Robotics: Science and Systems XII

Task Variant Allocation in Distributed Robotics

José Cano, David R. White, Alejandro Bordallo, Ciaran McCreesh, Patrick Prosser, Jeremy Singer, Vijay Nagarajan


We consider the problem of assigning software pro- cesses (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 con- figurations. 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 con- straints 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 in- stance 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.



    AUTHOR    = {José Cano AND David R. White AND Alejandro Bordallo AND Ciaran McCreesh AND Patrick Prosser AND Jeremy Singer AND Vijay Nagarajan}, 
    TITLE     = {Task Variant Allocation in Distributed Robotics}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2016}, 
    ADDRESS   = {AnnArbor, Michigan}, 
    MONTH     = {June}, 
    DOI       = {10.15607/RSS.2016.XII.045}