Vision‐based map building and path planning method in unmanned air/ground vehicle cooperative systems

Xi, Axing; Zhao, Jiying; Fu, Hongxun; Zhou, Tao; Liu, Dongjie

doi:10.1049/joe.2019.1144

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…With the rapid development of e-commerce, express delivery, and industry intelligent unmanned system technology, the cooperation of unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) has long been used for some harshest military tasks, such as monitoring and inspection in contested urban environments and border patrolling [1], border intelligence,surveillance, reconnaissance (ISR) missions [2]- [4], and post-disaster relief [5]. It is also used in many domain, such as cellular communications, data gathering [6], urban illegal building detection [7] and map building [8]. During the epidemic diseases spreading such as COVID-19, package delivery becomes a key means of emergency resource transmission.…”

Section: Introductionmentioning

confidence: 99%

Commanding Cooperative UGV-UAV With Nested Vehicle Routing for Emergency Resource Delivery

et al. 2020

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Unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) have been widely used in delivery. In the context of the COVID-2019, in order to control the development of the epidemic, many places have adopted measures to isolate and close the area once a confirmed case is found. While reducing the contact between people, it also blocks the normal driving of vehicles. Only by changing the traditional logistics and distribution methods can customers who have been in a closed and isolated area for a long time be served. Therefore, we use the Cooperative UGV-UAV to achieve it. In this paper, when commanding cooperative UGV and UAV for emergency resource delivery, we mainly focus on two questions: how to accept the operation order (OPORD) from the commander, how to generate a nested vehicle routing planning. We first employ one intelligent task understanding module to drive the intelligent unmanned vehicles to accept and process the C-BML (Coalition Battle Management Language) formatted OPORD with 5W (what, who, where, why, when) elements. Then, we slove the nested vehicle routing planning problem as a mixed integer linear program (MILP) with the outputs of what is the UGV route, what is the UGV sortie, and how to control the customers' distribution between the UGV and the UAV. Experimental results of random instances and case study show that using the iterative improvement algorithm increase the speed rate of solving more than 10%.

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