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David Brenken

PhD - McGill University
Supervisor
Audrey Sedal
Research Topics
Deep Learning
Deep Neural Networks
Molecular Modeling
Reinforcement Learning

Publications

They Hear Me Rolling: Design and Characterization of a Distributed, Rolling Acoustic-Tactile Sensor
Wilfred Mason
David Brenken
Olivier St-Martin Cormier
Audrey Sedal
Tactile sensor design has been widely explored at the centimeter-scale; fewer explorations exist in larger scale systems with varied geometr… (see more)ies. We present a meter-scale tactile sensor for wheeled robotic platforms based on a flexible acoustic waveguide. This sensor architecture performs contact sensing over the surface of a rotating wheel with a single transducer that is separated from the sensing surface. The design and characterization of the sensor are presented, along with a demonstration of a state-estimation framework using tactile sensor feedback to measure surface features.
2025-10-01
IEEE Sensors Letters (published)
doi.org
Acoustic tactile sensing for mobile robot wheels
Wilfred Mason
David Brenken
Falcon Z. Dai
Ricardo Gonzalo Cruz Castillo
Olivier St-Martin Cormier
Audrey Sedal
2024-02-28
ArXiv (preprint)
doi.org
arxiv.org
Acoustic tactile sensing for mobile robot wheels
Wilfred Mason
David Brenken
Falcon Z. Dai
Ricardo Gonzalo Cruz Castillo
Olivier St-Martin Cormier
Audrey Sedal
Tactile sensing in mobile robots remains under-explored, mainly due to challenges related to sensor integration and the complexities of dist… (see more)ributed sensing. In this work, we present a tactile sensing architecture for mobile robots based on wheel-mounted acoustic waveguides. Our sensor architecture enables tactile sensing along the entire circumference of a wheel with a single active component: an off-the-shelf acoustic rangefinder. We present findings showing that our sensor, mounted on the wheel of a mobile robot, is capable of discriminating between different terrains, detecting and classifying obstacles with different geometries, and performing collision detection via contact localization. We also present a comparison between our sensor and sensors traditionally used in mobile robots, and point to the potential for sensor fusion approaches that leverage the unique capabilities of our tactile sensing architecture. Our findings demonstrate that autonomous mobile robots can further leverage our sensor architecture for diverse mapping tasks requiring knowledge of terrain material, surface topology, and underlying structure.
2024-02-28
ArXiv (preprint)
doi.org
arxiv.org