Dynamic Resource Management for Providing QoS in Drone Delivery Systems

Khamidehi, Behzad; Raeis, Majid; Sousa, E.S.

doi:10.48550/arxiv.2103.04015

Cited by 2 publications

(3 citation statements)

References 18 publications

(32 reference statements)

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“…Hence, there is no uncertainty about this cell, and the value of uncertainty is 0. However, as t increases, the value uncertainty increases based on (3). When the value of t becomes sufficiently large, the uncertainty tends to 1.…”

Section: B Uncertainty Modelmentioning

confidence: 98%

“…Unmanned aerial vehicles (UAVs) have recently attracted considerable interest in a wide range of applications. Aerial reach, high mobility, and cost-effective deployment are the key features that make the UAVs an ideal candidate for applications such as drone delivery [1]- [3], search and rescue [4], [5], wireless communications [6]- [8], and mapping, tracking, and monitoring [9]- [11]. UAV-assisted traffic monitoring in urban areas is another emerging application that can play a key role in intelligent transportation systems (ITSs) [12].…”

Section: Introductionmentioning

confidence: 99%

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Distributed Deep Reinforcement Learning for Intelligent Traffic Monitoring with a Team of Aerial Robots

Khamidehi¹,

Sousa²

2021

Preprint

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This paper studies the traffic monitoring problem in a road network using a team of aerial robots. The problem is challenging due to two main reasons. First, the traffic events are stochastic, both temporally and spatially. Second, the problem has a non-homogeneous structure as the traffic events arrive at different locations of the road network at different rates. Accordingly, some locations require more visits by the robots compared to other locations. To address these issues, we define an uncertainty metric for each location of the road network and formulate a path planning problem for the aerial robots to minimize the network's average uncertainty. We express this problem as a partially observable Markov decision process (POMDP) and propose a distributed and scalable algorithm based on deep reinforcement learning to solve it. We consider two different scenarios depending on the communication mode between the agents (aerial robots) and the traffic management center (TMC). The first scenario assumes that the agents continuously communicate with the TMC to send/receive realtime information about the traffic events. Hence, the agents have global and real-time knowledge of the environment. However, in the second scenario, we consider a challenging setting where the observation of the aerial robots is partial and limited to their sensing ranges. Moreover, in contrast to the first scenario, the information exchange between the aerial robots and the TMC is restricted to specific time instances. We evaluate the performance of our proposed algorithm in both scenarios for a real road network topology and demonstrate its functionality in a traffic monitoring system.

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Section: B Uncertainty Modelmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Distributed Deep Reinforcement Learning for Intelligent Traffic Monitoring with a Team of Aerial Robots

Khamidehi¹,

Sousa²

2021

Preprint

Self Cite

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“…Reducing customer waiting cost is an important goal of vehicle-UAV delivery [58], but scholars pay extra attention to it. Khamidehi et al [59] found that it is more reasonable to provide a probabilistic upper-bound on the queue length of the packages waiting to be delivered. Boyles and Zhu [60] embedded the waiting cost as a sub-problem into the electric vehicle route.…”

Section: Introductionmentioning

confidence: 99%

Vehicle and UAV Collaborative Delivery Path Optimization Model

Líu²,

Lai³

et al. 2022

Mathematics

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In the context of frequent public emergencies, emergency logistics distribution is particularly critical, and because of the unique advantages of unmanned aerial vehicles (UAVs), the model of coordinated delivery of vehicles and UAVs is gradually becoming an essential form of emergency logistics distribution. However, the omission of start-up costs prevents the cost of UAV battery replacement and the sorting, assembly and verification of packages from being factored into the total cost. Furthermore, most existing models focus on route optimization and delivery cost, which cannot fully reflect the customer’s desire for service satisfaction under emergency conditions. It is necessary to convert the unsatisfactory degree of time window into a penalty cost rather than a model constraint. Additionally, there is a lack of analysis on the mutual waiting cost between vehicles and UAVs when one of them is performing delivery tasks. Considering the effects of the time window, customer demand, maximum load capacity, and duration of distribution benefits, we propose a collaborative delivery path optimization model for vehicles and UAVs to minimize the total distribution cost. A genetic algorithm is used to obtain the model solution under the constraints of distribution subloops, distribution order, and take-off and landing nodes. To assess the efficacy of the vehicle and UAV collaborative delivery path optimization model, this paper employs a county-level district in Xi’an city as a pilot area for an emergency delivery. Compared with the vehicle-alone delivery model, the UAV-alone delivery model and vehicle-UAV collaborative delivery model, this model can significantly reduce the utilization of distribution vehicles while also significantly lowering the start-up cost, waiting cost and penalty cost. Thus, the model can effectively improve delivery timeliness and customer satisfaction. The total cost of this model is 39.2% less than that of the vehicle-alone delivery model and 16.5% less than that of the UAV-alone delivery model. Although its delivery cost is slightly higher than the vehicle-UAV collaborative delivery model, the reduction in the start-up cost and penalty cost decrease the overall cost of distribution by 11.8%. This suggests that to cut costs of all sizes and conserve half of the resources used by vehicles, employing the vehicle-UAV collaborative delivery model for emergency distribution is preferable. Moreover, the model integrating the start-up cost, penalty cost, waiting cost, etc., can more effectively express the requirements of timeliness for UAV delivery under emergency conditions.

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Dynamic Resource Management for Providing QoS in Drone Delivery Systems

Cited by 2 publications

References 18 publications

Distributed Deep Reinforcement Learning for Intelligent Traffic Monitoring with a Team of Aerial Robots

Distributed Deep Reinforcement Learning for Intelligent Traffic Monitoring with a Team of Aerial Robots

Vehicle and UAV Collaborative Delivery Path Optimization Model

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