A low-cost marine compass (Part 2 – Software)

The problem with low-cost sensors is that the accelerometer outputs are very noisy (much less so for the magnetometer).

The LSM303D sensor is one of 2 magnetic sensors supported by the PX4 development team. The solution they have chosen to reduce the noise in the accelerometer outputs is to apply a low-pass software filter to the measurements, in addition to the hardware low-pass filter built into the chip. In this project, I have ported their LSM303D SPI driver and 2-pole software filter to the WinAVR environment.

I have also kept the strategy adopted for my Hi-Res compass, which is to use a higher sampling rate for the accelerometer, and take the average result in the calculation performed after each magnetometer measurement.

I have chosen a 6.25 Hz sampling rate for the LSM303D magnetometer, to insure that it will operate in its hi-resolution mode. The accelerometer sampling rate is set at 200 Hz, and the cut-off frequency of the software low-pass filter is set at 40 Hz.

Here is a general explanation of the code behaviour during normal operation.

The Pro Mini continually reads triple raw values (x,y,z) from the accelerometer. For each axis, it feeds the raw value to the software low-pass filter, which spits out a new filtered value for each axis. The filtered values are accumulated and counted.
 
Each time the LSM303D magnetometer has a new set of 3 raw values available (x,y,z), it fires an interrupt on the Pro Mini. When this happens, the Pro Mini applies the calibration correction to the magnetometer values. It calculates the average of the accelerometer filtered values since the last interrupt, and applies the calibration correction. From this set of 6 values, it calculates the tilt-compensated heading, and the heel and pitch angles. It formats the results in 2 NMEA sentences that are sent to the serial port (115200 baud), 6.25 times per second. 

The source code for the WinAVR environment can be found here. I am using Programmer’s Notepad to compile the code and program the Pro Mini through the Arduino bootloader. 

This implies that the calibration factors have already been found since they appear as constants in the code. The software has other modes of operation useful for developing these calibration factors, which will be presented in Part 3 of this series.

A low-cost marine compass (Part 1 – Hardware
A low-cost marine compass (Part 3 - Calibration

A low-cost marine compass (Part 1 – Hardware)

For an electronic compass, the accuracy depends more on the quality of the calibration that on the price of the components. Here is the design of a low-cost compass developed around this idea.

The compass is based on the LSM303D chip which combines a 3-axis accelerometer and a 3-axis magnetometer. I used the Pololu carrier board featuring this chip (9.95 US$).

The other component of the compass is an Arduino Pro Mini 328 – 3.3V/8Mhz from Sparkfun (9.95 US$). The Pro Mini communicates with the LSM303D through the SPI bus, using drivers adapted from the PX4 Development Team. The 2 components are hardwired together to keep a compact form.

The following picture illustrates the small compass besides my Hi-Resolution custom compass previously described on this blog.

Here is the wiring diagram for SPI communication. The FTDI breakout is used to program the Pro Mini and to read the LSM303D raw data during the calibration step.

The long ribbon cable is required during calibration so that the compass can be moved around at a safe distance from the breakout and the laptop to avoid magnetic interference. After calibration, the ribbon cable is used to power the compass (3.3V) and to transmit the tilt-compensated heading and the heel angle through the serial link to whatever instrument that needs it.


A low-cost marine compass (Part 2 – Software) 
A low-cost marine compass (Part 3 - Calibration)

Catamaran’s Sailing Instruments

A catamaran’s owner has decided to adapt my design to his boat, which is sailed on a large freshwater lake. As the lake’s currents are virtually negligible, the boat speed is measured from a GPS. Furthermore, for a catamaran, there is no need to correct wind angle measurements for heel. No electronic compass is planned for now. A Nexus tablet is used as display, with a Bluetooth link to the system.

For a catamaran, as there is no heel, there is no way to calculate the leeway as we do for a keelboat  using boat speed and heel (as described here). So the owner will have to make test runs and physically measure the leeway in different conditions, while logging boat speed, wind speed and apparent wind angle. It is expected that the leeway angle can be correlated to these 3 variables, and that correction curves can be developed and programmed in the microcontroller. For now, the leeway corrections will be neglected until test data are available.

The microprocessor acquires or calculates the following data 10 times per second (10 Hz):

·        Apparent wind angle (AWA)

·        Apparent wind speed (AWS)

·        Boat speed through water (STW = GPS’s SOG)

·        True wind speed (TWS)

·        True wind angle (TWA)

·        Velocity Made Good (VMG)
·        Leeway angle
·        Heading ( = GPS's COG, when not corrected for leeway)
·        Wind direction

These data are available through a serial link, going both to a Bluetooth modem and to serial-to-USB converter.

The Nexus tablet calculates running averages over an adjustable range and refreshes a custom display 10 times per second.

A laptop can be connected to the system to view or change calibration values, or to download live data for further analysis.

The system is now under construction and will soon be installed. Software (both for the microprocessor and the Android tablet) is also under development. The microprocessor code under development (in the WinAVR environment) can be found here. More detailed documentation will be added at the same location.

Going Bluetooth with an Android Tablet

After experimenting with Wi-Fi, I have now added a Bluetooth link between my system and an Android tablet. This has proved to be very reliable, and simpler to implement when compared to Wi-Fi.

As the Bluetooth modem operates from a 5 V supply, no level shifting is required on the data line.

However, there is one constraint that has to be considered. When the tablet tries to connect to the system, the connection will fail if there is data already flowing on the serial output.

There are 2 ways to deal with this:

               - add a switch to manually cut the serial input during the connection step;

               - connect during an initial delay at the system start-up (this is how I proceed, as there is a 10 seconds delay without transmission when my system is switched on).

