Embedded and real-time architecture for bio-inspired vision-based robot navigation

A recent trend in several robotics tasks is to consider vision as the primary sense to perceive the environment or to interact with humans. Therefore, vision processing becomes a central and challenging matter for the design of real-time control architectures. We follow in this paper a biological inspiration to propose a real-time and embedded control system relying on visual attention to learn specific actions in each place recognized by our robot. Faced with a performance challenge, the attentional model allows to reduce vision processing to a few regions of the visual field. However, the computational complexity of the visual chain remains an issue for a processing system embedded onto an indoor robot. That is why we propose as the first part of our system, a full-hardware architecture prototyped onto reconfigurable devices to detect salient features at the camera frequency. The second part learns continuously these features in order to implement specific robotics tasks. This neural control layer is implemented as embedded software making the robot fully autonomous from a computation point of view. The integration of such a system onto the robot enables not only to accelerate the frame rate of the visual processing, to relieve the control architecture but also to compress the data-flow at the output of the camera, thus reducing communication and energy consumption. We present in this paper the complete embedded sensorimotor architecture and the experimental setup. The presented results demonstrate its real-time behavior in vision-based navigation tasks.