为送物机器人设计抗扰路徑規劃，提升複雜環境下的配送可靠性。
Robust Route Planning for Sidewalk Delivery Robots
- 結合模擬與魯棒優化，建模行人與障礙物帶來的行程時間不確定性。
- 椭圓與分布式魯棒方法在平均與最差情況延遲上優於其他方案。
- 適用於寬度大、速度慢或保守導航的機器人，在高人流與惡劣天氣下表現更穩。
送物機器人是解決最後一公里物流的有前景方案，但其在動態環境中面臨行人流與障礙物干擾，導致行程時間高度不確定，嚴重影響效率。本研究通過模擬機器人、行人與障礙物互動，顯式建模行程時間不確定性，結合魯棒優化生成真實行程時間。探討三種不確定性集構建方法：預算型、橢圓型與支援向量聚類（SVC）法，並引入基於模糊集的分佈式魯棒最短路徑（DRSP）方法。以斯德哥爾摩市中心行人模式為基礎進行實證分析，結果顯示相比傳統最短路徑（SP）方法，魯棒路徑規劃顯著提升運行可靠性；其中橢圓與DRSP方法在平均與最差情況延遲上表現最佳。敏感性分析表明，對較寬、較慢、導航更保守的機器人而言，魯棒方法效益更高，尤其在高人流與惡劣天氣情境下。
原文摘要 · Abstract (English)
Sidewalk delivery robots are a promising solution for last-mile freight distribution. Yet, they operate in dynamic environments characterized by pedestrian flows and potential obstacles, which make travel times highly uncertain and can significantly affect their efficiency. This study addresses the robust route planning problem for sidewalk robots by explicitly accounting for travel time uncertainty generated through simulated interactions between robots, pedestrians, and obstacles. Robust optimization is integrated with simulation to reproduce the effect of obstacles and pedestrian flows and generate realistic travel times. Three different approaches to derive uncertainty sets are investigated, including budgeted, ellipsoidal, and support vector clustering (SVC)-based methods, together with a distributionally robust shortest path (DRSP) method based on ambiguity sets that model uncertainty in travel-time distributions. A realistic case study reproducing pedestrian patterns in Stockholm's city center is used to evaluate the efficiency of robust routing across various robot designs and environmental conditions. Results show that, when compared to a conventional shortest path (SP) method, robust routing significantly enhances operational reliability under variable sidewalk conditions. The ellipsoidal and DRSP approaches outperform the other methods in terms of average and worst-case delay. Sensitivity analyses reveal that robust approaches are higher for sidewalk delivery robots that are wider, slower, and more conservative in their navigation behaviors, especially in adverse weather and high pedestrian congestion scenarios.
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