420 mm (16.5 in)
420 mm (16.5 in)
280 mm (11 in)
120 mm (4.7 in)
2.5 kg (5.5 lbs)
### Compatibility and Expandability
Compatible with popular microcontrollers and single-board computers, such as Arduino, Raspberry Pi, and BeagleBone
Expandable design allows for easy integration of custom components and modules
Applications
| The Black Gladiator Tracked Robot Chassis is suitable for a wide range of applications, including |
Robotics and autonomous systems
IoT projects and smart home automation
Research and development
Education and training
Industrial automation and inspection
Search and rescue operations
Technical Specifications
For detailed technical specifications, please refer to the datasheet provided with the product or available for download from the manufacturer's website.
Warranty and Support
The Black Gladiator Tracked Robot Chassis comes with a one-year limited warranty against manufacturing defects. Comprehensive technical support, including documentation, tutorials, and community forums, is available to ensure a smooth and successful project development experience.
Black Gladiator - Tracked Robot Chassis DocumentationThe Black Gladiator is a robust and versatile tracked robot chassis designed for IoT applications. This chassis provides a solid foundation for building robotic systems that require mobility, stability, and durability. The tracked design allows for smooth movement on various terrains, making it ideal for outdoor and industrial applications.Technical Specifications:Material: Durable aluminum alloy
Dimensions: 300mm x 200mm x 150mm (Length x Width x Height)
Weight: 1.5 kg
Track dimensions: 400mm x 50mm x 20mm (Length x Width x Height)
Motor specifications:
+ Type: Brushed DC motor
+ Voltage: 12V
+ Current: 1A
+ Speed: Up to 0.5 m/s
Sensor interfaces: I2C, UART, GPIO
Power requirements: 12V DC, 1AProgramming and ControlThe Black Gladiator can be controlled using a microcontroller or a single-board computer (SBC) such as the Arduino or Raspberry Pi. The chassis provides a GPIO interface for connecting sensors and peripherals.### Example 1: Basic Motor Control using ArduinoIn this example, we'll demonstrate how to control the Black Gladiator's motors using an Arduino Uno board.Hardware Requirements:Arduino Uno board
Black Gladiator Tracked Robot Chassis
Jumper wiresSoftware Requirements:Arduino IDE (version 1.8.x or later)Code:
```c++
const int leftMotorForward = 2; // Pin for left motor forward control
const int leftMotorBackward = 3; // Pin for left motor backward control
const int rightMotorForward = 4; // Pin for right motor forward control
const int rightMotorBackward = 5; // Pin for right motor backward controlvoid setup() {
pinMode(leftMotorForward, OUTPUT);
pinMode(leftMotorBackward, OUTPUT);
pinMode(rightMotorForward, OUTPUT);
pinMode(rightMotorBackward, OUTPUT);
}void loop() {
// Move forward
digitalWrite(leftMotorForward, HIGH);
digitalWrite(rightMotorForward, HIGH);
delay(2000);
// Stop
digitalWrite(leftMotorForward, LOW);
digitalWrite(rightMotorForward, LOW);
delay(1000);
// Turn left
digitalWrite(leftMotorBackward, HIGH);
digitalWrite(rightMotorForward, HIGH);
delay(500);
// Stop
digitalWrite(leftMotorBackward, LOW);
digitalWrite(rightMotorForward, LOW);
delay(1000);
}
```
Explanation:This code example demonstrates basic motor control using the Arduino Uno board. The code defines the pins for left and right motor control and sets them as output pins in the `setup()` function. In the `loop()` function, the code moves the robot forward, stops, turns left, and stops again, illustrating basic movement control.### Example 2: Object Tracking using OpenCV and Raspberry PiIn this example, we'll demonstrate how to use the Black Gladiator with a Raspberry Pi and OpenCV to track objects using a camera.Hardware Requirements:Raspberry Pi 4 Model B
Black Gladiator Tracked Robot Chassis
Raspberry Pi Camera Module v2
Jumper wiresSoftware Requirements:Raspbian OS (version 10 or later)
OpenCV (version 4.5.x or later)
Python 3.xCode:
```python
import cv2
import numpy as np
import RPi.GPIO as GPIO# Set up GPIO pins for motor control
GPIO.setmode(GPIO.BCM)
left_motor_forward = 17
left_motor_backward = 23
right_motor_forward = 24
right_motor_backward = 25
GPIO.setup(left_motor_forward, GPIO.OUT)
GPIO.setup(left_motor_backward, GPIO.OUT)
GPIO.setup(right_motor_forward, GPIO.OUT)
GPIO.setup(right_motor_backward, GPIO.OUT)# Set up camera
cap = cv2.VideoCapture(0)while True:
ret, frame = cap.read()
# Convert frame to grayscale
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# Apply thresholding to segment objects
_, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
# Find contours
contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# Iterate through contours and track objects
for contour in contours:
x, y, w, h = cv2.boundingRect(contour)
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
# Track object movement
if x > 320: # Object is on the right side of the frame
# Move right motor forward
GPIO.output(right_motor_forward, GPIO.HIGH)
GPIO.output(left_motor_backward, GPIO.LOW)
elif x < 160: # Object is on the left side of the frame
# Move left motor forward
GPIO.output(left_motor_forward, GPIO.HIGH)
GPIO.output(right_motor_backward, GPIO.LOW)
else: # Object is in the center of the frame
# Stop motors
GPIO.output(left_motor_forward, GPIO.LOW)
GPIO.output(right_motor_forward, GPIO.LOW)
cv2.imshow('Object Tracking', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
breakcap.release()
cv2.destroyAllWindows()
GPIO.cleanup()
```
Explanation:This code example demonstrates object tracking using OpenCV and the Raspberry Pi. The code sets up the GPIO pins for motor control and initializes the camera module. The code then reads frames from the camera, converts them to grayscale, and applies thresholding to segment objects. The code finds contours and iterates through them to track object movement. Based on the object's position, the code controls the motors to move the robot accordingly.These examples demonstrate the versatility and programmability of the Black Gladiator Tracked Robot Chassis. By combining the chassis with a microcontroller or SBC, you can build a wide range of IoT applications that require mobility and sensor integration.