Metadata Overview

Main design files: https://github.com/unl-cchil/canine_treat_dispenser.

Target group: Scientists in canine cognition.

Skills required: Desktop 3D printing, surface mount electronics assembly.

Replication: No builds known to the authors so far.

See section “Build Details” for more detail.

(1) Overview

Introduction

Operant conditioning is a critical tool used to train pet dogs (McKinley and Young 2003). In particular, positive reinforcement involves providing a reward in the presence of a behavior to increase the expression of that behavior (McKinley and Young 2003). Clicker training is a classic example of employing operant conditioning in dogs, where a command is given and, if the dog expresses the correct behavior (or an approximation of that behavior), the dog handler presses the clicker and follows up with a food treat or other reward. These forms of operant conditioning are often used in training obedience and in reducing problem behaviors (Reisner 2016).

Operant conditioning is also used by researchers interested in studying aspects of dog cognition (McKinley and Young 2003). Clicker training, however, is often not ideal for these studies because the clicker and food dispensing must be implemented by a person, which may distract the dog from the cognitive task. For that reason, studies of dog cognition often use automated treat dispensers that are triggered by the researchers, sometimes with remote controls. For example, canine cognition can be tested by dispensing a treat when the animal subject touches a visual indicator on a screen (see demonstration video at https://www.youtube.com/watch?v=veKvqE5ipu4). This allows researchers to present a range of different stimuli to the dogs, record their responses, and reinforce the responses with food treats.

The PetSafe Treat & Train Manners Minder Remote Reward Dog Trainer (PetSafe 2020) is a commercial treat dispenser for training pet dogs at home using operant conditioning techniques. A user can dispense treats using a radio frequency (RF) remote control. The Treat & Train (PetSafe 2020) is a commonly used treat dispenser for canine operant conditioning, and it is generally controlled using its RF remote, dispensing a single treat per button press (Hiby et al. 2004, Yin et al. 2008, Hardin et al. 2015, Protopopova et al. 2016, Majikes 2017, Wallis et al. 2017, Edwards 2019). However, the inconsistencies in the human operating the remote, as well as the remote’s range, can limit the effectiveness of the treat dispenser. There have been instances of these dispensers being retrofitted to automatically dispense treats, but there have not been any published examples of the method to do so (Wallis et al. 2017). There have, however, been published builds of food-dispensing devices for other species, such as fruit flies (Wayland et al. 2018).

Our aim here is to describe the construction, operation, and reliability of a dispenser that not only is directly controlled by a computer (rather than a remote control) but is a self-contained experimenting system when a Raspberry Pi is integrated into the Treat & Train. To do this, we created a custom-printed circuit board to drive the dispenser.

We integrated a Raspberry Pi 4 single-board computer (Raspberry Pi Foundation 2020) and our custom-printed circuit board into the existing Treat & Train (PetSafe 2020) dispenser to create a computer-controlled adaptation of this commonly used device. Our creation allows stimulus generation using common experiment generation software such as PsychoPy (Peirce 2007), controls reward delivery, and performs data collection and transfer in a single, self-contained unit. This paper outlines the methods performed to retrofit the Treat & Train dispenser, the hardware and software designed, and the results of reliability testing in the dispensing of treats. All documentation and firmware are available at https://github.com/unl-cchil/canine_treat_dispenser.

Overall Implementation and Design

Treat & Train System

The Treat & Train dispenser consists of a rotating wheel with cutouts that allow treats to fall from the treat storage area to a feeding receptacle. A motor rotates the wheel and a photo interrupter sensor detects whether a treat was dispensed (Figure 1). This means that applying a constant current source to an infrared (IR) transmitter (LED) and an IR receiver and monitoring the output of the receiver will indicate if there is an object blocking the LED. In the case of our system, this would indicate that a treat has been dispensed, which is the output we are trying to detect. Additionally, there is a photo interrupter in the base of the dispensing wheel that can detect the movement of the wheel.

