Multi-Level Deep Deterministic Policy Gradient reinforcement learning for safety-constrained trajectory optimization...

Collaborative robots operating in modern manufacturing environments require trajectory planning strategies that simultaneously support efficiency, adaptability, and operational safety. This study proposes a Multi-Level Deep Deterministic Policy Gradient (ML-DDPG) framework for safety-constrained trajectory optimization. The framework combines a strategic waypoint level, a continuous DDPG motion-control policy, and a reactive feasibility projection that corrects unsafe actions before execution. The trajectory-planning problem is formulated as a Markov Decision Process with reward terms for goal progress, obstacle clearance, smoothness, energy, execution time, and safety. Descriptive experiments in a custom PyTorch-based three-dimensional environment compare ML-DDPG with conventional planners and the DDPG, TD3, and SAC baselines. In the retained single training run, ML-DDPG produced observed values of 98.7% success, 1.08 mm trajectory error, a 0.98 smoothness index, and no recorded collision. These values are single-run observations and are not means over independent seeds; variability and statistical significance cannot be estimated from the available record. The exported traces show faster reward growth within the 200-episode budget, and the single-run ablation record associates the largest observed gains with the combined multi-level, reward, and projection components. One UR5e demonstration yielded 0.018 m mean tracking error, 0.042 m maximum tracking error, and no collision in that trial. A separate ABB welding sequence is included only as qualitative feasibility evidence because synchronized trajectory, force, and weld-quality logs were unavailable. The findings motivate further evaluation, while multi-seed training, repeated hardware trials, complete reporting of evaluation procedures, and cross-platform validation remain necessary before broader generalization.