Dynamic network flow optimization models for air vehicle resource allocation

Nygard, Kendall E.; Chandler, Phillip; Pachter, Meir

doi:10.1109/acc.2001.946006

Cited by 108 publications

(55 citation statements)

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“…Restricting these capacities to a value of one on the arcs leading to the sink, along with the integrality property, induces binary values for the decision variables Xy. Due to the special structure of the problem, there will always be an optimal solution that is all integer [1]. Solutions to this problem pose a small computational burden, making it feasible for implementation on the processors likely to be available on disposable wide area search munitions.…”

Section: Network Optimization Modelmentioning

confidence: 99%

Task Allocation for Wide Area Search Munitions via Iterative Network Flow

Schumacher

Chandler

Rasmussen

2002

AIAA Guidance, Navigation, and Control Conference and Exhibit

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The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, DISTRIBUTION/AVAILABILITY STATEMENTApproved for public release; distribution is unlimited. SUPPLEMENTARY NOTESPresented at the AIAA Guidance Navigation and Control Conference, Monterey, CA, August 5-8, 2002. This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States. ABSTRACT (Maximum 200 Words)This paper addresses the problem of task allocation for wide area search munitions. The munitions are required to search for, classify, attack, and verify the destruction of potential targets. It is assumed that target field information is communicated between all elements of the swarm. A network flow optimization model is used to develop a linear program for optimal resource allocation. This method can be used to generate a tour of several assignments to be performed consecutively, by running the assignment iteratively and only updating the assigned task with the shortest ETA in each iteration. Periodically re-solving the overall optimization problem results in coordinated action by the search munitions. Simulation results are presented for a swarm of eight vehicles searching an area containing three potential targets. All targets are quickly serviced without using up an excessive amount of potential search time. AbstractThis paper addresses the problem of task allocation for wide area search munitions. The munitions are required to search for, classify, attack, and verify the destruction of potential targets. It is assumed that target field information is communicated between all elements of the swarm. A network flow optimization model is used to develop a linear program for optimal resource allocation. This method can be used to generate a "tour" of several assignments to be performed consecutively, by running the assignment iteratively and only updating the assigned task with the shortest ETA in each iteration. Periodically re-solving the overall optimization problem results in coordinated action by the search munitions. Simulation results are presented for a swarm of eight vehicles searching an area containing three potential targets. All targets are quickly serviced without using up an excessive amount of potential search time.

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Section: Network Optimization Modelmentioning

confidence: 99%

Task Allocation for Wide Area Search Munitions via Iterative Network Flow

Schumacher

Chandler

Rasmussen

2002

AIAA Guidance, Navigation, and Control Conference and Exhibit

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“…However, with limited resources of MSAs, the task for each MSA often covers more than one target, which cannot be interpreted as a simple end-to-end configuration problem. Meanwhile, distinct from many traditional motion-planning applications (e.g., target search [1], [2], [8], [9] and target engagement [3], [24]), the surveillance job here does not end after each target is visited. The MSAs have to come back to the targets repetitively to update their status.…”

Section: A Related Workmentioning

confidence: 99%

“…We shall use this notion with the elements representing targets. The Stirling set number can be obtained by the following equation [41]: (8) where is a binomial coefficient. Equation (8) indicates that increases exponentially as or increases, which makes the task decomposition an NP-hard problem by itself.…”

Section: A Task Decompositionmentioning

confidence: 99%

“…The Stirling set number can be obtained by the following equation [41]: (8) where is a binomial coefficient. Equation (8) indicates that increases exponentially as or increases, which makes the task decomposition an NP-hard problem by itself. Since the desired motion plan for each MSA is a traverse loop, and the ultimate goal is to have the MSAs execute their loop paths in the shortest time, it is more likely that the optimal partitioning divides the targets into clusters.…”

Section: A Task Decompositionmentioning

confidence: 99%

See 1 more Smart Citation

Motion planning for multitarget surveillance with mobile sensor agents

2005

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Abstract-In the surveillance of multiple targets by mobile sensor agents (MSAs), system performance relies greatly on the motion-control strategy of the MSAs. This paper investigates the motion-planning problem for a limited resource of MSAs in an environment of targets ( ). The kinematics of the MSA is modeled as a point mass moving at a constant speed with a bounded turning radius. Based on the fact that the track information of each target degrades over time, the motion-planning problem is formulated as an optimization problem whose objective is to minimize the average time duration between two consecutive observations of each target. In the case of a single MSA, the motion-planning problem is further interpreted so as to find a time-optimal loop path to traverse the targets. A gradient-approximation algorithm is then proposed to generate a suboptimal loop path for a mobile agent to traverse a sequence of target points. For the multi-MSA-multitarget case, a cooperative online motion-planning approach is developed.Index Terms-Cooperative motion control, mobile sensor agents (MSAs), multi-MSA-multitarget (MMMT) tracking, robot motion planning, sensor management, uninhabited air vehicles (UAVs).

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“…A longer planning horizon is essential for good performance in highly coupled systems. Iterative network flow [4], binary linear programming, and auction [5] are addressed in the paper as algorithms for multiple task assignment.…”

Section: Introductionmentioning

confidence: 99%