A role allocation method for cooperative agents in robotic soccer games

Pei-Chuan Zhou, Alan Liu
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Abstract

This research describes a dynamic role allocation and fast formation positioning method in the multi-agent robot soccer domain. The optimal role assignment is a well-known problem from operations research, and the problem-solving techniques include the simplex method and the Hungarian method. The goal of those methods is to calculate the running time of positioning and to minimize the sum of all costs. However, the computing concept is different from the practical positioning, because the consuming time of practical positioning is determined by the time it takes for the last agent to reach its target position. In the robot soccer, both of role allocation and formation positioning must be performed in real time, so that robots can be effective in a dynamic environment. Therefore, we propose a new method with dynamic role assignment mechanism to find a near-optimal solution. The proposed method produces a set of corresponding combination quickly, and it is an acceptable approximate solution for the problem. We also provide time complexity analysis of our algorithms in this study, and the evaluation indicates that our method is much faster to complete the role assignment than dynamic programming approaches. Our experiment shows that the output of one-to-one mapping between agents and roles through the initial role allocation and re-allocation. Furthermore, in a robotic soccer game the number of available robots varies as a result of several situations such as hardware or software failures and penalties. Hence, we also use different priorities for the different roles and positions, so that the most important ones will always be played to fulfill the obligations.
机器人足球比赛中协作智能体角色分配方法
研究了一种多智能体机器人足球领域的动态角色分配和快速队形定位方法。最优角色分配是运筹学中一个著名的问题,求解方法主要有单纯形法和匈牙利法。这些方法的目标是计算定位的运行时间,并使所有成本的总和最小。然而,计算概念与实际定位不同,因为实际定位的消耗时间是由最后一个agent到达目标位置所需的时间决定的。在机器人足球运动中,角色分配和队形定位都必须是实时的,这样机器人才能在动态环境中发挥作用。因此,我们提出了一种基于动态角色分配机制的近似最优解求解方法。该方法能快速生成一组对应的组合,是该问题的一种可接受的近似解。我们还对我们的算法进行了时间复杂度分析,评估表明我们的方法比动态规划方法更快地完成角色分配。我们的实验表明,通过初始角色分配和重新分配,可以输出agent和角色之间的一对一映射。此外,在机器人足球比赛中,可用机器人的数量因硬件或软件故障和处罚等几种情况而变化。因此,对于不同的角色和职位,我们也会使用不同的优先级,以便始终发挥最重要的作用来履行义务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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