Energy Consumption in Unmanned Aerial Vehicles: A Review of Energy Consumption Models and Their Relation to the UAV Routing

Thibbotuwawa, Amila; Nielsen, Peter; Banaszak, Zbigniew; Bocewicz, Grzegorz

doi:10.1007/978-3-319-99996-8_16

Cited by 76 publications

(64 citation statements)

References 32 publications

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“…In contrast to traditional routing problems, the UAV fleet mission planning problem addresses multiple decision layers including both the fleet level where the fleet is managed in terms of task assignment and availability management and the platform level where the individual missions of the UAVs are created [3,4]. The current state of research into the area is fragmented and neglects that different types of decisions are addressed at different abstraction levels of UAV fleet mission planning [4,8,14,15]. In general, the accomplishments in the field are focused on UAV routing for transporting materials and surveillance [2,[16][17][18] without considering the changing conditions in weather and non-linear energy consumption of UAVs [19].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Weather is critical for energy consumption as it affects the travel speed of the UAV (and thus total energy consumed), and the ambient temperature affects the energy capacity [2,3,28] of batteries used in UAVs. Cold temperatures may adversely affect battery performance until the batteries warm up [2,15]. Air density at the same time provides air resistance and lift and directly affects energy consumption.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Air density at the same time provides air resistance and lift and directly affects energy consumption. Air density is a function of humidity, air pressure, and temperature [3,15]. The current state of research has yet to consider weather factors and assumes the weather has a negligible impact on performance [2,16,31,32].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The technical parameters of UAVs including the UAV dimensions, battery capacity and payload limits, and the aspects of changing weather conditions, including wind speed, wind direction, and air density, all influence the search for possible UAV mission planning solutions [3,12]. As the linear approximations reported in the current state are insufficient in terms of finding acceptable energy calculations for the UAVs considered in this research study [10], non-linear models proposed are used to calculate energy consumption in relation to weather conditions [4,15].…”

Section: Literature Reviewmentioning

confidence: 99%

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UAV Mission Planning Resistant to Weather Uncertainty

Thibbotuwawa

Bocewicz

Radzki

et al. 2020

Sensors

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Fleet mission planning for Unmanned Aerial Vehicles (UAVs) is the process of creating flight plans for a specific set of objectives and typically over a time period. Due to the increasing focus on the usage of large UAVs, a key challenge is to conduct mission planning addressing changing weather conditions, collision avoidance, and energy constraints specific to these types of UAVs. This paper presents a declarative approach for solving the complex mission planning resistant to weather uncertainty. The approach has been tested on several examples, analyzing how customer satisfaction is influenced by different values of the mission parameters, such as the fleet size, travel distance, wind direction, and wind speed. Computational experiments show the results that allow assessing alternative strategies of UAV mission planning.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

UAV Mission Planning Resistant to Weather Uncertainty

Thibbotuwawa

Bocewicz

Radzki

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

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“…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%

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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Energy‐consumption model for rotary‐wing drones

Li,

Zhan,

Wang

2024

Journal of Field Robotics

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With technological advancement, the use of drones in delivery systems has become increasingly feasible. Many companies have developed rotary‐wing drone (RWD) technologies for parcel delivery. At present, the limited endurance is the main disadvantage of RWD delivery. The energy consumption of RWDs must be carefully managed, and it is necessary to develop an effective energy‐consumption model to support RWD flight planning. Because the interaction between the forces on the RWD and its flying environment is very complex, it is challenging to estimate accurately the RWD energy consumption. This study summarizes several energy‐consumption models proposed in the literature, then we develop an RWD energy‐consumption model (called the integrated model) based on analyzing the dynamic equilibrium of forces and power consumption in flight phases (including climb, descent, hover, and horizontal flight). Computational experiments involving several commercial RWDs indicate that the integrated model is more effective than several models in the literature. In the case where an RWD completed one flight segment, on average, 87.63% of the battery capacity was consumed in the horizontal flight phase. We also analyzed the effects of the total mass and horizontal airspeed on the RWD endurance and found that a larger mass corresponded to shorter endurance, and in the experimental range of the horizontal airspeed, a higher horizontal airspeed corresponded to longer endurance. Moreover, the total mass affected the RWD endurance more significantly than the horizontal airspeed.

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Energy Consumption in Unmanned Aerial Vehicles: A Review of Energy Consumption Models and Their Relation to the UAV Routing

Cited by 76 publications

References 32 publications

UAV Mission Planning Resistant to Weather Uncertainty

UAV Mission Planning Resistant to Weather Uncertainty

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

Energy‐consumption model for rotary‐wing drones

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