Hacking a robot

byMaxymilian Śmiech
May 15, 2019
7 minute read

Previous post have summarized “THE HACKATHON” in TouK. Today we will present one of the projects in greater detail – “Lidar/ROS.org based robot”. Our team wanted to either transport sandwiches or monitor WiFi quality in the office. Not deciding on the final goal we immediately saw that mobile robot platform will be needed in both cases.

Analysis

Some of our coworkers own Xiaomi vacuum cleaners. Such robot can be managed from mobile app which displays accurate map of your premises and allows to select areas that need cleaning. Xiaomi robot looked promising but two problems arose. First, the price is significant. Second, the communication protocol is not open. There are libraries on GitHub which try to reverse engineer the details but quick analysis has shown that we may end up stuck on some irritating problem and fail to realise our goals.

Closer examination of Xiaomi robot has revealed the core piece that allows it to automatically navigate around the house. It is LIDAR – laser distance measurement device. Another brand of Roomba vacuum cleaners relies on camera-like sensor instead. As computer vision seemed more difficult to approach we have decided on using LIDAR to build two-dimensional map of robot’s surroundings and navigate around the office.

Hardware

LIDAR technology is not as cheap as simple ultrasonic distance sensors but we have found two promising solutions on the market: YDLIDAR and RPLIDAR. Both brands are lines of different products with increasing capabilities and prices but the basic ones were within our budget. Quick comparison has shown that parameters of respective lowest-end models are similiar so we decided to order YDLIDAR because it was the quickest to ship from Amazon to Poland.

One thing to note is that core LIDAR component can be acquired for even lower price but such device will have fixed line of sight. Our chosen model, just like the one built into Xiaomi, has full 360 degree rotating head. It makes 5 to 10 rotations per second scanning 5 thousand points in that time. The output data stream contains angle and distance for each measured point.

For our robot we needed a mobile base. Common choice is base with two motorized wheels plus one or two support wheels that freely rotate in all directions giving minimal friction. We discarded that option because such simple bases for amateur constructions have weak motors and we had quite a load to put on top. Professional bases with two powerful motors are expensive. In the end we bought four-wheeled base with independent low-grade DC motors driving each wheel. The wheels are not steerable – turning is done like in a tank. Our kit was also equipped with two motor encoders – devices that measure how many times wheel has rotated – we will try to use that knowledge later.

According to the manufacturer’s data our base can bear 800 grams of load. We have put there:

Raspberry Pi 3 B+, the most powerful model available
Arduino Uno
4-channel motor shield for Arduino
YDLIDAR
LiPo battery pack
DC voltage regulators

We have decided that our robot should be fully autonomous so all the data processing will happen on-board using Raspberry Pi. It was connected via USB with Arduino which was used to control motors using standard Arduino-compatible controller. We knew that RPi has GPIO pins that can be used to control peripherals like Arduino would but we wanted to separate the concerns and use all the power of RPi for other responsibilities.

Software

We have assembled a mobile base with YDLIDAR mounted on top but YDLIDAR itself cannot build a complete map of our office and navigate around it. We needed algorithms that could interpret incoming data stream of distance measurements and convert it into usable map. We have found the ROS.org project. It is called Robot Operating System but instead of being full OS it is Linux-based framework – collection of tools and algorithms that makes programing robots easier. As hackathon was designed to deliver working products each team was given time to prepare before the main event. We have spent that time on learning ROS and gathering main components for our robot.

ROS is capable of handling LIDAR data, building a map and performing navigation of robot. If some feature is not available in ROS it can be added by coding of a “node” – separate program that communicates with another nodes using “topics” – ordered streams of events. Fortunately, all parts of our use case were already available as standard ROS nodes, topics and event types. YDLIDAR’s manufacturer provided custom ROS-compatible node which handles low-level interaction with device. There is also an Arduino relay library that makes it possible to write Arduino code that directly subscribes and publishes events to ROS topics.

Having written less than 100 lines of ROS nodes’ launch configuration in XML and less that 100 lines of Arduino code, we were able to remote control our robot and see the map on screen. We have used separate notebook which handled joystick controller and displayed a map. The notebook was configured as ROS slave connected over WiFi to ROS master running on RPi. Below we show how simple it was to setup USB joystick controller:

<launch>
  <node pkg="joy"
        type="joy_node"
        name="ross_joy"
        respawn="true" >

    <param name="autorepeat_rate" value="10" />
  </node>

  <node pkg="teleop_twist_joy"
        type="teleop_node"
        name="ross_teleop"
        respawn="true" >

    <param name="scale_linear" value="0.3" />
    <param name="scale_angular" value="0.3" />
  </node>
</launch>

Unexpected problems and spontaneous solutions

During the hackathon days we have experienced some difficulties. As we were not able to test full robot assembly before the main event, some problems have surfaced very late in the process and dirty solutions must have been quickly hacked.

