An Unmanned Delivery Vehicle Path-Planning Method Based on Point-Graph Joint Embedding and Dual Decoders

The path-planning of unmanned delivery vehicles (UDVs) has garnered significant interest due to their extensive use in contactless delivery during severe epidemics and automated delivery of parcels in diverse scenarios. However, previous studies have focused on achieving the shortest path or time based on the comprehensive cost consumption in the transportation process and ignored the impact of different customers’ different delivery time requirements in the actual interactive system. Hence, a path-planning model is presented to tackle the routing dilemma of UDVs in logistics. This new dilemma, called the unmanned delivery vehicle routing problem (UDVRP), considers the comprehensive transportation cost consumption of distribution vehicles and the customer satisfaction of each distribution point. Customer satisfaction is defined based on the delivery time requirements of different customers. This novel deep neural network model incorporates an attention mechanism and applies a method called point-graph joint embedding and dual decoders (PGDD) to solve the problem. The network’s architecture, consisting of an encoder and two decoders, directly determines the path for unmanned delivery vehicles. In addition, the model is trained offline using a deep reinforcement-learning strategy in combination with pseudo-label learning. In this scenario, the output of one decoder serves as the label for another, overseeing its learning process to choose the most effective path. Experimental results demonstrate that PGDD reduces total costs by 8.73% on average compared to state-of-the-art algorithms in 100-node scenarios, with performance gains reaching 12.5% for larger-scale problems (400 nodes), validating its superiority in complex path-planning. Additionally, PGDD improves customer satisfaction by 15.2% and achieves a response time below 90ms in real-world deployment tests. The experimental results demonstrate that the proposed method is superior to several state-of-the-art algorithms in solving the path-planning problem of unmanned distribution vehicles.