Where should I mount my odometry pods?

The provided documentation does not specify where odometry pods should be mounted on the robot. It does state that odometry wheels are generally sprung so that the wheel is in contact with the floor tiles at all times to ensure accuracy . Spring tensioning is highly recommended to push the wheel into the ground, ensuring constant contact and adequate traction .

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Dead Wheels

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Warning: This is a very niche aspect of design in FTC®. Generally it is something done by more experienced teams who have had time to repeatedly test their designs and mechanisms with software during the off-season.

The term dead wheels, tracking wheels, odometry pods, and odometry are often conflated in the FTC community. However, there are a few key differences one must keep in mind. Odometry is an umbrella term and refers to the general use of motion sensors for localization purposes. Meanwhile, dead wheels, tracking wheels, and odometry pods are all synonymous terms.

Term: Dead Wheel — A small unpowered wheel (usually an omni wheel) that tracks the distance the robot has traveled through the encoder attached to the wheel's axle.

Usually, there will be two or three wheels - one or two on the x and y axis each to track the front-back and left-right position relative to the starting point. Generally, odometry wheels are sprung so that the wheel is in contact with the floor tiles at all times to ensure accuracy.

Odometry refers to the use of motion sensors for localization. Localization is a means for being able to locate the position of the bot at some point in time. Localization is crucial in path following and advanced autonomous modes as one needs to know where they are to generate the necessary movements needed to reach a desired destination. Localization software plays a major role in odometry; however, in order to produce accurate results, reliable and accurate hardware design is a necessity.

The simplest form of odometry is drive encoder localization. This is the use of encoders measuring the rotation of motors that power the drive train. One is able to read the encoder data and feed it through the kinematic equation for that specific drive train to derive the body's velocity. Drive encoder localization is generally quite simple and easy to setup as almost all of the FTC legal motors have built-in encoders. Getting drive encoder localization setup is simply a matter of plugging in wires, no additional hardware needed.

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Dead Wheels > Two-Wheel Odometry Pods

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Pros:

Cheaper than 3-wheel design
Pretty good accuracy
No tuning of the heading necessary
Cons:

Subject to more drift than the 3-wheel design

Pros:

Relatively accurate tracking. Great accuracy in a 30-second autonomous mode
Cons:

Quite pricey
Tuning of the heading is very important

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Dead Wheels

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Many teams in the community have converged on a unique solution that isn't seen very much outside of FTC: the use of "dead wheels," "tracking wheels," or "odometry pods" (these terms are all synonymous). These refer to small "dead" or non-driven (not powered by a motor) wheels attached to an encoder sensor. Two or three dead wheel pods are often sprung to the ground to ensure accurate tracking. The two-wheel design utilizes one parallel and one perpendicular pod (parallel and perpendicular with respect to the drive wheel axis), measuring x and y movement respectively. Change in heading is measured via a gyroscope. The three-wheel design utilizes two parallel and one perpendicular pod, measuring x and y movement respectively. However, this design forgoes the gyroscope and instead measures heading via the difference with the two parallel wheels. This is often more accurate in the context of the FTC control system because the BNO055 IMU (used for the gyroscope in the two-wheel design) utilizes I2C which is slower than the rest of the I/O on the REV Hub and cannot be bulk read. These two issues lead to minute drift issues which can compound over time, thus leading to a more inaccurate localization system when using the two-wheel design.

However, designing consistently accurate dead wheels proves to be a difficult design challenge. It is often quite pricey. A set of three dead wheels will cost a minimum of $100 for the encoders alone, prior to any hardware.

Let's go through the advantages and disadvantages of each system.

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Odometry

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Odometry is a form of localization that uses data from sensors like encoders to derive an estimated position relative to a starting point. Localization is a means for being able to locate the position of the bot at some point in time. Odometry is especially useful in autonomous programs because it allows for easier implementation of different tasks on the field due to understanding one's position.

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Dead Wheels > Design > Spring Tensioning

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It is highly recommended that your dead wheel design includes some form of spring tensioning that pushes the wheel into the ground. This ensures that the wheel is always in contact with ground and has adequate traction. Sufficient force is required to ensure constant traction to prevent the wheels from slipping. Keep in mind that too much force may lift a light drive train off the ground and disrupt driving.

The most popular method of spring tensioning is to pivot your pod around a point and provide a rotational force via a spring or rubber band.

(Figure: FTC 14320's spring tensioning)

A much more niche option is to vertically spring odometry pods. The idea is that springing around a pivot will cause the dead wheels to move in the axis parallel to the ground if the height of the dead wheels relative to the ground changes. Vertically sprung odometry pods will not experience such an issue. However, this is not really an issue that most teams will experience. Vertically springing is much harder to design well and is not recommended for the relatively minor improvement in accuracy it yields.

(Figure: FTC 18172's vertical springing)

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Odometry > Resources for Odometry

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There are several great resources out there for odometry. We highly recommend Road Runner (https://acme-robotics.gitbook.io/road-runner/). For the math behind Road Runner (which utilizes pose exponentials), you can also read Ryan's paper (https://github.com/acmerobotics/road-runner/blob/master/doc/pdf/Mobile_Robot_Kinematics_for_FTC.pdf). An additional resource for Road Runner is Learn Road Runner (https://learnroadrunner.com/) which is a step-by-step procedural guide that explains how to work with the Road Runner quickstart (https://github.com/acmerobotics/road-runner-quickstart).

We also recommend Tyler's book (https://file.tavsys.net/control/controls-engineering-in-frc.pdf) as it goes into great detail about various controls in FIRST® robotics.

If you're using other resources, it is important that you do not use ones that utilize Euler integration as it is less optimal for real life approximations of robot pose.

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