Figure 1 

Location of the two IR transmitter-receiver pairs, where (a) is the treat IR receiver, (b) is the treat IR LED—so (a) and (b) constitute a single photo interrupter—and (c) is the wheel photo interrupter, with both parts integrated into a single package.

To power the IR LED, we inserted a current-limiting resistor in series with the LED power supply. On the negative end we wanted to monitor the output voltage, which should be proportional to the amount of IR light interacting with it. To do this, we placed a resistor divider of sufficiently high resistance to measure the output voltage. This allows the circuit to act as a voltmeter and gives us sufficient resolution to detect treats crossing the IR beam.

The Treat & Train has a brushed DC motor on-board that drives a gear and pulley system to the main dispensing wheel. The stock dispenser drives this motor at 6 VDC in one direction. For our purposes, we allowed more flexibility by using a motor driver chip to drive the motor and control its speed. To drive a 6 VDC motor from a USB power supply, which is set to 5 VDC, we needed to boost the voltage. To allow some excess capacity/voltage for future implementations, we designed a boost regulator with an output voltage of 12 VDC.

Hardware Development

We developed a simple driver board with a USB interface, microcontroller, power stage, motor interface, and an interface for the Treat & Train sensors. We used a microcontroller to drive these circuits, an ARM Cortex-M0+, which is supported by the Arduino IDE for easy programmability and customization. This system was then paired with a Raspberry Pi 4 to create a self-contained dispenser. Additionally, we combined this system with a television display and a USB infrared touchscreen device that is used as mouse input. The result is a complete operant system in which stimuli are presented on the television display, responses are recorded by the touchscreen, and treats are automatically dispensed based on the responses (see demonstration video at https://www.youtube.com/watch?v=veKvqE5ipu4). The connection diagram for this is shown in Figure 2.

Figure 2 

Connection diagram for the developed hardware.

Next, we designed the interfaces to the Treat & Train main board, which had all of its internal connections severed to be interfaced to our circuits. The first circuit is the digital interface for the RF remote ‘click’ signal that dispenses treats. This is triggered by connecting the positive motor output of the Treat & Train main board to a transistor circuit that would energize when the main board tries to drive the motor. This retains RF remote control functionality. The second circuit is the analog voltage sensing resistor divider, for which we used 1 MΩ resistors to keep the current drawn low. These are shown in Figure 3.

Figure 3 

(Left) Transistor to detect the RF remote being activated, (Right) resistor divider circuit to read the IR receiver output voltage. Note: “WHITE WIRE” is the hardware connection to the RF receiver; “CLICK” is the output signal for the RF receiver; “TREAT” is the voltage reading from the output of the IR receiver.

For the custom board, we used an ARM Cortex-M0+ microcontroller (ATSAMD21E from Microchip), with a TB6612 motor driver (Figure 4). The USB voltage was regulated to 3.3 VDC using an AP2112k low dropout regulator and the 12 VDC for the motor was generated using an LMR6201 1.6 MHz step-up converter with a 1.4 A switch. Finally, the right-angle USB connector was mounted such that it would be exposed out of the back wall of the Treat & Train.

Figure 4 

Custom printed circuit board.

This board was designed to work with the “Sparkfun Qwiic Micro” bootloader (Microsoft 2019) that is available through the Boards Manager in the Arduino IDE. Reference this firmware when determining what the Arduino pin names refer to if using this firmware with another processor. The pins that are broken out on the screw terminals are shown in Table 1 and are valid for firmware version 0.4.

Table 1

Custom board pin descriptions and functions, applicable to firmware version 0.4.

