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.

中文翻译：

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解决分布式机器人中的任务变量分配问题。

我们考虑在分布式机器人环境中将软件进程（或任务）分配给硬件处理器的问题。我们引入了任务变体的概念，它支持软件适应特定的硬件配置。任务变体促进了功能质量与目标执行处理器所需容量和类型之间的权衡。我们将向处理器分配任务变体的问题形式化为数学模型，该模型结合了机器人应用中的典型约束；该模型是多目标、多维、多项选择背包问题的约束形式。我们提出并评估了该问题的三种不同解决方法：约束规划、构造性贪婪启发式和局部搜索元启发式。此外，我们在分布式交互式多智能体导航系统的真实实例中演示了任务变体的使用，表明与本地搜索元启发式、贪婪算法相比，我们的最佳解决方案方法（约束编程）提高了系统的服务质量。启发式和随机解决方案的平均分别为 16%、31% 和 56%。