Systems and Algorithms of OTTO-XL: An Autonomous Snow Removal Vehicle

Matthew Klein, Charles Hart, Buck Baskin, Roger Quinn

Abstract:	Robotic snowplows have not experienced the commercial success of their vacuum, lawnmower, and tractor counterparts. This is largely due to the unique set of challenges that a snowplow faces during operation. They are heavy and armed with a large metal plow, yet operate in close proximity with cars and humans, making sophisticated obstacle detection and avoidance systems necessary. They must also strategically move snow in precise paths to avoid redistributing the snow in areas that have already been cleared. This requires precision localization and navigation, often in urban canyons and other areas with reduced GPS availability. To this end, the ION Autonomous Snowplow Competition (ASC), and annual event in St. Paul, MN, tasks competitors with solving some of these many challenges. In this paper, we present OTTO-XL, Case Western Reserve University's entry to the 7th annual ION ASC. Attention is focused on the robot's localization strategy, which includes using an extended Kalman filter (EKF), featuring Ultra-Wideband (UWB) beacons. We also highlight the navigation system, including global path planning and local steering algorithms. Obstacle avoidance systems are also discussed. Both LIDAR and camera vision sensors were used to detect different types of obstacles, including fixed posts and a moving "stop sign" obstacle. Finally, we discuss mechanical and electrical systems, as well as safety subsystems.
Published in:	Proceedings of the 30th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2017) September 25 - 29, 2017 Oregon Convention Center Portland, Oregon
Pages:	665 - 699
Cite this article:	Klein, Matthew, Hart, Charles, Baskin, Buck, Quinn, Roger, "Systems and Algorithms of OTTO-XL: An Autonomous Snow Removal Vehicle," Proceedings of the 30th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2017), Portland, Oregon, September 2017, pp. 665-699. https://doi.org/10.33012/2017.15133
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