Mobile robotic agents often suffer from localization uncertainty which grows with time and with the agents' movement. This can hinder their ability to accomplish their task. In some settings, it may be possible to perform assistive actions that reduce uncertainty about a robot's location. For example, in a collaborative multi-robot system, a wheeled robot can request assistance from a drone that can fly to its estimated location and reveal its exact location on the map or accompany it to its intended location. Since assistance may be costly and limited, and may be requested by different members of a team, there is a need for principled ways to support the decision of which assistance to provide to an agent and when, as well as to decide which agent to help within a team. For this purpose, we propose Value of Assistance (VOA) to represent the expected cost reduction that assistance will yield at a given point of execution. We offer ways to compute VOA based on estimations of the robot's future uncertainty, modeled as a Gaussian process. We specify conditions under which our VOA measures are valid and empirically demonstrate the ability of our measures to predict the agent's average cost reduction when receiving assistance in both simulated and real-world robotic settings.

翻译：移动机器人智能体常面临随时间和运动而增长的定位不确定性，这会影响其任务执行能力。在某些场景中，可通过执行辅助行动来降低机器人位置的不确定性。例如，在多机器人协作系统中，轮式机器人可请求无人机飞抵其估计位置，在地图上精确标定其实际位置或引导其抵达目标位置。由于辅助行动可能代价高昂且资源有限，且团队成员均可发起请求，因此需要建立系统性方法，以决定何时为哪个智能体提供何种协助。为此，我们提出"协助价值"（VOA）概念，用以表示在特定执行时刻提供协助所能带来的预期成本降低。我们基于高斯过程建模的机器人未来不确定性估计，给出了VOA的计算方法，并明确了VOA度量的有效性条件。通过仿真与真实机器人场景实验，我们经验性地证明了所提度量能够有效预测智能体接受协助后的平均成本降低。