Human-Like Context Sensing for Robot Surveillance

Abstract

Robot surveillance requires robots to make sense of what is happening around them, which is what humans do with contexts. This is critical when the robots have to interact with people. Thus, the main issue is how to model human-like context to be mapped to robots, so that they can mirror human understanding. We propose a context model, organized according to the different dimensions of the environment. We then introduce the notions of endurants and perdurants to account for how space and time, respectively, aggregate context for humans. To map real-world data, i.e. sensory inputs, to our context model, we propose a system capable of managing both the robots sensors and interacting with sensors from other devices. The proposed use case is a robot, using the system fusing sensory inputs and the context model, patrolling an university building.