Pin Name Arduino Pin Function Description

M+ N/A Output Connected to positive motor pin
M- N/A Output Connected to negative motor pin
GND N/A Input Common ground pin
IR1 N/A Output 3.3V power supply for IR LEDs, current limited to 5mA
IR2 N/A Output 3.3V power supply for IR LEDs, current limited to 5mA
TRT A1 Input IR receiver sensor from treat sensor
BRK N/A Output 3.3V power supply for photointerrupter sensors, current limited to 1mA
WHL A3 Input IR receiver sensor output from wheel sensor
CLK D8 Input RF remote input for ‘Dispense’ button

The internal connections from the microcontroller to the motor driver are specified in Table 2.

Table 2

Arduino pin names and descriptions.

Pin Name Arduino Pin Description

AIN1 D7 First input for motor driver, invert from other input to drive motor in one direction or the other.
AIN2 D6 Second input for motor driver, invert from other input to drive motor in one direction or the other.
PWMA D3 PWM input that controls the speed of the motor.

Software Development

Our implementation will have two sets of software: one set that runs on the microcontroller and another that runs on a host computer, in this case the Raspberry Pi. We composed the first set in the Arduino IDE 1.8.12 (Arduino 2020) using an open-source bootloader and the second using Python (Python Software Foundation 2019), which allows for cross-platform compatibility and testing.

The firmware for the current implementations is at version 0.4 and was designed using the Arduino IDE 1.8.12 (Arduino 2020). The firmware running on the custom-printed circuit board operates using a defined USB protocol driven by byte codes. This can be easily expanded upon by adding cases to the switch statement for the command byte (Table 3). A response of 0x00 indicates the command successfully executed and a response of 0x01 indicates the command failed. Commands are processed in the order they are received.

Table 3

USB protocol byte descriptions.

Name Value Function

Start Byte 67 (Dec), 0x43, ‘C’ Signals the system to start listening for input
Command Byte 1 byte Command defined below
Data Byte 1 byte Defined based on commands
End Byte 69 (Dec), 0x45, ‘E’ Signals the end of the current command

There are a few pre-defined commands for this version of the firmware, one of which is not accessible through USB input (Table 4).

Table 4

USB commands that follow Table 3 protocol.

Command Command Byte Data Function

Dispense ‘D’ ‘0’–’9’ Command to dispense up to 9 treats.
Print ‘P’ ‘0’ Prints current value of sensors, data field is set to zero in ASCII (‘0’).
Update RF ‘B’ ‘0’–’9’ Updates the number of treats that will be dispensed when using the RF remote.
Dispense ‘A’ 0–255 Command to dispense up to 255 treats.
Motor Update ‘M’ 0–255 Updates the motor speed used when operating the dispenser in any mode.
Default: 127
Wheel Test ‘F’ ‘0’ Performs the wheel test routine to verify operation of the firmware and sensors.
RF Dispense N/A N/A By clicking the ‘Dispense’ button on the RF remote, the system will dispense the defined number of treats.
Default: 1 treat

On the Pi, a Python class was implemented to send the above commands over a USB serial link to the driver board. This class was used with PsychoPy in the experiment script.

Dispenser Development

To run entire experimental setups, perform the visualization of input stimulus, control stimulus devices, and log generated data, we integrated a Raspberry Pi 4 (Raspberry Pi Foundation 2020) single-board computer running the default Raspberry Pi OS (Raspberry Pi Foundation 2020). This allowed us to achieve three goals:

  1. Wireless Access Point
    • – The Raspberry Pi 4 can host its own WiFi access point, which allows the experimenter to connect to the Pi and run experiments, download results, and interact with the main system.
  2. Decoupled Interfaces
    • – The experimenter’s screen and the screen used by the canine are separate and require no extra hardware to achieve. By connecting to the Pi using SSH (Secure Shell), the command line can be exposed to run the experiments. The canine screen can be connected to the HDMI0 of the Pi and the SSH session can be started over a wireless connection.
  3. Simple Setup
    • – The system is expected to be easy to setup and maintain. We can use robust packages such as pi-ap (Houlahan 2019) and SD card backups to easily restore the dispenser and get new dispensers up and running.

