Computer Vision & Drones

Project Overview

Comprehensive collection of robotics projects focusing on computer vision applications and drone technology. This portfolio showcases various autonomous systems, computer vision algorithms, and unmanned aerial vehicle (UAV) implementations for different applications and research purposes.

Project Collection

Key Areas of Focus

• Computer Vision: Advanced image processing and object recognition algorithms
• Drone Technology: Autonomous flight control and navigation systems
• Robotics Integration: Multi-robot coordination and swarm intelligence
• Sensor Fusion: Integration of multiple sensor types for enhanced perception
• Autonomous Systems: Self-navigating and decision-making capabilities

Applications

• Aerial Photography: Automated drone photography and videography
• Surveillance: Security and monitoring applications
• Research: Academic and industrial research projects
• Entertainment: Recreational and commercial drone applications

Semi autonomous military drone (version one)

Above video shows the first stage of the differential drive drone. It has two cameras, one below to aid in manual drive whilst the top camera is to detect human faces. Once the top camera detects a human face, the bottom camera searches for a weapon using computer vision. In the demo a toy gun resembles the weapon to search for, once the weapon is detected a firing sound (audio file is played). It further supports both manual and autonomous driving.

The robot can be driven remotely using a joystick over wifi on a client computer. To achieve autonomous driving it has a high precision 1D gyro and encoders for dead reckoning drive. The goal location is provided in the client computer gui, this is done by specifying the target x and y coordinates in metres. It is able to avoid obstacles whilst going towards the target location. Three Infra-red Ranger sensors in the front are used to detect obstacles and re-adjust its path to the target.

Color tracking using differential drive robot

Robot that uses computer vision to track and follow a blue cup. It will only move towards the blue cup and ignores anything else in the scene.

This uses a USB camera and computer vision precisely blob detection. The desired color range (blue cup hue and its shades) is trimmed from the HSV color chart. Circle hough transform is applied to detect the desired blob.

Object tracking using vision

Above robot is able to track a known object and drive towards it (in this case a coke can). Computation is done using OpenCV on a remote laptop. Computed results are sent to the robot using socket programming.

SURF algorithm is used to detect the coke can features. Once sufficient features are detected, the robot then drives towards the can. The robot runs on a beagle bone single board computer, which handles communication with the remote computer and serially communicates desired velocities to a PIC microcontroller. The PIC microcontroller then directly interfaces the speed controller board and sets each wheel's PWM and direction.