This year is the first time we introduced the Line Following and Maze Solving challenges, and it has been exciting, difficult, rewarding and intense. Both challenges are programming-intensive, but they still require students to make robots with structural integrity.
The first part of each challenge is just to solve the problem: follow the black line without deviation from start to finish, and navigate the maze from start to finish. For those who could solve the problem, the next challenge was seeing who could do it the most quickly.
As we have learned, after first creating a functioning robot it's important to make small changes to the physical form and/or the program to improve its overall performance. Taking a systematic approach, making changes in measured increments and recording the variations in performance will lead to the best iteration of a particular robot. In self-help terms, we want our robots to "become their best selves." In both these challenges the students had to analyze the interplay between varying levels of speed and different degrees of accuracy. They found that the faster the robot goes, the less accurate it is, but the more accurate it is, the slower it goes.
For the line following 'bots, everyone used the light sensor to follow the black line. Some used the sensor to locate the color black, while others used the sensor to measure reflected light intensity (RLI). The maze solving 'bots had a lot more variety in their sensors and programming ideas. Many used an ultrasonic sensor to follow along the side of a wall and a touch sensor to recognize dead ends, while others attempted to solve the maze with dead reckoning or a series of true/false (Boolean) statements.
In an exciting twist to the maze competition at BMS, one robot solved the challenge and did multiple trials with slight changes, getting better and better each time. Another robot solved the maze once, and the team went back to the drawing board, frantically making changes until the last possible minute. Their second attempt was more than 2.5 times faster than their first with these few key changes.