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

Aaron Hao Tan, Goldie Nejat

Abstract:

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.