The SmartWheeler platform• Collaboration between McGill, U.Montreal, Ecole

Polytechnique Montreal + 2 clinical rehab centers.

• Standard commercial power wheelchair with onboard computer and custom-made electronics.

• Sensors: laser range-finders, sonars, RGB-D camera (Kinect), wheel odometers.

• Communication: 2-way voice, touch-sensitive LCD, wireless.

[Honoré et al., RESNA 2010.]

Overall software architecture

Two primary components of cognitive robotic system:Interaction Manager and Navigation Manager

Current system:classical mapping path planning

Obstacle detection:Fusion of laser, sonar and RBG-D data to detect standard obstacles, including glass walls.Next challenge is to detect “negative” obstacles (holes, descending ramp)

Mapping and localization:Particle filtering SLAM algorithms, using laser data, integrated in ROS.

Local control:Pre-programmed behaviors for simple tasks (wall following, door traversal)

Global path planning:Deterministic search algorithm to achieve point-to-point navigation in discretized state space.

Ongoing research:Socially adaptive navigation

Approach:

- Extract information about dynamic obstacles from RBG-D camera.

- Acquire training data containing trajectories of driving in crowds from a human expert.

- Apply machine learning methods on the training data to learn a policy that matches dynamic features to control actions.

[Kim and Pineau., RSS 2013.]

Speech-based user interaction

Using a Partially Observable Markov Decision Process (POMDP) to model the dialogue interaction and select the robot response (right column).

[Atrash et al., Int.J. Social Robotics. 2009.]