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Michael Quann
Michael Quann
Ph.D. Candidate, University of Michigan
Geverifieerd e-mailadres voor umich.edu
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Off‐road ground robot path energy cost prediction through probabilistic spatial mapping
M Quann, L Ojeda, W Smith, D Rizzo, M Castanier, K Barton
Journal of Field Robotics 37 (3), 421-439, 2020
332020
An energy-efficient method for multi-robot reconnaissance in an unknown environment
M Quann, L Ojeda, W Smith, D Rizzo, M Castanier, K Barton
2017 American Control Conference (ACC), 2279-2284, 2017
212017
Chance constrained reachability in environments with spatially varying energy costs
M Quann, L Ojeda, W Smith, D Rizzo, M Castanier, K Barton
Robotics and Autonomous Systems 119, 1-12, 2019
112019
Ground robot terrain mapping and energy prediction in environments with 3-D topography
M Quann, L Ojeda, W Smith, D Rizzo, M Castanier, K Barton
2018 Annual American Control Conference (ACC), 3532-3537, 2018
92018
Power prediction for heterogeneous ground robots through spatial mapping and sharing of terrain data
M Quann, L Ojeda, W Smith, D Rizzo, M Castanier, K Barton
IEEE Robotics and Automation Letters 5 (2), 1579-1586, 2020
82020
An iterative learning control approach to multi-agent formations
M Quann, K Barton
Dynamic Systems and Control Conference 57267, V003T37A009, 2015
22015
Ground Robot Energy Prediction and Reachability in Off-Road Environments Through Spatial Terrain Mapping
M Quann
2019
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Artikelen 1–7