First problem: power source. During initial tests we used 24V DC power source connected to wall power. It had to be calibrated because internal protection cut off the power when current drawn by motors become too high. We also had 12V LiPo battery for final tests and show. Different input voltages were converted by on-board step-down regulators. One has provided 5V needed by RPi, Arduino and YDLIDAR. Second regulator fed 10V to the motors. During the tests it appeared that YDLIDAR cannot be powered from RPi’s USB port because its voltage was not stable enough. In the end we have connected YDLIDAR power input directly to 5V output from appropriate regulator.

Second problem: jerky movement. We thought (because ROS wiki suggested so) that it would be a good design to include PID controller driving the wheels. It is an algorithm that tries to maintain one value (in our case: measured actual speed of wheels) by varying another value that directly influences the first (in our case: power applied to motors). After some tests we have disabled PID controller because it requires fine tuning to behave correctly. As our rotational wheel encoders report only 10 ticks per revolution, the PID was confused by such low measurement resolution and tried to vary motor power too sharply rendering smooth movement impossible. We believe it can be tuned properly but during the hackathon we have setup joystick to directly control motors’ power, and not robot’s target speed, making human operator responsible for adjustments.

Third problem: faulty encoders. Our will to have wheel encoders originated not from possibility to enable PID controller, but from opportunity to increase mapping precision. Knowing how much robot has moved can help to better correlate data from multiple laser scans, producing more accurate map. Unfortunately, one of encoders appeared to work incorrectly, reporting too few ticks per revolution. Not having much time to investigate that we decided to disconnect encoders completely.

Making maps

At the beginning of second day we have already known main limitations of hardware and software and decided that we will use those parts that work predictably. We confirmed that PID controller was not neccessary for our purposes and mapping can be done using laser data only. We decided to enable simplest mapping algorith in ROS – a method called Hector SLAM. We could start first tests.

At first we mapped small room with two desks in it and a glass door. We have put additional objects in the middle to see how their presence would be handled. Everything worked smoothly using default parameters of Hector method. We also confirmed that it is easy to overlay map with additional data coming from sensors – in our case it was simple photoresistor measuring light intensity in the room.

Then we moved onto mapping bigger area – the hall between rooms. There is additional wall dividing it in the middle and a pillar. We added few other objects. During the tests robot was wired to the power source. We tested how to move our bodies around so to not interfere with the measurements. It appeared that after mapping initial fragment it is safe to walk around and Hector algorithm will ignore moving objects. Only after staying for too long in the same place our legs started to be included as part of the map.

Final test shows straight corridor. Its map is bended and we are not sure of the cause. It may be related to slow scan rate of YDLIDAR which accumulates error during robot’s movement.

Future work

Having learnt ROS before the hackathon and solved mostly hardware-related problems during the event, we have shown that map building is possible even with simple setup. We would like to expand the algorithmic part of the solution, enabling our robot to autonomously move in the office environment. Proper navigation components are already available in ROS.

Conclusions

ROS is a powerful tool that can be used both by amateurs and proffesionals. Its sophisticated architecture allows for complex definitions and management of industrial-grade robots, but can also fit in quick and dirty home projects. For us the biggest challenges lied in the hardware layer, but having electronic engineer on the team helped to connect all the parts together. All Ross team members are happy with results and wish to continue the project.

Maxymilian Śmiech

— Previous article

TouK Hackathon - March 2019

Next article —

The experience of developing “Quak” during a hackathon.

Spring Security by example: securing methods

byJakub Nabrdalik
March 30, 2011

This is a part of a simple Spring Security tutorial:

1. Set up and form authentication
2. User in the backend (getting logged user, authentication, testing)
3. Securing web resources
4. Securing methods
5. OpenID (login via gmail)
6. OAuth2 (login via Facebook)
7. Writing on Facebook wall with Spring Social

Securing web resources is all nice and cool, but in a well designed application it's more natural to secure methods (for example on backend facade or even domain objects). While we may get away with role-based authorization in many intranet business applications, nobody will ever handle assigning roles to users in a public, free to use Internet service. We need authorization based on rules described in our domain.

For example: there is a service AlterStory, that allows cooperative writing of stories, where one user is a director (like a movie director), deciding which chapter proposed by other authors should make it to the final story.

