In situations involving teams of diverse robots, assigning appropriate roles to each robot and evaluating their performance is crucial. These roles define the specific characteristics of a robot within a given context. The stream actions exhibited by a robot based on its assigned role are referred to as the process role. Our research addresses the depiction of process roles using a multivariate probabilistic function. The main aim of this study is to develop a role engine for collaborative multi-robot systems and optimize the behavior of the robots. The role engine is designed to assign suitable roles to each robot, generate approximately optimal process roles, update them on time, and identify instances of robot malfunction or trigger replanning when necessary. The environment considered is dynamic, involving obstacles and other agents. The role engine operates hybrid, with central initiation and decentralized action, and assigns unlabeled roles to agents. We employ the Gaussian Process (GP) inference method to optimize process roles based on local constraints and constraints related to other agents. Furthermore, we propose an innovative approach that utilizes the environment's skeleton to address initialization and feasibility evaluation challenges. We successfully demonstrated the proposed approach's feasibility, and efficiency through simulation studies and real-world experiments involving diverse mobile robots.

翻译：在涉及多种异构机器人的场景中，为每台机器人分配适当角色并评估其性能至关重要。这些角色定义了特定环境下机器人的具体特征。机器人基于所分配角色表现出的流式行为被称为过程角色。本研究采用多元概率函数描述过程角色，主要目标是开发面向协作多机器人系统的角色引擎，并优化机器人的行为。该角色引擎旨在为每台机器人分配合适角色，生成近似最优的过程角色并及时更新，同时识别机器人故障实例或在必要时触发重规划。所考虑的环境是动态的，包含障碍物和其他智能体。角色引擎采用混合运行模式——中心化启动与去中心化行动相结合——并为智能体分配未标记角色。我们采用高斯过程（GP）推断方法，基于局部约束及与其他智能体相关的约束来优化过程角色。此外，我们提出一种创新方法，利用环境骨架来解决初始化和可行性评估难题。通过涉及多种移动机器人的仿真研究和实际实验，我们成功证明了所提出方法的可行性与效率。