And they're off
The team from Zhejiang University traveled halfway around the world to be at the competition and were one of the first teams to get set up. The Zhejiang team was Chaoyue Xu, Siyu Chen, Zhenghao Fei, and Zhonghua Zheng.
Here, the students put their robots on the course for a test run to work out any bugs caused during their long trip overseas.
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For this competition, pegs are inserted in holes on the board to represent the maple trees. The black lines signify the path.
Zhe Jiang University met the robotics challenge using two robots. One was the Pathfinder, which is a scanning robot to spot the position of every tree and transfer that information to the controllers. (You can see a video clip here)
How robots see
Robots can’t run without eyes. Robots were equipped with laser and distance sensors to navigate the path without knocking over the trees. Sensors detect the location relative to each tree and navigate the robot and the trees.
Kansas State University
This team has won this ASABE Robotics Competition eight years in a row, including (SPOILER ALERT) this one.
If/then and a whole lot of 11001011100100010110
Lines of computer code make these robots work, delivering commands using a desktop computer and wireless communication.
Microcontrollers, like this Arduino Mega, controls different types of sensors and actuators while communicating with other microcrontrollers. It is used to monitor all the sensors, send driving orders and control the grabber arms and Velcro dispensers.
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Laser ranger finder
The first action of K-State’s robot was to rotate a laser range finder, shown here. A sensor measures the distance from the trees to the laser. That data is then sent to the base station, where the tree locations and the minimum path are calculated. Distance sensors and line tracking sensors are also used to navigate.
Radio me in
These radio modules by XBee enable are used to send information back and forth between the microcontrollers and computer base station. Information about the robot location, tree location and robot direction is sent from the robot to the base station for routing and decision making.
Don't mess with Texas
Next up . . . Texas A&M (left to right): Mario Mendez, Walter Oosthuizen, Jose Batz, Victoria Garibay, Dr. Thomasson, missing Sipu Pan.
The team advisor Alex Thomasson states:
"The team focused on accomplishing the mechanical requirements of the competition. Specifically, the feat of affixing some type of string to the “trees” on the course and having them remain there while the robot carries the string to the next tree and the next, was very challenging. Design considerations involved selecting optimal string material and determining a method of applying the string to the tree. Many hours were spent perfecting these aspects of the robot’s performance."
180 degree rotation
Team leader Jose Batz says:
"We wanted to have a simple design, yet efficient. Our robot design consisted of small sized vehicle with an arm attached on top. The arm had two degrees of motion; allowing for movement of the arm from left to right and the tip to rotate 180 degrees. This design ensured maximum area of contact between the yarn fed through the arm and the Velcro attached to the trees."
Knitting up a winner
In addition, a roller at the tip of arm and a spring was placed on the arm so that the arm would arch towards the dowel, rather than only a single point of contact. The string material was regular yarn.
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Last up, McGill
Bharath Sudarsan and Trevor Stanhope, students at McGill University, were the minds behind the 2014 Robotics Challenge. They also participated in the challenge with their Maple Autonomous Pathfinder.
Their Maple Autonomous Pathfinder consisted of two scouts that did the positioning and mapping; a server used to exchange data and estimate the shortest path; and a spooler to interconnect the trees with sap lines.
Wrap it up
Here’s a picture of the spooler that hooks up the sap lines.
Computer vision is used to find and navigate the trees.
Stereo vision is used to simulate depth, and . . .
. . . magnetic sensors guide the robot’s orientation.
And the winner is. . .
All teams were judged on the number of trees that were connected by the sap line; a formal written report; and a formal presentation, among other factors. The team that racked up the most points was Kansas State University, recognized as the winner of the competition for the 8th year in a row. Stay tuned for this year’s challenge, which takes place this July in New Orleans