The method for accepting chapters, looks like this:

DDevelopment & Design

Phonegap / Cordova and cross domain ssl request problem on android.

byPaweł Byszewski
July 29, 2013

In one app I have participated, there was a use case:

User fill up a form.
User submit the form.
System send data via https to server and show a response.

During development there wasn’t any problem, but when we were going to release production version then some unsuspected situation occurred. I prepare the production version accordingly with standard flow for Android environment:

ant release
align
signing

During conduct tests on that version, every time I try to submit the form, a connection error appear. In that situation, at the first you should check whitelist in cordova settings. Every URL you want to connect to, must be explicit type in:

res/xml/cordova.xml

If whitelist looks fine, the error is most likely caused by inner implementation of Android System. The Android WebView does not allow by default self-signed SSL certs. When app is debug-signed the SSL error is ignored, but if app is release-signed connection to untrusted services is blocked.

Workaround

You have to remember that secure connection to service with self-signed certificate is risky and unrecommended. But if you know what you are doing there is some workaround of the security problem. Behavior of method

CordovaWebViewClient.onReceivedSslError

must be changed.

Thus add new class extended CordovaWebViewClient and override ‘onReceivedSslError’. I strongly suggest to implement custom onReceiveSslError as secure as possible. I know that the problem occours when app try connect to example.domain.com and in spite of self signed certificate the domain is trusted, so only for that case the SslError is ignored.

public class MyWebViewClient extends CordovaWebViewClient {

   private static final String TAG = MyWebViewClient.class.getName();
   private static final String AVAILABLE_SLL_CN 
                               = "example.domain.com";

   public MyWebViewClient(DroidGap ctx) {
       super(ctx);
   }

   @Override
   public void onReceivedSslError(WebView view, 
                                  SslErrorHandler handler, 
                                  android.net.http.SslError error) {

       String errorSourceCName = error.getCertificate().
                                  getIssuedTo().getCName();

       if( AVAILABLE_SLL_CN.equals(errorSourceCName) ) {
           Log.i(TAG, "Detect ssl connection error: " + 
                       error.toString() + 
                       „ so the error is ignored”);

           handler.proceed();
           return;
       }

       super.onReceivedSslError(view, handler, error);
   }
}

Next step is forcing yours app to use custom implementation of WebViewClient.

public class Start extends DroidGap
{
   private static final String TAG = Start.class.getName();

   @Override
   public void onCreate(Bundle savedInstanceState)
   {
       super.onCreate(savedInstanceState);
       super.setIntegerProperty("splashscreen", R.drawable.splash);
       super.init();

       MyWebViewClient myWebViewClient = new MyWebViewClient(this);
       myWebViewClient.setWebView(this.appView);

       this.appView.setWebViewClient(myWebViewClient);
       
       // yours code

   }
}

That is all ypu have to do if minSdk of yours app is greater or equals 8. In older version of Android there is no class

android.net.http.SslError

So in class MyCordovaWebViewClient class there are errors because compliator doesn’t see SslError class. Fortunately Android is(was) open source, so it is easy to find source of the class. There is no inpediments to ‘upgrade’ app and just add the file to project. I suggest to keep original packages. Thus after all operations the source tree looks like:

Class SslError placed in source tree.

Now the app created in release mode can connect via https to services with self-signed SSl certificates.

DDevelopment & Design

Using WsLite in practice

byPiotr Jagielski
January 3, 2013

TL;DR

There is a example working GitHub project which covers unit testing and request/response logging when using WsLite.

Why Groovy WsLite ?

I’m a huge fan of Groovy WsLite project for calling SOAP web services. Yes, in a real world you have to deal with those - big companies have huge amount of “legacy” code and are crazy about homogeneous architecture - only SOAP, Java, Oracle, AIX…

But I also never been comfortable with XFire/CXF approach of web service client code generation. I wrote a bit about other posibilites in this post. With JAXB you can also experience some freaky classloading errors - as Tomek described on his blog. In a large commercial project the “the less code the better” principle is significant. And the code generated from XSD could look kinda ugly - especially more complicated structures like sequences, choices, anys etc.

Using WsLite with native Groovy concepts like XmlSlurper could be a great choice. But since it’s a dynamic approach you have to be really careful - write good unit tests and log requests. Below are my few hints for using WsLite in practice.

