Abstract
With the development of e-commerce, the last-mile delivery has become a significant part of customers’ shopping experience. In this paper, an autonomous last-mile delivery method using multiple unmanned ground vehicles is investigated. Being a smart logistics service, it provides a promising solution to reduce the delivery cost, improve efficiency, and avoid the spread of airborne diseases, such as SARS and COVID-19. By using a cooperation strategy with multiple heterogeneous robots, contactless parcel delivery can be carried out within apartment complexes efficiently. In this paper, the last-mile delivery with heterogeneous UGVs is formulated as an optimization problem aimed at minimizing the maximum makespan to complete all tasks. Then, a heuristic algorithm combining the Floyd’s algorithm and PSO algorithm is proposed for task assignment and path planning. This algorithm is further realized in a distributed scheme, with all robots in a swarm working together to obtain the best task schedule. A good solution with an optimized makespan is achieved by considering the constraints of various robots in terms of speed and payload. Simulations and experiments are carried out and the obtained results confirm the validity and applicability of the developed approaches.
Highlights
Unmanned ground vehicles (UGVs) have been successfully applied in diverse applications, such as planet exploration on the Moon and Mars [1, 2]
Given the environment in which UGVs run, task assignment and path planning should be performed considering the parameters of parcels and UGVs
Based on Algorithm 2, we have developed a controller that can be distributed on a set of UGVs. ese controllers can communicate with each other, achieve collective decisionmaking, and drive each robot to deliver goods to corresponding targets following an optimized path
Summary
Unmanned ground vehicles (UGVs) have been successfully applied in diverse applications, such as planet exploration on the Moon and Mars [1, 2]. Ere exist many extensions of the VRP, which can be classified into different categories according to configurations, problem modelling, solving algorithms, and objectives [6,7,8,9,10,11,12,13,14,15,16] This problem can be formulated differently considering homogeneous vehicles or heterogeneous vehicles [6, 7].
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