Robust Route Planning for Sidewalk Robot Delivery

Abstract

The last-mile delivery problem is a critical challenge in urban logistics due to high costs, traffic congestion, and environmental impacts. Sidewalk delivery robots offer a promising solution for urban areas, providing safer and higher-capacity alternatives to drones. However, their efficiency is significantly affected by unreliable travel times on sidewalks. This study addresses the robust shortest path problem (RSPP) for sidewalk robots, explicitly accounting for travel time uncertainty due to varying sidewalk conditions such as density and obstacles. We integrate optimization with simulation, using generated travel times to derive alternative uncertainty sets (budgeted, ellipsoidal, and SVC-based). This approach is applied to a realistic case study reproducing pedestrian patterns in Stockholm’s city center (Sweden) and examines the economic efficiency of robust routing under various robot design and environmental factors. Results demonstrate that robust routing significantly improves operational reliability under variable sidewalk conditions compared to traditional methods.