Achieving the above three criteria allows for a launching point towards more complex and integrated solutions for automated reward delivery and data logging.

The final dispenser with the new electronics and functionality had its connectors exposed on the back wall of the dispenser. The connections include the USB connector to the retrofitted control board, the Raspberry Pi 4 USB connectors, an HDMI port, Ethernet jack, and the USB-C power supply input port. This allows the Raspberry Pi 4 to display stimuli on a screen connected to the HDMI port and accept mouse inputs from an IR touchscreen adapter through on of the Pi USB ports. The Pi can also be network connected through the Ethernet jack. A short USB cable from the Pi provides power and serial communications to the driver board. This USB cable can be removed from the Pi, and the dispenser can be connected directly to a standard PC—bypassing the Raspberry Pi—to operate the retrofitted dispenser. This is shown in Figure 5.

Figure 5 

Peripheral interface for the dispenser.

The dispenser, when turned on, creates a WiFi access point called “operant-canine-xx”, with “xx” being the dispenser number, i.e. “01” with a local address of pi@192.168.0.1 and the password raspberry, which are the default values in the pi-ap configuration. This can be modified in the pi-ap software. By connecting to this AP, the Raspberry Pi can be accessed through SSH using a program such as PuTTY (Tatham 2020).

To use the RF remote to trigger the system, the Treat & Train main board must first be turned on before powering on the retrofitted board. If the Treat & Train is turned on after the retrofitted board, the ‘CLK’ signal will go high, as it is controlled through a transistor that is not turned off when the board is first powered on. Do not use anything other than a computer USB port for powering the system. The retrofitted board does not power the Treat & Train main board, so it will still require four D cell batteries to operate with backwards compatibility. This should be fixed in the next revision and the RF receiver should be powered by the custom printed circuit board.

(2) Quality Control

Safety

Operators of this dispenser should be aware of four hazards:

  • Finger pinches.
  • Electric shock.
  • Burns when assembling electronics.
  • Damage to Treat & Train.

Finger Pinches

The dispenser wheel turns continuously from a DC motor to dispense treats through an eyelet that, if not careful, can cause the user’s finger to be pinched when trying to fix a jam.

Electric Shock

The powering of electronics using an external power source, such as when testing a newly developed circuit, can cause electric shocks if not careful. Work tables should be placed where there is no risk of splashing water. All cables used to connect sensors and power should be inspected for damage and replaced if necessary.

Burns during Assembly

The assembly of the described electronics includes the use of soldering irons and hot air rework tools. These tools use high temperatures to melt solder and pose a risk of burning when used improperly. Ensure that the user’s manuals are read and adhered to when using new tools.

Damage to Treat & Train

When retrofitting an off-the-shelf device, there is always a risk of damaging or destroying the on-board electronics. Before applying power to any on-board sensors, double check your power supply and connections to prevent short circuits and over-voltage/over-current situations.

General Testing

Considering one line of canine behavior and psychology research includes the design of numerical preference studies (Ward 2007, Baker 2012, Miletto Petrazzini et al. 2020), we tested the dispenser’s accuracy of dispensing treats. This gives benchmarks that allow for future work that may be needed to meet specific performance deficits that exist in the current setup and for the dispenser to be adapted to new types of studies.

For this testing, we wanted to measure the failure rate of the dispensing commands, meaning the amount over or under that it dispenses from the target amount. To do this, we performed a total of 100 tests split into 10 tests of increasing dispensing targets from 1 to 10 treats, in steps of one treat. These tests were performed using two of the described dispensers, which were filled up to the “1 cup” line with Pet Botanics Training Reward treats, which are cylindrical treats that fit through the larger dispenser wheel. Reliability data are available at https://github.com/unl-cchil/canine_treat_dispenser.

