We consider the coordinated escort problem, where a decentralised team of supporting robots implicitly assist the mission of higher-value principal robots. The defining challenge is how to evaluate the effect of supporting robots' actions on the principal robots' mission. To capture this effect, we define two novel auxiliary reward functions for supporting robots called satisfaction improvement and satisfaction entropy, which computes the improvement in probability of mission success, or the uncertainty thereof. Given these reward functions, we coordinate the entire team of principal and supporting robots using decentralised cross entropy method (Dec-CEM), a new extension of CEM to multi-agent systems based on the product distribution approximation. In a simulated object avoidance scenario, our planning framework demonstrates up to two-fold improvement in task satisfaction against conventional decoupled information gathering.The significance of our results is to introduce a new family of algorithmic problems that will enable important new practical applications of heterogeneous multi-robot systems.

翻译：我们考虑协同护送问题，其中一组去中心化的辅助机器人隐式地协助价值更高的主机器人的任务。核心挑战在于如何评估辅助机器人行动对主机器人任务的影响。为捕捉这一影响，我们为辅助机器人定义了两个新颖的辅助奖励函数，称为满意度提升和满意度熵，分别计算任务成功概率的提升量或其中的不确定性。基于这些奖励函数，我们使用去中心化交叉熵方法（Dec-CEM）协调整个主机器人和辅助机器人团队，该方法是将CEM基于乘积分布近似扩展到多智能体系统的新扩展。在模拟避障场景中，我们的规划框架相较于传统的解耦信息收集方法，表现出高达两倍的任务满意度提升。我们研究成果的重要意义在于引入了一类新的算法问题，这将为异构多机器人系统的重要实际应用提供支持。