Observation-Enhanced Reinforcement Learning with Prior Reward Constraints for Robust AGV Obstacle Avoidance

The widespread deployment of automated guided vehicles (AGVs) in dynamic complex environments poses a critical challenge in achieving efficient avoidance and energy optimization. This paper proposes an observation-enhanced reinforcement learning with reward constraints algorithm (ORLRCA) to enhance AGV safety and energy efficiency obstacle avoidance in complex scenarios. In our work, first, a multimodal perceptual attention mechanism is introduced to dynamically capture obstacle motion patterns and environmental semantic features, thereby enhancing scene perception capabilities. Second, a multi-scale prior reward-constrained function is designed to jointly optimize safety distance, path smoothness and energy consumption metrics, effectively addressing the suboptimal convergence caused by conflicting objectives in RL strategies. Finally, leveraging the actor-critic network architecture of proximal policy optimization (PPO), we achieve end-to-end optimization of robust obstacle avoidance policy generation through synergistic integration of attention-enhanced state representations and multi-scale reward signals. To validate efficacy, a high-fidelity simulation environment is developed for comparative experiments. Results demonstrate that the proposed algorithm exhibits superior performance in obstacle avoidance success rate (SR) and energy efficiency compared to baseline RL methods, establishing a theoretical foundation for secure deployment and energy-efficient management of AGVs in practical industrial scenarios.