The results for this testing are shown in Tables 5 and 6. We tracked the mean number of treats dispensed for each dispenser tested (columns Actual One and Two), the number of treats dispensed that were over or under the expected number in each set of tests (columns Errors One and Two), the mean number of treats dispensed averaged across the two dispensers (column Mean Actual), and the mean number of failures between the two dispensers (Mean Error).

Table 5

Results of the treat dispensing trials.

Total Treats Dispensed Correct Number Dispensed Over-Dispensing Under-Dispensing

First Dispenser 100 68 31 1
Second Dispenser 100 78 22 0

Table 6

Results of the reliability testing.

Dispense Actual One Actual Two Errors One Errors Two Mean Actual Mean Error

1 1.10 1.00 1 0 1.05 0.50
2 2.40 2.20 4 2 2.30 3.00
3 3.40 3.30 4 3 3.35 3.50
4 4.10 4.00 1 0 4.05 0.50
5 5.20 5.30 2 3 5.25 2.50
6 6.40 6.50 4 5 6.45 4.50
7 7.60 7.10 6 1 7.35 3.50
8 8.50 8.60 4 4 8.55 4.00
9 9.50 9.20 4 2 9.35 3.00
10 10.00 10.20 2 2 10.10 2.00
Mean Error 32.0% 22.0% 27.0%

Each dispensing command has a potential error rate of 32% in the first dispenser and 22% in the second dispenser. This means that over- or under-dispensing occurs roughly every third and fourth event, respectively.

(3) Application

Use Case(s)

The modified Treat & Train was designed to be used in canine cognition studies and be controlled by a script to perform the reward dispensing in response to input from the canine subject. This described tools can also reasonably act as a platform for new dispenser development, which can be specifically designed for canine cognition research, such as precise dispensers.

Further, this may be used by dog owners who are training their dogs for particular tasks or who would like to develop games for their dogs to play. Existing commercial dispensers such as PupPod (PupPod 2020) allow for inputs from embedded sensors in toys. But they do not connect to touchscreens, which could provide rich possibilities for games. Our dispenser allows a wider range of potential stimuli to be presented to and responses to be recorded from dogs to trigger a reward.

Since the Raspberry Pi 4 has an on-board Bluetooth modem, this allows for integration of other custom devices such as inertial measurement units (IMUs), which measure its own movement in terms of acceleration in the X, Y, and Z axes, to allow a canine’s position and movement to control treat dispensing (Protopopova et al. 2016, Majikes 2017). Additionally, the sound output of the Pi can allow for sound triggers in the operant conditioning protocol (Protopopova et al. 2016, Edwards 2019).

Reuse Potential and Adaptability

With the included Raspberry Pi 4, custom sensors can be easily integrated through Python scripting to trigger the dispensing command. The developed Python class and custom hardware allow for control of other dispensers, such as for bird or rat pellets. Common off-the-shelf dispensers use a single motor to dispense sequential treats, in a similar fashion as the Treat & Train; due to this the presented hardware would work on those systems.

(4) Build Details

Availability of Materials and Methods

Building the custom hardware requires the use of a soldering iron, hot air rework gun, reflow oven, precise tweezers, and a vise. All the parts for this part of the build are available from DigiKey and a PCB manufacturer.

The retrofitting operation requires a 3D printed Raspberry Pi mount, a Dremel for exposing ports on the Treat & Train, and various M3 screws. Additionally, the extra parts, such as HDMI cables and ports, for the Raspberry Pi 4 are available from Amazon.

Ease of Build

The parts used in the build are readily available from online vendors such as Adafruit and DigiKey. The custom version requires the operation of more advanced tools such as a hot air gun or reflow oven, pick and place or precise tweezers, and solder paste and flux. We estimate the build can be performed in around eight hours with sufficient planning and experience from start to finish for the DIY version.