In my Bluetooth app, the first screen shows a ‘Connect’ button and a disabled ‘Start’ button. The 'Connect' button is pressed when there is no data coming in. Once the connection is established, the ‘Start’ button is enabled and can be pressed to initiate a new thread that decodes the modem output, and pass the information to the main instrument view that is now switched on, refreshed 10 times per second.

The Bluetooth thread also saves all data in a large memory buffer (16 MB) that can store up to 4.5 hours of data per session. When the app is closing, these data are written to a file on the tablet’s internal memory card, that can later be transferred to a PC for analysis.

7” Nexus Tablet as Instrument Display

I have been experimenting in building custom displays on a 7” Nexus tablet, from 10 Hz live data received through Wi-Fi from my master microcontroller. This short video taken while sailing shows how this looks like.

 

The Nexus tablet is powerful enough for the job, refreshing the screen 10 times per second with new data.

 This screenshot explains what is going on in close hauled mode:

There are 3 thick needles, each one accompanied with a smaller one and a corresponding number of the same color.

The blue data is for STW (Speed Through Water), the green data is for VMG (Velocity Made Good), and the red data is for AWA (Apparent Wind Angle). The small needles are the instantaneous measured values, refreshed 10 times per second. The thick needles and the corresponding numbers are dampened data (moving averages over 1 to 5 seconds, recalculated 10 times per second by the tablet). The damping period (4 s in this screenshot) is adjustable on the fly by sweeping a finger on the tablet up and down.

The small black circle is a moving target (depending on current True Wind Speed), which represents the optimal AWA for best VMG.

The next screenshot is for downwind mode. Here, the red needle represents TWA (True Wind Angle), a more useful information when flying the asymmetrical spinnaker.

There is also a night mode available, created by inverting the black and white colors.

 

The tablet is not bright enough to be used as a fixed display in the cockpit, but very usable as a personal display, like the tacticians in the last America Cup.

 

Ben Ainslie (Oracle Team)

 

 

Ray Davies (Team New Zealand)

 

 

 

 

 

 

 

GPS data from Wifly to Android phone (Part 2)

As a proof of concept, I have created a simple app that fetches the last UDP packet received, and displays its contents in a floating message box, each time the button is pressed.

Here is an excerpt of the app's java code that is used for that.

...
public class MainActivity extends Activity {
   ...
   public void sendMessage(View view) {
        new ServerAsyncTask(MainActivity.this).execute();
   }
   ...
   public static class ServerAsyncTask
               extends AsyncTask<Void, String, Void> {
      private Activity act;
      public ServerAsyncTask(Activity _act) {
           this.act = _act;
      }
      @Override
      protected String doInBackground(Void... params) {
         byte[] receiveData = new byte[1024];
         DatagramSocket socket = null;
         try {
            socket = new DatagramSocket(50000);
            socket.setSoTimeout(30000);
            DatagramPacket packet =
               new DatagramPacket(receiveData, receiveData.length);
            socket.receive(packet);
            String contents = new String(packet.getData());
            publishProgress(contents);
            socket.close();
         } catch (SocketException e) {
             socket.close();
         } catch (SocketTimeoutException e) {
             socket.close();
         } catch (IOException e) {
             socket.close();
         }
         return (Void)null;
      }
     
      @Override
      protected void onProgressUpdate(String... str) {
         Toast.makeText(act, str[0], Toast.LENGTH_SHORT).show();
      }
   }
} 

GPS data from Wifly to Android phone (Part 1)

Once an Access Point has been created on the phone with the app described in the last post, we can use the app’s data to configure the RN-XV module. Let's keep the same example:

            - SSID :                     DIRECT-6U-Android_37e5

            - passphrase :           sK2QIENk           
            - phone’s IP :            192.168.49.1

Here is the arrangement used to configure the RN-XV module:

  Only 4 pins will be used on the RN-XV module:

• pin 1 (red) : VDD_3V3
• pin2 (yellow) : UART_TX
• pin3 (green) : UART_RX, with an external 10k pull-up to 3.3 V
• pin 10 (black) : GND

The module is connected to a laptop running TeraTerm through a USB-to-Serial converter. Here, we use the Adafruit's USB to TTL Serial Cable. Note that the converter's red wire should NOT be used, as it is connected to the USB 5V power, which could damage or destroy the RN-XV. The TeraTerm monitor software is configured at 9600 baud, which is the default for the RN-XV UART.

 

Once connected, the following commands are sent to the RN-XV module:

 

$$$
factory R
reboot

 

$$$
set wlan ssid DIRECT-6U-Android_37e5
set wlan passphrase sK2QIEnk
save
reboot

 

$$$
set ip host 192.168.49.1
set ip remote 50000
set ip proto 1
set comm time 10000
set comm size 75
set comm match 13
save

reboot

 

With this configuration, the module will form and send a UDP packet from the already received data each time any one of the following conditions occurs:

• 75 bytes of serial data has been received by the module through the UART_RX pin;
• or the ASCII character 13 (carriage return) has just been received;
• or 10 seconds have occured since the last packet has been sent.

In the following arrangement, a GPS NMEA output at 9600 baud is connected to the UART_RX pin, and the RN-XV module continuously forms and sends UDP packets with the GPS data. Each packet will contain a complete NMEA sentence, as these sentences all end with the carriage return byte, and are less than 75 bytes.

 

 

 

Our next step will be to create an Android app for the phone that will capture the packets, retrieve the GPS data and display the info on the screen.