Modeling for UAV resource scheduling under mission synchronization

Zeng, Jia; Yang, Xin; Yang, Lingyu; Gongzhang, Shen

doi:10.3969/j.issn.1004-4132.2010.05.016

Cited by 40 publications

(21 citation statements)

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“…Research on capacitated UAVs with finite flight capability was investigated in [8][9][10][11][12]. When their fuel is exhausted, the UAVs must return to base and stay there; they do not return to the field.…”

Section: B Literature On Uav Schedulingmentioning

confidence: 99%

“…When their fuel is exhausted, the UAVs must return to base and stay there; they do not return to the field. A nonlinear mathematical model to allocate capacitated UAV resources to the battlefield was developed in [8]. A MILP based on the vehicle routing problem for capacitated UAVs, including time windows for the jobs, was investigated in [9].…”

Section: B Literature On Uav Schedulingmentioning

confidence: 99%

“…Constraint (7) guarantees that all split jobs in Ω J receive service. Constraint (8) ensures that a UAV not finish its flight at a split job. Constraint (9) prevents each UAV from finishing a flight at a start station s with index in Ω SS .…”

Section: Milp For Uav Scheduling and Designmentioning

confidence: 99%

“…The persons walk at a constant 4 km/hour. Persons 1, 2, 3, 4, 5 and 6 start to travel at times 5,8,17,20,9 and 30 minutes and finish their travel at times 17, 11, 20, 24, 14 and 33 minutes, respectively. They should be observed during their travel by a UAV.…”

Section: Modified Receding Horizon Taskmentioning

confidence: 99%

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On the concerted design and scheduling of multiple resources for persistent UAV operations

Kim¹,

Morrison²

2013

2013 International Conference on Unmanned Aircraft Systems (ICUAS)

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Figure 1. A system of three service station candidates, four UAV candidates and three tasks.  Abstract-A fleet of unmanned aerial vehicles (UAVs)supported by logistics infrastructure, such as automated service stations, may be capable of long-term persistent operations. Typically, two key stages in the deployment of such a system are resource selection and scheduling. Here, we endeavor to conduct both of these phases in concert for persistent UAV operations. We develop a mixed integer linear program (MILP) to formally describe this joint design and scheduling problem. The MILP allows UAVs to replenish their energy resources, and then return to service, using any of a number of candidate service station locations distributed throughout the field. The UAVs provide service to known deterministic customer space-time trajectories. There may be many of these customer missions occurring simultaneously in the time horizon. Each customer mission may be addressed by several UAVs. Multiple tasks may be conducted by each UAV between visits to the service stations. The MILP jointly determines the number and locations of resources (design) and their schedules to provide service to the customers. We then develop a modified receding horizon task assignment algorithm including the design problem (RHTA d ) to address the computational complexity of the MILP. Numerical experiments assess the performance of RHTA d relative to the MILP solved via CPLEX. RHTA d is substantially faster with quite acceptable loss of optimality. As such, problems of much larger size can be addressed.

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Section: B Literature On Uav Schedulingmentioning

confidence: 99%

Section: B Literature On Uav Schedulingmentioning

confidence: 99%

Section: Milp For Uav Scheduling and Designmentioning

confidence: 99%

Section: Modified Receding Horizon Taskmentioning

confidence: 99%

See 2 more Smart Citations

On the concerted design and scheduling of multiple resources for persistent UAV operations

Kim¹,

Morrison²

2013

2013 International Conference on Unmanned Aircraft Systems (ICUAS)

View full text Add to dashboard Cite

show abstract

“…This section gives a detailed summary of related studies which focus on UAV scheduling. Some researchers focused on developing the scheduling system for UAVs without considering travel time or distance restriction [50]. Authors in [50] developed a single-objective nonlinear integer programming model for solving a UAV scheduling problem which aims at allocating and maximizing the utilization of the available UAVs in an efficient manner.…”

Section: Literature Reviewmentioning

confidence: 99%

Task scheduling system for UAV operations in indoor environment

Khosiawan

Park

Moon

et al. 2018

Neural Comput & Applic

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The application of unmanned aerial vehicle (UAV) in indoor environment is emerging nowadays due to the advancements in technology. UAV brings more space flexibility in an occupied or hardly accessible indoor environment, e.g. shop floor of manufacturing industry, greenhouse, and nuclear powerplant. UAV helps in creating an autonomous manufacturing system by executing tasks with less human intervention in a time-efficient manner. Consequently, a scheduler is an essential component to be focused on; yet the number of reported studies on UAV scheduling has been minimal. This work proposes a mathematical model of the problem and a heuristic-based methodology to solve it. To suit near real-time operations, a quick response towards uncertain events and a quick creation of new high-quality feasible schedule are needed. Hence, the proposed heuristic is incorporated with particle swarm optimization algorithm to find a near optimal schedule in a short computation time. This proposed methodology is implemented into a scheduler and tested on a few scales of datasets generated based on real flight demonstrations. Performance evaluation of scheduler is discussed in detail, and the best solution obtained from a selected set of parameters is reported.

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Resource Planning for UAV Swarms Based on NSGA-II

Yao

Yang

et al. 2019

Intelligent Robotics and Applications

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Modeling for UAV resource scheduling under mission synchronization

Cited by 40 publications

References 0 publications

On the concerted design and scheduling of multiple resources for persistent UAV operations

On the concerted design and scheduling of multiple resources for persistent UAV operations

Task scheduling system for UAV operations in indoor environment

Resource Planning for UAV Swarms Based on NSGA-II

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