robots@home will provide an open mobile platform for the massive introduction of robots into
the homes of everyone. The emphasis is on providing easy-to-use modules that enable robot
navigation in everyday human environments. The breakthrough is based on three main innovations.
(1) The basic mechanical module as a scaleable mobile platform in response to the several and different application scenarios such as security, facility management, care, service and personal robotics.
(2) A dependable and embedded perception module provides a multi-modal set of sensor data for learning and mapping of the rooms and navigation through the environment.
And (3) the development of a safe and robust navigation method finally sets the case for using the platform in homes everywhere. Finally, perception, action and navigation are integrated and tested in typical home environments specified by the four industrial end users and in a furniture warehouse, e.g., IKEA. Developers as well as lay persons will show the robot around, indicate rooms and main items of furniture, give the robot time to investigate and certify the map, and then test the capabilities by commanding it to go, e.g., to the refrigerator, the dining table, or the bath tub.
It is the intended goal of the partners that the results of the robots@home project lead to a rapid introduction of the robots@home platform into commercial domestic robots and mobile service robotic systems. It will serve as an affordable, scaleable and versatile building block of robots that operate in environments presently not feasible, e.g., as depicted in Figure 1.
Fig. 1: Typical scenarios where the robots@home platform must navigate safely: table with
chairs with slanted or thin metal legs, toys, largely open balustrades, open space under a tilted roof, etc.
Technical Project Objectives
robots@home sets out to realise the necessary methods and modules for closing the existing technological gaps:
Expected new Knowledge and Industrial Innovations
The envisioned solution is inspired by recent work in cognitive science, neuroscience and animal navigation: a hierarchical cognitive map incorporates topological, metric and semantic information. It builds on structural features observed in a newly developed dependable embedded stereo vision system and complimented by time-of-flight and sonar sensors. This approach has the potential to achieve the three more and more challenging milestones, see Table below, leading up to the following industrial innovations: