Simplified FTC Odometry. Part 4: Robot Class
Phil's Simplified FTC Odometry Tutorial. Part 4: Robot Class
Link to code repo: https://github.com/gearsincorg/SimplifiedOdometry
This tutorial consists of four videos.
Part 1: Overview. An introduction to the purpose of this FTC tutorial, and some of the caveats that are assumed in it's application.
Part 2: Autonomous code. A walk-through of a sample Autonmous OpMode.
Part 3: Teleoperation code. A walk-through of a sample Telop. OpMode.
Part 4: Robot Class code. A detailed walk-through of the hardware class itself, explaining how the class works and how to adapt it to your robot.
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Note: For essential Mecanum code WITHOUT an odometry module. see this repo instead: https://github.com/gearsincorg/Essential-Mecanum
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This tutorial presents an alternative approach to FTC odometry, one that combines the ability to accurately measure how the robot is moving, with simple straight-line motions.
Lots of FTC teams (especially rookies, or teams without strong software mentorship) struggle with autonomous programming. As more teams adopt Omni (holonomic) drive trains like Mecanum and X drive, the problem has only grown. These drives don’t act predictably, often drifting off course, so it’s hard to tell exactly where a robot is going in autonomous just by watching what the driven wheels are doing.
Non-powered “follower” wheels were added to robots to measure the actual motion across the field, and so FTC Odometry was born. But now teams have to figure out how to use this new Odometry input.
If teams have access to one or more adventurous programmers, they can try using some of the more advanced Odometry tools like RoadRunner and MeepMeep. These tools treat the field as an X/Y coordinate grid, and can plan and execute complex curved motions. But these tools have steep learning curves, and rely on some sophisticated testing, so they don’t always lead to success.
I think something simpler is needed, so this tutorial is going to take a different approach.
Video Time Codes:
00:00 Title.
Part 1: Overview. https://youtu.be/lpVG2Pl6RGY
Part 2: Autonomous. https://youtu.be/EV7n9rGcxF4
Part 3: Teleoperation. https://youtu.be/X7RyAMbP240
Part 4: Robot Class. https://youtu.be/eWzGW7ZcPh8
Видео Simplified FTC Odometry. Part 4: Robot Class канала GEARSinc
Link to code repo: https://github.com/gearsincorg/SimplifiedOdometry
This tutorial consists of four videos.
Part 1: Overview. An introduction to the purpose of this FTC tutorial, and some of the caveats that are assumed in it's application.
Part 2: Autonomous code. A walk-through of a sample Autonmous OpMode.
Part 3: Teleoperation code. A walk-through of a sample Telop. OpMode.
Part 4: Robot Class code. A detailed walk-through of the hardware class itself, explaining how the class works and how to adapt it to your robot.
----------------------------------------------------------------------------------------------------------------------------------------
Note: For essential Mecanum code WITHOUT an odometry module. see this repo instead: https://github.com/gearsincorg/Essential-Mecanum
----------------------------------------------------------------------------------------------------------------------------------------
This tutorial presents an alternative approach to FTC odometry, one that combines the ability to accurately measure how the robot is moving, with simple straight-line motions.
Lots of FTC teams (especially rookies, or teams without strong software mentorship) struggle with autonomous programming. As more teams adopt Omni (holonomic) drive trains like Mecanum and X drive, the problem has only grown. These drives don’t act predictably, often drifting off course, so it’s hard to tell exactly where a robot is going in autonomous just by watching what the driven wheels are doing.
Non-powered “follower” wheels were added to robots to measure the actual motion across the field, and so FTC Odometry was born. But now teams have to figure out how to use this new Odometry input.
If teams have access to one or more adventurous programmers, they can try using some of the more advanced Odometry tools like RoadRunner and MeepMeep. These tools treat the field as an X/Y coordinate grid, and can plan and execute complex curved motions. But these tools have steep learning curves, and rely on some sophisticated testing, so they don’t always lead to success.
I think something simpler is needed, so this tutorial is going to take a different approach.
Video Time Codes:
00:00 Title.
Part 1: Overview. https://youtu.be/lpVG2Pl6RGY
Part 2: Autonomous. https://youtu.be/EV7n9rGcxF4
Part 3: Teleoperation. https://youtu.be/X7RyAMbP240
Part 4: Robot Class. https://youtu.be/eWzGW7ZcPh8
Видео Simplified FTC Odometry. Part 4: Robot Class канала GEARSinc
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