Solving the task variant allocation problem in distributed robotics

Cano, J. , White, D. R., Bordallo, A., McCreesh, C. , Michala, A. L. , Singer, J. and Nagarajan, V. (2018) Solving the task variant allocation problem in distributed robotics. Autonomous Robots, 42(7), pp. 1477-1495. (doi: 10.1007/s10514-018-9742-5)

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We consider the problem of assigning software processes (or tasks) to hardware processors in distributed robotics environments. We introduce the notion of a task variant, which supports the adaptation of software to specific hardware configurations. Task variants facilitate the trade-off of functional quality versus the requisite capacity and type of target execution processors. We formalise the problem of assigning task variants to processors as a mathematical model that incorporates typical constraints found in robotics applications; the model is a constrained form of a multi-objective, multi-dimensional, multiple-choice knapsack problem. We propose and evaluate three different solution methods to the problem: constraint programming, a constructive greedy heuristic and a local search metaheuristic. Furthermore, we demonstrate the use of task variants in a real instance of a distributed interactive multi-agent navigation system, showing that our best solution method (constraint programming) improves the system’s quality of service, as compared to the local search metaheuristic, the greedy heuristic and a randomised solution, by an average of 16, 31 and 56% respectively.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Cano Reyes, Dr Jose and White, Dr David and Michala, Dr Lito and Mccreesh, Dr Ciaran and Singer, Dr Jeremy
Authors: Cano, J., White, D. R., Bordallo, A., McCreesh, C., Michala, A. L., Singer, J., and Nagarajan, V.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Autonomous Robots
ISSN (Online):1573-7527
Published Online:25 April 2018
Copyright Holders:Copyright © 2018 The Authors
First Published:First published in Autonomous Robots 42(7):1477-1495
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
614231AnyScale ApplicationsJeremy SingerEngineering and Physical Sciences Research Council (EPSRC)EP/L000725/1COM - COMPUTING SCIENCE
608951Engineering and Physical Sciences Doctoral Training Grant 2012-16Mary Beth KneafseyEngineering and Physical Sciences Research Council (EPSRC)EP/K503058/1VPO VICE PRINCIPAL RESEARCH & ENTERPRISE