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Self-supervised learning of motion-induced acoustic noise awareness in social robots

Andrade João, Pedro Santana, Almeida Alexandre P.

Journal of Automation Mobile Robotics and Intelligent Systems

2019

Vol. 13, No. 1

3--14

With the growing presence of robots in human populated environments, it becomes necessary to render their presence natural, rather than invasive. To do that, robots need to make sure the acoustic noise induced by their motion does not disturb people nearby. In this line, this paper proposes a method that allows the robot to learn how to control the amount of noise it produces, taking into account the environmental context and the robot’s mechanical characteristics. Concretely, the robot adapts its motion to a speed that allows it to produce less noise than the environment’s background noise and, hence, avoiding to disturb nearby humans. For that, before executing any given task in the environment, the robot learns how much acoustic noise it produces at different speeds in that environment by gathering acoustic informatinon through a microphone. The proposed method was successfully validated on various environments with various background noises. In addition, a PIR sensor was installed on the robot in order to test the robot’s ability to trigger the noise-aware speed control procedure when a person enters the sensor’s field of view. The use of a such a simple sensor aims at demonstrating the ability of the proposed system to be deployed in minimalistic robots, such as micro unmanned aerial vehicles.