Unit testing

Suppose you have some invocation of WsLite SOAPClient (original WsLite example):

def getMothersDay(long _year) {
    def response = client.send(SOAPAction: action) {
       body {
           GetMothersDay('xmlns':'http://www.27seconds.com/Holidays/US/Dates/') {
              year(_year)
           }
       }
    }
    response.GetMothersDayResponse.GetMothersDayResult.text()
}

How can the unit test like? My suggestion is to mock SOAPClient and write a simple helper to test that builded XML is correct. Example using great SpockFramework:

void setup() {
   client = Mock(SOAPClient)
   service.client = client
}

def "should pass year to GetMothersDay and return date"() {
  given:
      def year = 2013
  when:
      def date = service.getMothersDay(year)
  then:
      1 * client.send(_, _) >> { Map params, Closure requestBuilder ->
            Document doc = buildAndParseXml(requestBuilder)
            assertXpathEvaluatesTo("$year", '//ns:GetMothersDay/ns:year', doc)
            return mockResponse(Responses.mothersDay)
      }
      date == "2013-05-12T00:00:00"
}

This uses a real cool feature of Spock - even when you mock the invocation with “any mark” (_), you are able to get actual arguments. So we can build XML that would be passed to SOAPClient's send method and check that specific XPaths are correct:

void setup() {
    engine = XMLUnit.newXpathEngine()
    engine.setNamespaceContext(new SimpleNamespaceContext(namespaces()))
}

protected Document buildAndParseXml(Closure xmlBuilder) {
    def writer = new StringWriter()
    def builder = new MarkupBuilder(writer)
    builder.xml(xmlBuilder)
    return XMLUnit.buildControlDocument(writer.toString())
}

protected void assertXpathEvaluatesTo(String expectedValue,
                                      String xpathExpression, Document doc) throws XpathException {
    Assert.assertEquals(expectedValue,
            engine.evaluate(xpathExpression, doc))
}

protected Map namespaces() {
    return [ns: 'http://www.27seconds.com/Holidays/US/Dates/']
}

The XMLUnit library is used just for XpathEngine, but it is much more powerful for comparing XML documents. The NamespaceContext is needed to use correct prefixes (e.g. ns:GetMothersDay) in your Xpath expressions.

Finally - the mock returns SOAPResponse instance filled with envelope parsed from some constant XML:

protected SOAPResponse mockResponse(String resp) {
    def envelope = new XmlSlurper().parseText(resp)
    new SOAPResponse(envelope: envelope)
}

Request and response logging

The WsLite itself doesn’t use any logging framework. We usually handle it by adding own sendWithLogging method:

private SOAPResponse sendWithLogging(String action, Closure cl) {
    SOAPResponse response = client.send(SOAPAction: action, cl)
    log(response?.httpRequest, response?.httpResponse)
    return response
}

private void log(HTTPRequest request, HTTPResponse response) {
    log.debug("HTTPRequest $request with content:\n${request?.contentAsString}")
    log.debug("HTTPResponse $response with content:\n${response?.contentAsString}")
}

This logs the actual request and response send through SOAPClient. But it logs only when invocation is successful and errors are much more interesting… So here goes withExceptionHandler method:

private SOAPResponse withExceptionHandler(Closure cl) {
    try {
        cl.call()
    } catch (SOAPFaultException soapEx) {
        log(soapEx.httpRequest, soapEx.httpResponse)
        def message = soapEx.hasFault() ? soapEx.fault.text() : soapEx.message
        throw new InfrastructureException(message)
    } catch (HTTPClientException httpEx) {
        log(httpEx.request, httpEx.response)
        throw new InfrastructureException(httpEx.message)
    }
}
def send(String action, Closure cl) {
    withExceptionHandler {
        sendWithLogging(action, cl)
    }
}

XmlSlurper gotchas

Working with XML document with XmlSlurper is generally great fun, but is some cases could introduce some problems. A trivial example is parsing an id with a number to Long value:

def id = Long.valueOf(edit.'@id' as String)

The Attribute class (which edit.'@id' evaluates to) can be converted to String using as operator, but converting to Long requires using valueOf.

The second example is a bit more complicated. Consider following XML fragment:

<edit id="3">
   <params>
      <param value="label1" name="label"/>
      <param value="2" name="param2"/>
   </params>
   <value>123</value>
</edit>
<edit id="6">
   <params>
      <param value="label2" name="label"/>
      <param value="2" name="param2"/>
   </params>
   <value>456</value>
</edit>

We want to find id of edit whose label is label1. The simplest solution seems to be:

def param = doc.edit.params.param.find { it['@value'] == 'label1' }
def edit = params.parent().parent()

But it doesn’t work! The parent method returns multiple edits, not only the one that is parent of given param…

Here’s the correct solution:

doc.edit.find { edit ->
    edit.params.param.find { it['@value'] == 'label1' }
}

Example

The example working project covering those hints could be found on GitHub.