The SmartWheeler platform
description
Transcript of The SmartWheeler platform
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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.]
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Overall software architecture
Two primary components of cognitive robotic system:Interaction Manager and Navigation Manager
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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.
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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.]
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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.]