A Proactive Approach to Resistant UAV Mission Planning

Radzki, Grzegorz; Nielsen, Peter; Bocewicz, Grzegorz; Banaszak, Zbigniew

doi:10.1007/978-3-030-40971-5_11

Cited by 10 publications

(10 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A research gap exists with regard to the planning of UAV missions in a manner, which is robust the changing weather conditions. Our work is application-oriented and it extends the previous works of Thibbotuwawa et al [11,12], and Radzki et al [13], as we use declarative modelling methods in order to provide alternative scenarios for disaster relief operations. In our previous works we have proposed:…”

Section: Introductionmentioning

confidence: 83%

“…Due to the NP-difficult nature of problems and occurrence of non-linear constraints, machine learning and/or artificial intelligent methods are used i.e., metaheuristics driven, like Variable Neighbourhood Search and Tabu Search [29,30]. Recent publications also present the declarative modelling based models, which enable for constraint programming problem formulation [10,11,13,31,32]. The abovementioned formal methods are supplemented by simulation tools, such as flight simulators [33,34].…”

Section: Literature Reviewmentioning

confidence: 99%

“…& Declarative modelling framework for an iterative approach to planning of UAV missions in the environment with constant weather conditions [11], & knapsack driven proactive UAVs mission planning [13],by using a model that takes into account the change of the cargo's weight during the flight, & Fast prototyping of feasible UAVs fleet scenarios with different weather conditions (wind speed and direction), different flight strategies (with constant air speed or constant ground speed), variable fleet sizes and number of recipients [12].…”

Section: Introductionmentioning

confidence: 99%

“…This study considers the mission planning of a UAV fleet under the assumption of an emergency return route (reactively activated when the weather conditions change beyond the expected range), and provides new contributions to the currently available literature. The novelty of the presented approach results from the extension of the declarative model used in our previous works, particularly in [11][12][13], by the addition of a function of resistance of a UAV mission to changes in the weather conditions. The proposed approach allows for foreseeing the UAV's battery discharged before completing its mission and thus, can be used in the process of reactive planning of UAVs fleet mission.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 83%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

View full text Add to dashboard Cite

show abstract

“…The extended work in [11] considers the same setting with many clusters, in which the problem is seen as a Constraint Satisfaction Problem. In [12], solutions are seen as sequences of routing problems formulated as 0-1 knapsack problems. However, in such works is not clear how the wind dynamism can affect the delivery planning since the wind is assumed to be constant during each mission.…”

Section: Drone Delivery Problemmentioning

confidence: 99%

Energy-Constrained Delivery of Goods with Drones Under Varying Wind Conditions

Sorbelli,

Corò,

Das

et al. 2020

Preprint

View full text Add to dashboard Cite

In this paper, we study the feasibility of sending drones to deliver goods from a depot to a customer by solving what we call the Mission-Feasibility Problem (MFP). Due to payload constraints, the drone can serve only one customer at a time. To this end, we propose a novel framework based on time-dependent cost graphs to properly model the MFP and tackle the delivery dynamics. When the drone moves in the delivery area, the global wind may change thereby affecting the drone's energy consumption, which in turn can increase or decrease. This issue is addressed by designing three algorithms, namely: (i) compute the route of minimum energy once, at the beginning of the mission, (ii) dynamically reconsider the most convenient trip towards the destination, and (iii) dynamically select only the best local choice. We evaluate the performance of our algorithms on both synthetic and real-world data. The changes in the drone's energy consumption are reflected by changes in the cost of the edges of the graphs. The algorithms receive the new costs every time the drone flies over a new vertex, and they have no full knowledge in advance of the weights. We compare them in terms of the percentage of missions that are completed with success (the drone delivers the goods and comes back to the depot), with delivered (the drone delivers the goods but cannot come back to the depot), and with failure (the drone neither delivers the goods nor comes back to the depot).

show abstract