Multi-robot belief space planning (MR-BSP) is essential for reliable and safe autonomy. While planning, each robot maintains a belief over the state of the environment and reasons how the belief would evolve in the future for different candidate actions. Yet, existing MR-BSP works have a common assumption that the beliefs of different robots are consistent at planning time. Such an assumption is often highly unrealistic, as it requires prohibitively extensive and frequent communication capabilities. In practice, each robot may have a different belief about the state of the environment. Crucially, when the beliefs of different robots are inconsistent, state-of-the-art MR-BSP approaches could result in a lack of coordination between the robots, and in general, could yield dangerous, unsafe and sub-optimal decisions. In this paper, we tackle this crucial gap. We develop a novel decentralized algorithm that is guaranteed to find a consistent joint action. For a given robot, our algorithm reasons for action preferences about 1) its local information, 2) what it perceives about the reasoning of the other robot, and 3) what it perceives about the reasoning of itself perceived by the other robot. This algorithm finds a consistent joint action whenever these steps yield the same best joint action obtained by reasoning about action preferences; otherwise, it self-triggers communication between the robots. Experimental results show efficacy of our algorithm in comparison with two baseline algorithms.

翻译：多机器人信念空间规划（MR-BSP）是实现可靠且安全自主能力的关键技术。在规划过程中，每个机器人维护对环境状态的信念，并推理在不同候选动作下信念的未来演化方式。然而，现有MR-BSP研究普遍假设不同机器人在规划时刻的信念是一致的。这一假设往往极不现实，因为它要求机器人具备成本高昂且过于频繁的通信能力。实际场景中，每个机器人可能对环境状态持有不同的信念。关键问题在于，当不同机器人的信念不一致时，现有MR-BSP方法可能导致机器人间缺乏协调，进而引发危险、不安全且次优的决策。本文针对这一关键缺口展开研究。我们提出一种新型分布式算法，能够保证找到一致的联合动作。对于给定机器人，该算法基于以下三个层面推理动作偏好：1）其自身局部信息，2）对其他机器人推理过程的感知，3）自身推理被其他机器人感知的感知结果。当上述步骤产生由动作偏好推理得出的相同最优联合动作时，本算法即可找到一致的联合动作；否则，将自动触发机器人间的通信。实验结果表明，与两种基线算法相比，本算法具有显著有效性。