LAUSR

Online decisionmaking is an important part of robotics problems in which mobile robots operate in unknown or partially known dynamic environments with the goal of acquiring information about some studied phenomena. This can be found in problems such as autonomous data collection, environmental monitoring, and robotic exploration missions that can be considered as variants of robotic information gathering. The key aspect of these problems is that the overall mission performance can only be evaluated after the mission is completed; however, the choice of which action to take at any time depends on local in-situ conditions that vary according to the information acquired during the mission. This special issue aims at presenting the state-of-the-art in approaches to online decision making for coordinating a team of mobile robots to fulfill a global mission objective through the individual actions of each robot. The particular focus is on missions such as multi-robot exploration, persistent environmental monitoring, and adaptive information gathering. The fundamental challenge of these missions is that little or no information about the environment is known in advance. Therefore, one of the problems that must be addressed is how to trade-off exploration of the unknown parts of the environment to collect new information about the operational environment, and exploitation of the current knowledge acquired so far to improve the mission performance.