There was a shortage of entries in the tablebot competition shortly before the registration window closed for RoboGames 2023. To make sure the contest would be held, I entered a robot. Then I had to build one.
A tablebot lives on the table. There are three “phases” to the competition:
There is also an unofficial Phase IV – which is to fall off the table and survive. I did not attempt this phase.
The majority of tablebots are quite simple – a couple of sonar or IR sensors and they kind of wander around the tabletop in hopes of completing the different phases. My tablebot is decidedly different – and it paid off as the robot won the gold medal at RoboGames 2023.
The entire robot is built of 3D printed parts and random things I had on hand.
I’ve had one of those $99 LD-06 lidars sitting around for a while, and decided this was a great project to use it on. I used a Dynamixel AX-12 servo to tilt the laser so I can find the table, the cube, or the goal.
All of the code runs on an STM32, on my custom Etherbotix board which was designed for my Maxwell robot a number of years ago. The robot uses differential drive with some 30:1 12V gear motors, which were purchased from Lynxmotion in 2008 and used in various fire fighting robots over the years.
A set of small digital Sharp IR sensors are used as cliff sensors. These can be moved up or down to calibrate for different table surfaces using a pair of adjustment screws. While the sensors are very accurate and stop the robot, they don’t see far enough ahead when going at full speed, and so I also use the laser to detect when the table edge is approaching.
Phase 1 is pretty straight forward – and mostly based on dead reckoning odometry:
The movements of Phase 2 are basically the same as Phase 1 – we drive forward, staying centered with odometry. The speed is a bit lower than Phase 1 because the laser is also looking for the block:
The final phase is the most complex, but not by much. As with the earlier phases, the robot moves down the table finding the block:
All of the software for my Tablebot is availble on GitHub.
Jim Dinunzio, a member of the Homebrew Robotics Club, took a video during the actual competition at Robogames so you can actually see the winning set of runs:
To make development easier, I also wrote a Python GUI that renders the table, the robot odometry trail, the laser data, and detected goals and cubes.
Along the way I actually ran into a bug in the ARM CMSIS DSP library. I used the
arm_sin_cos_f32() function to compute my odometry:
arm_sin_cos_f32(system_state.pose_th * 57.2958f, &sin_th, &cos_th);
system_state.pose_x += cos_th * d;
system_state.pose_y += sin_th * d;
system_state.pose_th = angle_wrap(system_state.pose_th + dth);
This function takes the angle (in degrees!) and returns the sine and cosine of the angle using a lookup table and some interesting interpolation. With the visualization of the robot path, I noticed the robot odometry would occasionally jump to the side and backwards – which made no sense.
Further investigation showed that for very small negative angles,
arm_sin_cos_f32 returned huge values. I dug deeper into the code and found that there are several different versions out there:
The issue turned out to be quite simple:
With that bug fixed, odometry worked flawlessly afterwards. As it turned out, I had this same function/bug existing in some brushless motor control code at work.
It is awesome that RoboGames is back! This little robot won’t be making another appearance, but I am starting to work on a RoboMagellan robot for next year.