Increasing allocated tasks with a time minimization algorithm for a search and rescue scenario

Turner, Joanna; Meng, Qinggang; Schaefer, Gerald

doi:10.1109/icra.2015.7139669

Cited by 14 publications

(18 citation statements)

References 14 publications

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“…We compare two different implementations of the CBBA b from [21]. The CBBA algorithm plays the benchmarking role in recent studies as the state-of-the-art decentralized task allocation algorithm [47,48]. We also compare results to the Multi-Vehicle Algorithm (MVA) [37] and to a centralized algorithm [44].…”

Section: Simulation Resultsmentioning

confidence: 99%

Heterogeneous Task Allocation and Sequencing via Decentralized Large Neighborhood Search

Sadeghi

Smith

2017

Un. Sys.

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This paper focuses on decentralized task allocation and sequencing for multiple heterogeneous robots. Each task is defined as visiting a point in a subset of the robot configuration space -this definition captures a variety of tasks including inspection and servicing. The robots are heterogeneous in that they may be subject to different differential motion constraints. Our approach is to transform the problem into a multi-vehicle generalized traveling salesman problem (GTSP). To solve the GTSP, we propose a novel decentralized implementation of large-neighborhood search (LNS). Our solution approach leverages the GTSP insertion methods proposed in Fischetti et al. [A branchand-cut algorithm for the symmetric generalized traveling salesman problem, Oper. Res. 45(3) (1997) 378-394]. to repeatedly remove and reinsert tasks from each robot path. Decentralization is achieved using combinatorial-auctions between the robots on tasks removed from robot's path. We provide bounds on the length of the dynamically feasible robot paths produced by the insertion methods. We also show that the number of bids in each combinatorial auction, a crucial factor in the runtime, scales linearly with the number of tasks. Finally, we present extensive benchmarking results to characterize both solution quality and runtime, which show improvements over existing decentralized task allocation methods.

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Section: Simulation Resultsmentioning

confidence: 99%

Heterogeneous Task Allocation and Sequencing via Decentralized Large Neighborhood Search

Sadeghi

Smith

2017

Un. Sys.

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“…The problem of interest is documented fully in [3,8,26] and [38]. It is the optimal, conflict-free assignment of a set of n heterogeneous agents V = [v 1 , .…”

Section: Problem Definitionmentioning

confidence: 99%

“…The removal performance impact (RPI) measures the benefits of removing a task from a bundle and the inclusion performance impact (IPI) measures the benefits of adding a task. Full details of the metrics and the PI algorithm are presented in [3,8,26,38]. The details are not reproduced here as, for the purposes of this paper, the reader only needs to know that there is a consensus phase, when the agents agree on the schedule, and that the RPI and the IPI are calculated using the time costs c i,k associated with each agent i and task k; the candidate robustness modules are created by using different robust versions of those costs (see Section 3.3).…”

Section: The Pi Algorithmmentioning

confidence: 99%

“…Apart from sampling and calculating the robust time costs in order to determine the RPI and IPI, the procedural details for the resulting PI-candidate algorithms are almost exactly the same as baseline PI, see [3,8,26] and [38]. However, there is another important difference between the baseline and the PI-candidate architecture; when using a candidate module, each agent i communicates its own estimate of the relevant, local uncertain parameters to the others (these include estimates of its own velocity Θ i and location Υ x,i , Υ y,i , and Υ z,i , and its estimates of all the task locations x,i , y,i , and z,i , and durations D i ).…”

Section: The Candidate Robustness Modulesmentioning

confidence: 99%

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Addressing robustness in time-critical, distributed, task allocation algorithms

Whitbrook

Meng²,

Chung³

2018

Appl Intell

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The aim of this work is to produce and test a robustness module (ROB-M) that can be generally applied to distributed, multi-agent task allocation algorithms, as robust versions of these are scarce and not well-documented in the literature. ROB-M is developed using the Performance Impact (PI) algorithm, as this has previously shown good results in deterministic trials. Different candidate versions of the module are thus bolted on to the PI algorithm and tested using two different task allocation problems under simulated uncertain conditions, and results are compared with baseline PI. It is shown that the baseline does not handle uncertainty well; the task-allocation success rate tends to decrease linearly as degree of uncertainty increases. However, when PI is run with one of the candidate robustness modules, the failure rate becomes very low for both problems, even under high simulated uncertainty, and so its architecture is adopted for ROB-M and also applied to MIT's baseline Consensus Based Bundle Algorithm (CBBA) to demonstrate its flexibility. Strong evidence is provided to show that ROB-M can work effectively with CBBA to improve performance under simulated uncertain conditions, as long as the deterministic versions of the problems can be solved with baseline CBBA. Furthermore, the use of ROB-M does not appear to increase mean task completion time in either algorithm, and only 100 Monte Carlo samples are required compared to 10,000 in MIT's robust version of the CBBA algorithm. PI with ROB-M is also tested directly against MIT's robust algorithm and demonstrates clear superiority in terms of mean numbers of solved tasks.

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“…The closest work to ours describes a Task Swap Allocation algorithm [Turner et al, 2015]. Their research concentrates on how to allocate more and more tasks, whereas our concentrates on the optimal team configuration per task.…”

Section: Literature Reviewmentioning

confidence: 99%

Distributed autonomous GIS to form teams for public safety

Nourjou

Gelernter

2015

Proceedings of the Fourth ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems

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Public safety requires emergency response that is timely and efficient. This paper describes how to distribute the emergency call among those in the area so as to optimize their locations when called, and their expertise. The call will route to the next most qualified if those in the immediate vicinity cannot come. We describe the system architecture and provide the algorithmic rules, as well as sketch an interface and propose how the completed system could be evaluated.

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Increasing allocated tasks with a time minimization algorithm for a search and rescue scenario

Abstract: Citation: TURNER, J., MENG, Q. and SCHAEFER, G., 2015. Increasing allocated tasks with a time minimization algorithm for a search and rescue scenario.

Cited by 14 publications

References 14 publications

Heterogeneous Task Allocation and Sequencing via Decentralized Large Neighborhood Search

Heterogeneous Task Allocation and Sequencing via Decentralized Large Neighborhood Search

Addressing robustness in time-critical, distributed, task allocation algorithms

Distributed autonomous GIS to form teams for public safety

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