This paper considers the problem of managing single or multiple robots and proposes a cloud-based robot fleet manager, Adaptive Goal Management (AGM) System, for teams of unmanned mobile robots. The AGM system uses an adaptive goal execution approach and provides a restful API for communication between single or multiple robots, enabling real-time monitoring and control. The overarching goal of AGM is to coordinate single or multiple robots to productively complete tasks in an environment. There are some existing works that provide various solutions for managing single or multiple robots, but the proposed AGM system is designed to be adaptable and scalable, making it suitable for managing multiple heterogeneous robots in diverse environments with dynamic changes. The proposed AGM system presents a versatile and efficient solution for managing single or multiple robots across multiple industries, such as healthcare, agriculture, airports, manufacturing, and logistics. By enhancing the capabilities of these robots and enabling seamless task execution, the AGM system offers a powerful tool for facilitating complex operations. The effectiveness of the proposed AGM system is demonstrated through simulation experiments in diverse environments using ROS1 with Gazebo. The results show that the AGM system efficiently manages the allocated tasks and missions. Tests conducted in the manufacturing industry have shown promising results in task and mission management for both a single Mobile Industrial Robot and multiple Turtlebot3 robots. To provide further insights, a supplementary video showcasing the experiments can be found at https://github.com/mukmalone/ AdaptiveGoalManagement.

翻译：本文探讨了单个或多个机器人的管理问题，并提出了一种基于云的机器人集群管理系统——自适应目标管理（AGM）系统，适用于无人移动机器人团队。AGM系统采用自适应目标执行方法，并提供面向RESTful API的通信接口，支持单个或多个机器人间的实时监控与控制。AGM的核心目标是协调单个或多个机器人在环境中高效完成任务。现有研究虽已提出多种机器人管理方案，但本文提出的AGM系统具备自适应性与可扩展性，特别适用于管理动态变化环境下多种异构机器人。该系统为医疗健康、农业、机场、制造业和物流等多个行业中的单个或多个机器人管理提供了一种通用高效的解决方案。通过增强机器人能力并实现无缝任务执行，AGM系统为复杂操作提供了强大工具。在ROS1结合Gazebo的仿真实验中，AGM系统在不同环境下展现了有效性，结果证明其能高效管理分配的任务与指令。在制造业场景中，对单个移动工业机器人和多个Turtlebot3机器人的测试显示，该系统在任务与指令管理方面表现优异。更多实验细节可参阅配套视频（链接：https://github.com/mukmalone/ AdaptiveGoalManagement）。