extensive experiments show that our method achieves safe and efficient obstacle avoidance with fewer steps and lower energy consumption compared to baseline models, including a TD3-based variant. These results clearly demonstrate the significant potential of our approach to advance robot motion planning in practical applications.
Schneider et al. (Fri,) studied this question.