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Assistive robots operate in complex environments and in presence of human beings, but the interaction between them can be affected by several factors, which may lead to undesired outcomes: wrong sensor readings, unexpected environmental conditions, or algorithmic errors represent just a few examples of the possible scenarios. When the safety of the user is not only an option but must be guaranteed, a feasible solution is to rely on a human-in-the-loop approach, e.g., to monitor if the robot performs a wrong action during a task execution or environmental conditions affect safety during the human-robot interaction, and provide a feedback accordingly. The present paper proposes a human-in-the-loop framework to enable safe autonomous navigation of an electric powered and sensorized (smart) wheelchair. During the wheelchair navigation towards a desired destination in an indoor scenario, possible problems (e.g. obstacles) along the trajectory cause the generation of electroencephalography (EEG) potentials when noticed by the user. These potentials can be used as additional inputs to the navigation algorithm in order to modify the trajectory planning and preserve safety. The framework has been preliminarily tested by using a wheelchair simulator implemented in ROS and Gazebo environments: EEG signals from a benchmark known in the literature were classified, passed to a custom simulation node, and made available to the navigation stack to perform obstacle avoidance.

Augmenting robot intelligence via EEG signals to avoid trajectory planning mistakes of a smart wheelchair