Auto Tuning

1) Auto Tune by Randy Mackay

2) Auto Trimming Tutorial

Testing & Tuning Stage

1) Field Test using Custom-made Quadcopter

Hexacopter mounted on the testing rack

Testing Rack built for testing hexacopter

Quadcopter built to simulate the hexacopter

Setting tuning parameters for PID Controller

2) Field Test Video of Hexacopter (Initial Stage)

Low Altitude Flight Test

3) Setting Waypoint in Mission Planner and testing

Setting Waypoint

Waypoint Test

4) Pid Tuning

Arducopter Tuning Guide

MegaPirate PID tuning - stabilize mode

MegaPirate PID tuning - rate (acro)

Image Processing

1) Payload Release Looping

            This is the procedural setup for activating the payload and navigation system. The integration of image processing block diagram from surveillance sensor to flight control system consists of the object recognition algorithm.

            The algorithm is divided into image segmentation steps that take the colour and texture information into account followed by a model-object detection step which includes the shape, scale and context information. The image is then matched with predefined target and used for data association and tracking with data acquired from GPS. 

              Lastly, an overriding command is sent to flight controller to activate the release mechanism as well as overriding the flight mode.

2) Image Segmentation

The image segmentation stage divides the original image into three different classes based on the colour and texture features. The images are collected in Red Green Blue (RGB) colour space. They are then transformed into a Hue, Saturation and Value (HSV) colour space to reduce the sensitivity towards change in light intensity.

After feature extraction the colour and texture features are grouped into one single feature vector consisting of three colour channels and thirty texture channels. Each feature vector is then assigned with a label representing its class. The aim is to segment the original image into three different classes, object, shadow and background.

The classifier converts the original colour images into images with meaningful class labels.

3) Object Model Detection

Object detection algorithms based purely on statistical information have their performance limited by the quality of data. The algorithm used to generate a target object outline is used as the prior knowledge for object detection where a simple round shape is used to approximate the edge of ground target known as edge matching.  Any potential object detections with the wrong shape and edges can be rejected. 

4) Image Matching

The image matching technique applied here is based on image segmentation with RGB values and objects model definition with shape and edge matching. This method is useful to detect predefined ground target. The higher the UAV flies, the more structure from the environment can be captured and thus, image registration is more reliable at higher altitude.

After both methods have been processed, a matching algorithm tries to identify the best match with the predefined image. The image obtained with highest percentage of matching in terms of colour, shape and edges will validate the criteria set for the activation of release mechanism.

Hardware Design

1) Hardware Setup Diagram

For ground control station, EasyCAP will be connected to laptop and wireless camera receiver. Whereas, for air module, CCD Camera is installed at the bottom of hexacopter air frame on a carbon-fibre landing gear. The landing gear will be equipped with a custom-built bracket to mount the camera. Whereas, the video transmitter will be installed above the hexacopter frame for better signal transmission.

2) Glance of pictures of before and after installation

Ground control station

1) MAVProxy

MAVProxy is a MAVLink protocol proxy and ground station. It is a fully-functioning GCS for UAV's and is oriented towards command-line operation. It is an extendable GCS for any UAV supporting the MAVLink protocol such as ArduPilotMega (APM). It is also a command-line, console-based application which runs in command prompt. It is written in Python scripts and supports loadable modules which in turn support consoles, moving maps, joysticks, antenna trackers and image processing module.

2) Mission Planner

Mission Planner is a fully-featured ground control station. It is used for waypoint entry using GPS and to select mission commands from drop-down menus. Mission planner can also be used to download mission log files for analysing and to configure APM settings for airframe. It is also used to set the tilting angle limit and servo PWM limit for release mechanism.

3) Setting servo PWM limits and angle limits

The picture below shows the snapshot of mission waypoint entry using Mission Planner V1.2.55 through GPS. Whereas, Figure 3.11 shows the setting of servo limits and tilting angle limits using Mission Planner. Both parameters are set to RC6 as servo input channel and RC11 as output channel on APM flight controller. Here, the lowest servo limit is set to 1100 whereas the highest is set to 1900 according to the value shown in radio calibration. The range of angle limits is set between -45 and +45 degree. 

Imaging Tools and software

1) The onboard CCD camera is tested to ensure its full functionabilty as provided in camera specifications. It is tested with a 933MHz video transmitter (TX) and receiver (RX) for wireless communication. The lens size and field of view is to be measured experimentally. The picture below shows the 1/3 420L Sony 12V CCD camera used in image capturing and the video transmitter and receiver used for video signal transmission as well as the camera range and degree of view detectable by CCD Camera according to its specifications.

2) A capture device is required to convert the analog video output from the wireless video receiver to a digital format that can be used by a video encoder named DV Driver. These capture devices are available as either internal capture cards or external devices that are connected via USB. EasyCAP DC60 which runs under STK1160 driver is used to capture video up to 720x576 pixels at 25fps (PAL) and 720x480 pixels at 30fps (NTSC). It enables four analog inputs of CVBS, S-VIDEO, AUDIO Left and Right.

3) Image processing is done using Python-SimpleCV where SimpleCV is an open source framework for building computer vision applications. With it, several high-powered computer vision libraries such as OpenCV can be accessed easily which makes computer vision easy. Whereas, Python is a widely used high-level programming language which emphasizes code readability and its syntax allows programmers to express concepts in fewer lines of code. Python supports multiple programming paradigms including object-oriented, imperative and functional programming styles. It is often used as a scripting language, for example in SimpleCV for computer vision applications.

Project Flow Chart

Above shows the flowchart of research activities. The images taken from CCD camera is acquired via video transmitter and transmit to wireless receiver of a ground control station. Image processing algorithm is incorporated in Python Integrated Development Environment (IDE). The imaging payload system is assembled on UAV platform as part of system integration.

Radio Rx channel and servo payload parameters for release mechanism is set in Mission Planner V2.2.55. Mavproxy is used as ground control station to interface with Python-SimpleCV to process real-flight image data. The captured image is processed with a pseudocode as given for the identification of predefined target. Lastly, computer overriding signal will be sent to flight controller for precision approach as well as release mechanism.