Planning under uncertainty is a fundamental challenge in robotics. For multi-robot teams, the challenge is further exacerbated, since the planning problem can quickly become computationally intractable as the number of robots increase. In this paper, we propose a novel approach for planning under uncertainty using heterogeneous multi-robot teams. In particular, we leverage the notion of a dynamic topological graph and mixed-integer programming to generate multi-robot plans that deploy fast scout team members to reduce uncertainty about the environment. We test our approach in a number of representative scenarios where the robot team must move through an environment while minimizing detection in the presence of uncertain observer positions. We demonstrate that our approach is sufficiently computationally tractable for real-time re-planning in changing environments, can improve performance in the presence of imperfect information, and can be adjusted to accommodate different risk profiles.

翻译：不确定性下的规划是机器人领域的基本挑战。对于多机器人团队而言，随着机器人数量增加，规划问题可能迅速变得计算上难以处理，使得这一挑战进一步加剧。本文提出了一种利用异构多机器人团队在不确定性下进行规划的新方法。具体而言，我们利用动态拓扑图的概念和混合整数规划，生成能够部署快速侦察团队成员以减少环境不确定性的多机器人计划。我们在多个具有代表性的场景中测试了该方法，这些场景中机器人团队必须在存在不确定观测者位置的情况下穿越环境，同时最小化被探测的概率。实验结果表明，我们的方法具有足够的计算可处理性以实现动态环境中的实时重新规划，能够在信息不完善的情况下提升表现，并且可以根据不同的风险特征进行调整。