In addition to its crucial impact on customer satisfaction, last-mile delivery (LMD) is notorious for being the most time-consuming and costly stage of the shipping process. Pressing environmental concerns combined with the recent surge of e-commerce sales have sparked renewed interest in automation and electrification of last-mile logistics. To address the hurdles faced by existing robotic couriers, this paper introduces a customer-centric and safety-conscious LMD system for small urban communities based on AI-assisted autonomous delivery robots. The presented framework enables end-to-end automation and optimization of the logistic process while catering for real-world imposed operational uncertainties, clients' preferred time schedules, and safety of pedestrians. To this end, the integrated optimization component is modeled as a robust variant of the Cumulative Capacitated Vehicle Routing Problem with Time Windows, where routes are constructed under uncertain travel times with an objective to minimize the total latency of deliveries (i.e., the overall waiting time of customers, which can negatively affect their satisfaction). We demonstrate the proposed LMD system's utility through real-world trials in a university campus with a single robotic courier. Implementation aspects as well as the findings and practical insights gained from the deployment are discussed in detail. Lastly, we round up the contributions with numerical simulations to investigate the scalability of the developed mathematical formulation with respect to the number of robotic vehicles and customers.

翻译：除了对客户满意度有关键影响外，最后一公里配送（LMD）还因是运输流程中最耗时、成本最高的环节而饱受诟病。当前紧迫的环境问题与近期电子商务销售额的激增，重新引发了人们对最后一公里物流自动化与电气化的关注。为应对现有机器人快递员面临的挑战，本文提出了一种面向小范围城市社区、以客户为中心且注重安全性的LMD系统，该系统基于人工智能辅助的自主配送机器人。所提出的框架能够实现物流流程的端到端自动化与优化，同时兼顾现实世界中存在的运营不确定性、客户偏好的时间安排以及行人安全。为此，集成的优化组件被建模为具有时间窗的累积容量车辆路径问题的鲁棒变体，其中在不确定行驶时间下构建路线，目标是最小化配送总延迟（即客户的总等待时间，该因素可能对客户满意度产生负面影响）。我们通过在一所大学校园内使用单台机器人快递员进行的实际试验，展示了所提出的LMD系统的实用性。文中详细讨论了实施细节以及从部署中获得的发现与实践经验。最后，我们通过数值模拟对研究成果进行补充，以探究所建立的数学模型在机器人车辆和客户数量方面的可扩展性。