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.

移动机器人代理通常遭受随时间和移动增长的定位不确定性，这可能妨碍其完成任务。在某些情况下，可以执行辅助操作来减少对机器人位置的不确定性。为了支持对代理进行决策以及决定在团队中帮助哪个代理，有必要提出有原则的方法。为此，我们提出了协助价值 (VOA) 来表示在执行的某一点上协助将产生的预期成本降低。我们提供了基于高斯过程模型机器人未来不确定性估计的计算VOA的方法。我们指定了VOA测量值有效的条件，并在模拟和实际机器人环境中实证了我们测量值预测代理接收协助时的平均成本降低能力。

移动代理的协助价值