Service robots are required to operate in indoor environments to help humans in their daily lives. To achieve the tasks that they might be assigned, the robots must be able to autonomously model and interact with the elements in it. Even in homes, which are usually more predictable than outdoor scenarios, robot perception is an extremely challenging task. Clutter, distance and partial views complicate modelling the environment, making it essential for robots to approach the objects to perceive in order to gain favourable points of view. This article proposes a novel grammar-based distributed architecture, designed with reusability and scalability in mind, which enables robots not only to find and execute the perception-aware plans they need to achieve their goals, but also to verify that the world representation they build is valid according to a set of grammatical rules for the world model. Additionally, it describes a real-world example of use, providing qualitative results, in which a robot successfully models the room in which it is located and finds a coffee mug.
|Journal||International Journal of Advanced Robotic Systems|
|Publication status||Published - 1 Dec 2015|
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