未知环境下动态任务分配的群体智能算法
Swarm Algorithms for Dynamic Task Allocation in Unknown Environments
AI总结：
针对未知环境下动态任务分配问题，提出三种群体智能算法，验证了不同任务速率下各算法的性能差异，为基于环境参数的算法优化提供参考。
摘要：
机器人群体是由大量以分布式方式运行的机器人组成的系统，在搜救、自然灾害响应和自组装等领域有诸多应用。这类应用中的不少场景都可抽象为环境中的通用任务分配问题，即机器人必须自主分配到任务并完成任务。尽管已有多种任务分配算法被提出，但大多数算法要么假设任务位置是先验已知的，要么假设任务集合是静态的。\n我们在任务会在未知位置动态出现的离散通用模型下开展研究，提出了三种全新的群体任务分配算法。我们证明，当任务出现速率较慢时，我们提出的基于任务信息传播的分布式算法变体，完成任务的效率高于Levy随机游走算法——后者是自然界中许多生物用于高效搜索环境的策略。我们还提出了一种劳动分工算法：部分智能体使用我们基于任务信息传播的算法，其余智能体则使用Levy随机游走算法。最后，我们提出了一种混合算法，每个智能体可在使用传播的任务信息和遵循Levy随机游走两种模式之间动态切换。\n我们的研究表明，劳动分工算法和混合算法的表现优于基于任务信息传播的算法和Levy游走算法，尤其是在任务速率为中低水平时。而当任务出现速率较快时，我们观察到Levy随机游走策略的表现与这些新方法相当，甚至更优。本研究阐明了这些算法在不同任务速率下的相对性能，也为根据环境参数优化我们的算法提供了思路。
英文摘要：
Robot swarms, systems of many robots that operate in a distributed fashion, have many applications in areas such as search-and-rescue, natural disaster response, and self-assembly. Several of these applications can be abstracted to the general problem of task allocation in an environment, in which robots must assign themselves to and complete tasks. While several algorithms for task allocation have been proposed, most of them assume either prior knowledge of task locations or a static set of tasks. Operating under a discrete general model where tasks dynamically appear in unknown locations, we present three new swarm algorithms for task allocation. We demonstrate that when tasks appear slowly, our variant of a distributed algorithm based on propagating task information completes tasks more efficiently than a Levy random walk algorithm, which is a strategy used by many organisms in nature to efficiently search an environment. We also propose a division of labor algorithm where some agents are using our algorithm based on propagating task information while the remaining agents are using the Levy random walk algorithm. Finally, we introduce a hybrid algorithm where each agent dynamically switches between using propagated task information and following a Levy random walk. We show that our division of labor and hybrid algorithms can perform better than both our algorithm based on propagated task information and the Levy walk algorithm, especially at low and medium task rates. When tasks appear fast, we observe the Levy random walk strategy performs as well or better when compared to these novel approaches. Our work demonstrates the relative performance of these algorithms on a variety of task rates and also provide insight into optimizing our algorithms based on environment parameters.
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