## 6 Conclusions and Future Recommendations  
For large-scale problems, providing precise and prompt solutions poses a significant challenge. This study introduces a novel method to tackle the problem of path-planning for unmanned delivery vehicles in complex environments with numerous interconnected points. The comprehensive experimental evaluation demonstrates the superiority of the PGDD model in unmanned delivery vehicle path-planning. Across diverse scenarios, the PGDD achieves an average total cost reduction of 8.73% in 100-node tasks and 12.5% in large-scale 400-node environments compared to state-of-the-art baselines, validating its scalability and efficiency. The fuzzy time-window penalty mechanism dynamically adjusts route weights based on customer satisfaction constraints, improving delivery success rates by 15.2% while maintaining real-time responsiveness (<90 ms). The dual-decoder co-training framework, which synergizes reinforcement learning with pseudo-label supervision, ensures robust optimization of both global routing costs and local edge selection accuracy. By integrating point-graph joint embeddings and adaptive learning rate strategies, the PGDD effectively balances multi-objective trade-offs in complex urban logistics. Additionally, considering that training the PGDD model requires substantial computational resources, its practical application in small-scale logistics companies might be limited. We propose two solutions. First, we propose model light-weighting by compressing the model through knowledge distillation and quantization training and deploying it on edge computing devices for low-power consumption. Second, we propose cloud computing collaboration, exploring a cloud-edge collaborative distributed training framework that enables small and medium enterprises to access high-performance computing resources on demand while ensuring data privacy. In future recommendations, First, Dynamic Environment Adaptation will focus on enhancing PGDD's integration with real-time sensors, such as LiDAR and cameras, to improve obstacle avoidance. Second, Multi-Vehicle Coordination will explore conflict resolution and task allocation mechanisms to optimize logistics operations in large-scale multi-vehicle systems. Third, Energy Sustainability will introduce renewable energy-aware charging scheduling, such as solar-powered solutions, to reduce the carbon footprint, as the current energy model does not account for green energy considerations. Fourth, Human-Centric Interfaces will be developed to enable interactive manual route adjustments and ethical compensation strategies for delivery delays, ensuring greater flexibility and fairness in logistics operations. Lastly, Cross-Domain Applications will extend PGDD's applicability to drone-based deliveries and medical supply distribution, further validating its generalizability across different logistics scenarios. Conceptualization, J.C.; Data curation, J.C.; Formal analysis, J.C.; Funding acquisition, Z.N. ; Investigation, J.C.; Methodology, J.C.; Project administration, Z.N. ; Software, J.C.; Supervision, W.L., Q.C. and R.Y. ; Validation, J.C.; Visualization, J.C.; Writing--original draft, J.C.; Writing--review and editing, J.C. All authors have read and agreed to the published version of the manuscript. This research was financially supported by the Enterprise Entrusted Projects under Grant W2023 JSZX0212 and W2023 JSZX0213, in part by the Anhui Provincial Science and Technology Major under Grant 201903a05020020. Not applicable. The data that support the findings of this study are available from the corresponding author upon reasonable request. We thank all the authors of the references that gave us inspiration and help. The authors are grateful to the editors and anonymous reviewers for their valuable comments that improved the quality of this paper. The authors declare no conflicts of interest. * [PERSON] et al.