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2018 Competition Event and Results

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Each year, teams come up with unique vehicle designs to match the objectives of the competition. This 2018 competition event year has some well established teams, and some very new teams. Each team has prepared a vehicle concept that is anticipated to win the grand prize of the event.

Below are descriptions provided by the teams of their vehicle concept, presenting the unique features and elements of their design that make their concept a winner.

2018 Teams and Their Vehicle Design Approach

Case Western Reserve University (“OTTO XL”): A differential drive robot that uses a beacon system, an Inertial Measurement Unit (IMU), and wheel encoders to localize itself within the arena, and uses a combination of cameras and a LIDAR for obstacle detection and identification. Path planning combines predefined waypoints with dynamic obstacle avoidance behaviors.

Sponsor: MTD, Inc.

Case Western Reserve University (“Sno Joke”): A snowplow vehicle that has four-wheel skid steer platform with 24V gear motors, driven by low-cost embedded electronics. An active beacon system allows the robot to determine its position, and inertial measurements allow the estimation of its orientation. The robot has been programmed to enjoy cold weather.

Sponsor: MTD, Inc.

Dunwoody College of Technology (“Snow Devils 10002”): The 2018 Snow Devils Team utilizes a two-wheel drive chassis and magnetic strip navigation system. The goal this year will be to interface an Allen Bradley PLC controller to both the magnetic sensor and RoboTeq motor controller. This will allow more team members to take part in code development and debugging. Additionally a secondary ultra-sonic sensing system is planned for moving obstacle detection.

Sponsor: Dunwoody College of Technology

Dunwoody College of Technology (“Wendigo 2018”): The 2018 Wendigo Team utilizes a four-wheel drive chassis that weighs in at approximately 1500 pounds. The goal this year will be to interface an Allen Bradley PLC controller to a vision-based navigation system and obstacle detection sensors. This will allow more team members to take part in code development and debugging.

Sponsor: Dunwoody College of Technology

Iowa State University Robotics Club (“Iowa State University Cyplow”): The snowplow vehicle is a skid steer robot with a computer vision system to detect obstacles, and a secondary system on the side of the field to perform localization with OpenCV’s Aruco module.

Sponsor: Iowa State University

Marquette University (“Arnold”): A hydraulically powered vehicle with all wheel drive, skid-steering, UTV tires and fixed angle UTV plow. Vehicle is powered by a 35 hp internal combustion engine and weighs approximately 600 pounds.

Sponsors: Douglas Dynamics, Fluid Power Institute, Briggs & Stratton

New Jersey Institute of Technology (“Snobot”): A rotating auger collects the snow into a centralized heating chamber which then liquefies the snow until it is a fluid and then pumped out to a drain or external location.

Sponsors: New Jersey Institute of Technology

North Dakota State University (“Thundar 3.0”): An approximately 300 pound autonomous skid steered snowplow robot with actuated plow motors to control pitch and elevation of the plow. There is a SICK LiDAR sensor for comprehensive obstacle detection at the front of the robot. Positioning and localization are achieved by running a Kalman filter of odometrically processed data from the LiDAR, wheel encoders, and Inertial Measurement Unit (IMU), along with GPS coordinates. Navigation is done through a path planner subsystem of the autonomous software. Software has a Game Evaluator for high level decision making

Sponsors: National Science Foundation, NDSU Mechanical Engineering Department

Samuel O’Blenes (“Plowerwheels”): A differential drive vehicle based on a Power Wheels Wild Thing chassis. The vehicle relies on Ultra-Wide Band for localization and LiDAR for obstacle avoidance.

Sponsors: Self-sponsored

University of Michigan, Dearborn (“Yeti 8.0”): An autonomous vehicle which uses a LiDAR and a camera for vision and obstacle detection. Localization of the robot is achieved using a LiDAR assisted by a set of landmarks. The robot uses preplanned waypoints to navigate across the course.

Sponsors: University of Michigan Dearborn College of Engineering and Computer Science, SolidWorks

University of Minnesota, Twin Cities (“Snow Squirrel”): A compact, track driven vehicle with a steel base and plow, aluminum frame, plastic body panels, and the ability to plow snow by autonomously mapping and navigating an environment. It does this by taking in data on its surroundings with a LiDAR and a camera, and translating them into an optimal path for plowing.

Sponsors: University of Minnesota College of Science and Engineering, University of Minnesota Twin Cities Student Services Fees, Honda Town