Operating Software and Peripherals

For the custom hardware, Arduino IDE 1.8.12 was used as the development environment. For the Raspberry Pi 4, the Raspberry Pi OS was used as well as pyserial (version 3.4), PsychoPy (version 3.2.4), and pi-ap (version 1.10.02).

Dependencies

This build requires the use of pi-ap (Houlahan 2019), Raspberry Pi OS (Raspberry Pi Foundation 2020), and Arduino IDE (Arduino 2020). The driving software to send commands over the USB port is written in Python 3.7.6 (Python Software Foundation 2019).

Hardware Documentation and File Location

Name: GitHub

Persistent identifier: https://github.com/unl-cchil/canine_treat_dispenser

Licence: Creative Commons CC-BY-SA, 4.0

Publisher: Jeffrey R. Stevens

Date published: 29/07/2020

Software is stored together with the hardware files, in the same repository.

Licence: Creative Commons CC-BY-SA, 4.0

Date published: 29/07/2020

(5) Discussion

Conclusions

This study presented a method by which a common, off-the-shelf, canine treat dispenser can be modified to conduct operant conditioning studies using a computer interface. Using the presented version, the common Arduino platform was used to interface to the onboard photo interrupters and control the dispensing motor, achieving the same functionality as the original dispenser but controlled by a computer. This computer, a Raspberry Pi 4, was integrated to the new hardware to run a Linux distribution and operate the generation of stimulus through PsychoPy (Peirce 2007). In adding this central computing platform, all results from the experiments can be centrally stored and easily accessed for future processing and reporting.

By making this dispenser DIY and open-source, researchers may expand on the work and include new trigger methods ranging from sound to ultrasonic distance sensing. By having a single-board computer housed within the dispenser, experiment control software can be generated and run by using the tested and supported PsychoPy Python package (Peirce 2007). Additionally, this setup allows for cross-platform development, meaning that any software already generated using the PsychoPy package is supported. The inclusion of the Raspberry Pi 4 also makes the entire setup mobile, so experiments can be conducted outside of the laboratory.

The demonstrated computer-controlled dispenser can be readily used and assembled with minimal skill and can act as a jumping off point for automated canine operant conditioning studies. Since automated reward dispenser are difficult to build from scratch, our proposed modifications of the Treat & Train would allow for researchers to integrate an automated dispenser into their studies (Hardin et al. 2015, Protopopova et al. 2016, Majikes 2017, Wallis et al. 2017, Robinson et al. 2020). Additionally, since the use of positive reinforcement training has been shown to indicate an increased level of obedience, our dispenser can be used in at-home situations for canine training exercises (Hiby et al. 2004).

The average error rate between the two dispensers is 27%, which is an error every fourth dispensing command. These errors often happen when two treats stack in the output hole and, when it dispenses, both fall through, leading to dispensing one extra treat. If, for instance, a circular or thicker treat were used, this issue could be mitigated. Alternatively, modifying the dispensing wheel to limit the number of treats grabbed could also help. As for situations where the treat would jam the movement of the wheel, the force of the turning wheel would cut the treat in half, as the treats used in testing were pliable enough to allow for this. This resolved the issue when it presented itself, but this may not be the case for all treat types.

For studies in which precise treat dispensing is not required, our work allows researchers to relatively inexpensively adapt an off-the-shelf product into a treat dispenser that can be controlled either by a personal computer or a Raspberry Pi, thus facilitating operant conditioning studies in dogs.

Future Work

Our results indicate that the developed dispenser is most likely not suitable for precise dispensing experiments, such as numerical preference experiments. Due to this, our focus will be to prove out the developed dispenser for canine cognition studies and stress test the current design. Additionally, to make the developed printed circuit board more easily integrated, it would be beneficial to adapt it into a Raspberry Pi HAT (Hardware Attached on Top), in which the board is directly attached to the Raspberry Pi.

Additional File

The additional file for this article can be found as follows:

Data file

CSV file of reliability testing data. DOI: https://doi.org/10.5334/joh.27.s1