Towards Fully Autonomous Last-mile Logistics: A Case Study with a Safety-boosted Self-driving Delivery Robot

While crucial to customer satisfaction, last-mile delivery (LMD) remains the most time-consuming and costly stage of the shipping process. Pressing environmental concerns and the recent surge in e-commerce sales have sparked renewed interest in automating and electrifying last-mile logistics. To address the limitations of current robotic couriers, this paper presents a customer-centric and safety-conscious LMD system designed for small urban communities. Built on AI-assisted autonomous delivery robots, the proposed system enables end-to-end automation and optimization of the logistics process, with added considerations for pedestrian safety. The optimization component is modeled as a robust variant of the Cumulative Capacitated Vehicle Routing Problem with Time Windows (RCCVRPTW), accommodating real-world operational uncertainties, customer satisfaction, and delivery preferences. Specifically, RCCVRPTW constructs routes under uncertain travel times with the objective of minimizing total delivery latency (i.e., customers’ waiting time), directly addressing factors linked to customer dissatisfaction. To validate the effectiveness of the system, real-world proof-of-concept tests are conducted at a university campus using a single robotic courier. Detailed discussions cover key implementation aspects, findings, and insights gained from these deployment experiments. Finally, the scalability of the RCCVRPTW model is investigated in the Gurobi solver with varying numbers of robotic vehicles and customers.