While individual robots are becoming increasingly capable, with new sensors and actuators, the complexity of expected missions increased exponentially in comparison. To cope with this complexity, heterogeneous teams of robots have become a significant research interest in recent years. Making effective use of the robots and their unique skills in a team is challenging. Dynamic runtime conditions often make static task allocations infeasible, therefore requiring a dynamic, capability-aware allocation of tasks to team members. To this end, we propose and implement a system that allows a user to specify missions using Bheavior Trees (BTs), which can then, at runtime, be dynamically allocated to the current robot team. The system allows to statically model an individual robot's capabilities within our ros_bt_py BT framework. It offers a runtime auction system to dynamically allocate tasks to the most capable robot in the current team. The system leverages utility values and pre-conditions to ensure that the allocation improves the overall mission execution quality while preventing faulty assignments. To evaluate the system, we simulated a find-and-decontaminate mission with a team of three heterogeneous robots and analyzed the utilization and overall mission times as metrics. Our results show that our system can improve the overall effectiveness of a team while allowing for intuitive mission specification and flexibility in the team composition.

翻译：尽管单个机器人随着新型传感器和执行器的引入变得日益强大，但预期任务的复杂度却呈指数级增长。为应对这一复杂性，异构机器人团队近年来成为重要的研究方向。如何在团队中有效利用机器人及其独特技能具有挑战性。动态运行时环境常使静态任务分配不可行，因此需要一种具备能力感知的动态任务分配方法。为此，我们提出并实现了一套系统，允许用户使用行为树（BTs）定义任务，并在运行时将任务动态分配至当前机器人团队。该系统能在ros_bt_py行为树框架中静态建模单个机器人的能力，并提供运行时拍卖机制将任务动态分配给当前团队中最具能力的机器人。系统利用效用值和前置条件确保任务分配提升整体任务执行质量，同时避免错误分配。为评估该系统，我们模拟了三台异构机器人执行搜索与去污任务，并以利用率和整体任务时间作为指标进行分析。结果表明，该系统能提升团队整体效能，同时实现直观的任务定义和灵活的团队组成。