prathimaanand / squirtlebot---turtlebot-reconnaissance-mission

In this project, a simulation of a disaster environment was implemented using a TurtleBot3 hardware. The robot was used to survey the area by mapping its trajectory also the area and identify potential dangers and victims

SquirtleBot - TurtleBot Reconnaissance Mission

In this project, a simulation of a disaster environment was implemented using a TurtleBot3 hardware. The robot was used to survey the area by mapping its trajectory also the area and identify potential dangers and victims. The project demonstrated the efficiency of using a robot to map an area and identify hazards and injured people before sending human responders into the field. The performance of the mover_base and explore_lite packages were modified using ROS and Python in order to improve the robot's capabilities in terms of reliability and precision.

Introduction and Motivation

The purpose the this project is to simulate a reconnaissance mission after a disaster This could be a burned down building, hurricane, earthquake, etc. To reduce the risk to first responders, robots would be deployed in dangerous environments to survey and report back To model this scenario: turtlebot -> mobile platform apriltags -> victims closed room with obstacles -> dangerous environment

Turtlebot Types

There are 2 types of Turtlebots:

1. Burger.
2. Waffle. We are using turtlebot burger for our application for its better maneuverability, operation time and lightweightedness as compared to turtlebot waffle.

Turtlebot Specifications

General Specifications : Size : 138178192(mm) Maximum translational velocity: 0.22 m/s Maximum rotational velocity : 2.84 rad/s

Technical Specifications: Raspberry pi 3 (Single board Computer) 1024(1GB) RAM 64 bit Quad Core Arm Cortex - A53 Wifi : Dual band 2.4 & 5 GHz Power Supply 5V. OpenCR (Micro Controller Unit) 32 bit ARM Cortex -M7 with FPU Power Supply 5v

Actuator (XL430 - W250) Power supply 6.5-12V (11.1V recom). No load Speed with recom power 57 rev/s. Uses PID control Algorithm (Feedback : Position, Velocity, Load, Real Time tick, Trajectory, Temperature, Input Voltage, etc) Laser Distance Sensor (360 LDS -01) 2D 360 Deg laser scanner. Power Supply 5V. Ambient light resistance <= 10,000 luminescence

Camera - (Raspberry pi cam v2.1) Sensor IMX219 - 8MP Horizontal FoV - 62.2Deg. Vertical FoV - 48.8 Deg. IMU 3-Axis Gyroscope 3-Axis Accelerometer Battery Lithium polymer 11.1V 1800mAh / 19.98Wh 5C

Turtlebot Initialization

Prior to starting to solve the problem: OS installation Network configuration Camera and Lidar nodes Camera Calibration Starting point for our solution Explore_lite Apriltag_detection

Camera Calibration

Found intrinsics of the camera using Kalibr Central Pixel, focal length Distortion coefficients Low reprojection error Less than a pixel in both axes