Incentive Mechanism and Path Planning for UAV Hitching over Traffic Networks

TL;DR

This paper introduces a two-stage multimodal logistics framework where UAVs call ground vehicles for hitching to extend delivery range, optimizing incentive mechanisms and path planning with a novel conflict avoidance algorithm, validated through simulations.

Contribution

It proposes a joint incentive mechanism and path planning model for UAV hitching with a new conflict avoidance algorithm, enhancing delivery success rates and efficiency.

Findings

Increased UAV delivery success rate in simulations.

UAV delivery time is twice as fast as ground vehicles alone.

Effective dynamic pricing balances vehicle response and payments.

Abstract

Package delivery via the UAVs is a promising transport mode to provide efficient and green logistic services, especially in urban areas or complicated topography. However, the energy storage limit of the UAV makes it difficult to perform long-distance delivery tasks. In this paper, we propose a novel multimodal logistics framework, in which the UAVs can call on ground vehicles to provide hitch services to save their own energy and extend their delivery distance. This multimodal logistics framework is formulated as a two-stage model to jointly consider the incentive mechanism design for ground vehicles and path planning for UAVs. In Stage I, to deal with the motivations for ground vehicles to assist UAV delivery, a dynamic pricing scheme is proposed to best balance the vehicle response time and payments to ground vehicles. It shows that a higher price should be decided if the vehicle response time is long to encourage more vehicles to offer a ride. In Stage II, the task allocation and path planning of the UAVs over traffic network is studied based on the vehicle response time obtained in Stage I. To address pathfinding with restrictions and the performance degradation of the pathfinding algorithm due to the rising number of conflicts in multi-agent pathfinding, we propose the suboptimal conflict avoidance-based path search (CABPS) algorithm, which has polynomial time complexity. Finally, we validate our results via simulations. It is shown that our approach is able to increase the success rate of UAV package delivery. Moreover, we estimate the delivery time of the UAV in a pessimistic case, it is still twice as fast as the delivery time of the ground vehicle only.