Obstacle Detection in Last-Mile Delivery Robots under Sensor Fusion Pipelines and Routing Diversity

Abstract

The rapid proliferation of e-commerce has significantly amplified the demand for efficient urban logistics, placing the last mile of delivery under immense scrutiny. Autonomous last mile delivery robots have emerged as a promising solution to mitigate the high costs and logistical bottlenecks associated with this final leg of the supply chain. However, deploying autonomous platforms in unstructured, highly dynamic urban environments presents formidable challenges, particularly concerning navigational safety and reliability. This paper provides a comprehensive exploration of obstacle detection mechanisms, emphasizing the critical integration of advanced sensor fusion pipelines and routing diversity. By synthesizing data from disparate modalities such as Light Detection and Ranging, vision cameras, radio detection and ranging, and ultrasonic sensors, delivery robots can achieve a robust perception of their surroundings regardless of adverse weather or variable illumination. Furthermore, isolated perception is insufficient for operational success in complex scenarios. This study elucidates how routing diversity algorithms leverage fused perceptual data to dynamically calculate alternative paths, thereby preventing navigational deadlocks and enhancing systemic resilience. Through detailed theoretical analysis and simulated performance evaluations, the research demonstrates that the symbiotic relationship between high-fidelity sensor fusion and adaptive routing paradigms significantly improves both obstacle avoidance efficacy and overall delivery success rates. The findings offer valuable insights for the design and deployment of next-generation autonomous logistics systems.