Enhancing Robot Task Completion Through Environment and Task Inference: A Survey from the Mobile Robot Perspective

In real-world environments, ranging from urban disastrous scenes to underground mining tunnels, autonomous mobile robots are being deployed in harsh and cluttered environments, having to deal with perception and communication issues that limit their facilitation for data sharing and coordination with other robots. In these scenarios, mobile robot inference can be used to increase spatial awareness and aid decision-making in order to complete tasks such as navigation, exploration, and mapping. This is advantageous as inference enables robots to plan with predicted information that is otherwise unobservable, thus, reducing the replanning efforts of robots by anticipating future states of both the environment and teammates during execution. While detailed reviews have explored the use of inference during human–robot interactions, to-date none have explored mobile robot inference in unknown environments and with cooperative teams. In this survey paper, we present the first extensive investigation of mobile robot inference problems in unknown environments with limited sensor and communication range and propose a new taxonomy to classify the different environment and task inference methods for single- and multi-robot systems. Furthermore, we identify the open research challenges within this emerging field and discuss future research directions to address them.