Drone Package Delivery: A Heuristic approach for UAVs path planning and tracking

Bekhti, Mustapha; Achir, Nadjib; Boussetta, Khaled; Abdennebi, Marwen

doi:10.4108/eai.31-8-2017.153048

Cited by 8 publications

(8 citation statements)

References 11 publications

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“…: fuel consumption [17], delivery time [7] and delivery costs [18]. & Various fields of application e.g., reconnaissance and mapping [19], package delivery [20] and delivery communication capabilities [14,21].…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling Robust Delivery Scenarios for a Fleet of Unmanned Aerial Vehicles in Disaster Relief Missions

Radzki

Golińska-Dawson

Bocewicz

et al. 2021

J Intell Robot Syst

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Besides commercial and military applications, unmanned aerial vehicles (UAVs) are now used more commonly in disaster relief operations. This study proposes a novel model for proactive and reactive planning (different scenarios) that allow for a higher degree of realism, thus a higher likelihood for a mission of being executed according to the plan even when weather forecasts are changing. The novelty of this study results from the addition of a function of resistance of UAVs mission to changes in weather conditions. We link the influence of weather conditions on the UAV’s energy consumption. The goal is to ensure the completion of planned deliveries by a fleet of UAVs under changing weather conditions before their batteries discharge and to identify the emergency route for returned if the mission cannot be completed. An approach based on constraint programming is proposed, as it has proven to be effective in various contexts, especially related to the nonlinearity of the system’s characteristics. The proposed approach has been tested on several instances, which have allowed for analyzing how the plan of mission is robust to the changing weather conditions with different parameters, such as the fleet size, battery capacity, and distribution network layout.

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Section: Literature Reviewmentioning

confidence: 99%

Modelling Robust Delivery Scenarios for a Fleet of Unmanned Aerial Vehicles in Disaster Relief Missions

Radzki

Golińska-Dawson

Bocewicz

et al. 2021

J Intell Robot Syst

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show abstract

“…The creation of sub-missions is done by the method proposed in Figure 5. The example of a cluster containing five customers (1)(2)(3)(4)(5) and an FTW of 20 minutes is considered here to illustrate the creation of sub-missions ( Figure 9, 10, 11 and 12). For each FTW which falls under a STW, the input parameters for the UAV fleet size, maximum carrying payload, the number of customers per route and the maximum energy of the UAV are changed to make the sub-missions while all remaining parameters are kept constant.…”

Section: Input Data Valuesmentioning

confidence: 99%

Section: Input Data Valuesmentioning

confidence: 99%

“…The immense development of unmanned aerial vehicle (UAV) capabilities has promulgated its influence in many domains, such as defense, search and rescue, agriculture, manufacturing, and environmental surveillance [1][2][3][4] to execute complex industrial functions. Fleet-mission planning for UAVs in the context of materials delivery has become a major research topic [5][6][7][8] and could be treated as an extension of the vehicle routing problem (VRP) [9] which belongs to the class of planning problems that are NP-hard [10,11]. Various objectives could be used in UAV mission planning such as reducing individual UAV costs, enhancing its profit, increasing safety in operations, reducing lead time, or increasing the load capacity of the entire system [12,13].…”

Section: Introductionmentioning

confidence: 99%

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A Solution Approach for UAV Fleet Mission Planning in Changing Weather Conditions

et al. 2019

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With a rising demand for utilizing unmanned aerial vehicles (UAVs) to deliver materials in outdoor environments, particular attention must be given to all the different aspects influencing the deployment of UAVs for such purposes. These aspects include the characteristics of the UAV fleet (e.g. size of fleet, UAV specifications and capabilities), the energy consumption (highly affected by weather conditions and payload) and the characteristics of the network and customer locations. All these aspects must be taken into account when aiming to achieve deliveries to customers in a safe and timely manner. However, at present, there is a lack of decision support tools and methods for mission planners that consider all these influencing aspects together. To bridge this gap, this paper presents a decomposed solution approach, which provides decision support for UAVs’ fleet mission planning. The proposed approach assists flight mission planners in aerospace companies to select and evaluate different mission scenarios, for which flight-mission plans are obtained for a given fleet of UAVs, while guaranteeing delivery according to customer requirements in a given time horizon. Mission plans are analyzed from multiple perspectives including different weather conditions (wind speed and direction), payload capacities of UAVs, energy capacities of UAVs, fleet sizes, the number of customers visited by a UAV on a mission and delivery performance. The proposed decision support-driven declarative model supports the selection of the UAV mission planning scenarios subject to variations on all these configurations of the UAV system and variations in the weather conditions. The computer simulation based experimental results, provides evidence of the applicability and relevance of the proposed method. This ultimately contributes as a prototype of a decision support system of UAVs fleet-mission planning, able to determine whether is it possible to find a flight-mission plan for a given fleet of UAVs guaranteeing customer satisfaction under the given conditions. The mission plans are created in such a manner that they are suitable to be sent to Air Traffic Control for flight approval.

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“…optimization criteria (e.g., fuel consumption [24], delivery time [25], delivery costs [26]) • fields of application (e.g., reconnaissance and mapping [27], package delivery [28], delivery communication capabilities [29,30])…”

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confidence: 99%

Reactive UAV Fleet’s Mission Planning in Highly Dynamic and Unpredictable Environments

et al. 2021

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Unmanned aerial vehicles (UAVs) create an interesting alternative for establishing more sustainable urban freight deliveries. The substitution of traditional trucks in the last-mile distribution by a UAV fleet can contribute to urban sustainability by reducing air pollution and increasing urban freight efficiency. This paper presents a novel approach to the joint proactive and reactive planning of deliveries by a UAV fleet. We develop a receding horizon-based approach to reactive, online planning for the UAV fleet’s mission. We considered the delivery of goods to spatially dispersed customers over an assumed time horizon. Forecasted weather changes affect the energy consumption of UAVs and limit their range. Therefore, consideration should be given to plans for follow-up tasks, previously unmet needs, and predictions of disturbances over a moving time horizon. We propose a set of reaction rules that can be encountered during delivery in a highly dynamic and unpredictable environment. We implement a constraint programming paradigm, which is well suited to cope with the nonlinearity of the system’s characteristics. The proposed approach to online reactive UAV routing is evaluated in several instances. The computational experiments have shown that the developed model is capable of providing feasible plans for a UAV fleet’s mission that are robust to changes in weather and customer’s orders.

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Drone Package Delivery: A Heuristic approach for UAVs path planning and tracking

Cited by 8 publications

References 11 publications

Modelling Robust Delivery Scenarios for a Fleet of Unmanned Aerial Vehicles in Disaster Relief Missions

Modelling Robust Delivery Scenarios for a Fleet of Unmanned Aerial Vehicles in Disaster Relief Missions

A Solution Approach for UAV Fleet Mission Planning in Changing Weather Conditions

Reactive UAV Fleet’s Mission Planning in Highly Dynamic and Unpredictable Environments

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