A novel distributed scheduling algorithm for time-critical multi-agent systems

Whitbrook, Amanda; Meng, Qinggang; Chung, Pei-Jung

doi:10.1109/iros.2015.7354299

Cited by 30 publications

(32 citation statements)

References 12 publications

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“…Two particular combinatorial auctionbased algorithms lend themselves to the solution of the problems of interest in this paper -CBBA [4] and the PI algorithm [3]. It has been shown empirically that the baseline PI algorithm performs better than the baseline CBBA algorithm [3,8,26] for deterministic task allocation problems with tight task deadlines, with PI demonstrating a much better success rate with different numbers of tasks and agents, and different network topologies. However, the papers mentioned do not examine PI's handling of uncertainty.…”

Section: Auction-based Approachesmentioning

confidence: 99%

“…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%

“…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 ). During the consensus phase of the PI algorithm, see [3,8,26] and [38], where the agents reach agreement upon the task allocations, each agent is thus aware of the other agents' estimates, and can use these values when calculating the other agents' robust time costs (and hence the resulting IPI and RPI values). These metrics, along with those of the agent itself, ultimately determine the agreed schedules.…”

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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Section: Auction-based Approachesmentioning

confidence: 99%

“…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%