Task Variant Allocation in Distributed Robotics

Jose Cano Reyes, David White, Alejandro (Alex) Bordallo, Ciaran McCreesh, Patrick Prosser, Jeremy Singer, Vijayanand Nagarajan

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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%, 41% and 56% respectively.
Original languageEnglish
Title of host publicationProceedings of Robotics: Science and Systems XII 2016
Place of PublicationAnn Arbor, USA
Number of pages9
Publication statusPublished - 22 Jun 2016
EventRobotics: Science and Systems XII 2016 - Ann Arbor, United States
Duration: 18 Jun 201622 Jun 2016


ConferenceRobotics: Science and Systems XII 2016
Country/TerritoryUnited States
CityAnn Arbor
Internet address


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