Study of Urban Logistics Drone Path Planning Model Incorporating Service Benefit and Risk Cost

• Published in: Drones (Basel) Vol. 6; no. 12; p. 418
• Main Authors: Shao, Quan, Li, Jiaming, Li, Ruoheng, Zhang, Jiangao, Gao, Xiaobo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-12-2022
• Subjects: Aircraft; Algorithms; Aviation; Buildings; Collision dynamics; Cost benefit analysis; Customer services; Drone aircraft; Drone vehicles; Drones; Genetic algorithms; Logistics; Logistics services; Mathematical models; Methods; Metropolitan areas; optimization algorithms; Path planning; Pedestrian safety; Pedestrians; Planning; Risk assessment; risk cost; Risk management; Safety; service benefit; Technology application; Traffic accidents & safety; Urban environments; urban logistics; China
• ISSN: 2504-446X; 2504-446X
• DOI: 10.3390/drones6120418

The application of drones provides a powerful solution for “the last-mile” logistics services, while the large-scale implementation of logistics drone services will threaten the safety of buildings, pedestrians, vehicles, and other elements in the urban environment. The balance of risk cost and service benefit is accordingly crucial to managing logistics drones. In this study, we proposed a cost-benefit assessment model for quantifying risk cost and service benefit in the urban environment. In addition, a global heuristic path search rule was developed to solve the path planning problem based on risk mitigation and customer service. The cost-benefit assessment model quantifies the risk cost from three environmental elements (buildings, pedestrians, and vehicles) threatened by drone operations based on the collision probability, and the service benefit based on the characteristics of logistics service customers. To explore the effectiveness of the model in this paper, we simulate and analyse the effects of different risk combinations, unknown risk zones, and risk-benefit preferences on the path planning results. The results show that compared with the traditional shortest-distance method, the drone path planning method proposed in this paper can accurately capture the distribution of risks and customers in the urban environment. It is highly reusable in ensuring service benefits while reducing risk costs and generating a cost-effective path for logistics drones. We also compare the algorithm in this paper with the A* algorithm and verify that our algorithm improves the solution quality in complex environments.