Monitor Air Quality with a Fleet of Sensors

In this tutorial you will use a fleet of devices to collect air quality data from different places and display the most recent readings from each device in a custom viewing dashboard.

Air quality dashboard in a web browser with PM2.5 readings from three different sensor machines displayed.

Requirements

You can complete this tutorial using any number of air quality sensing machines.

For each machine, you will need the following hardware:

In addition to viam-server, this tutorial uses the following software:

Decide how you will organize your fleet

Before you start connecting your devices to the Viam app, you’ll need to decide how you want to group your devices.

In the Viam app, machines are grouped into locations, and locations are grouped into organizations. Each location can represent either a physical location or some other conceptual grouping. An organization is the highest level grouping, and often contains all the locations (and machines) of an entire company. These groupings allow you to manage permissions; you can grant a user access to an individual machine, to all the machines in a location, or to everything in an entire organization. You choose how to group your machines.

Tree diagram with three locations that belong to one organization. One location contains sub-location A containing two machines and sub-location B containing one machine. Each of the two other locations contains machines directly with no sub-locations.

For more information, see Fleet Management.

Example

Imagine you create an air quality monitoring company called Pollution Monitoring Made Simple. Anyone can sign up and order one of your sensing machines. When a new customer signs up, you assemble a new machine with a sensor, SBC, and power supply.

Before shipping the sensor machine to your new client, you connect the machine to the Viam app and configure it. To manage all your company’s air quality sensing machines together, you create one organization called Pollution Monitoring Made Simple. Inside that organization, you create a location for each customer. You have some individual customers, for example Antonia, who have a sensor machine in their home, or perhaps one inside and one outside. You have other customers who are businesses, for example RobotsRUs, who have two offices, one in New York and one in Oregon, with multiple sensor machines in each. RobotsRUs wants to separate their sensor data by physical location, so you create a location for RobotsRUs and then create sub-locations to group their New York sensor machines and their Oregon machines.

When you grant Antonia access to her location, she will be able to view data from the air sensors at her home. When you grant RobotsRUs access to their location, they will be able to view data from all of their sub-locations, or they can choose to spin up a dashboard showing data from only one sub-location at a time. You, as the organization owner, will be able to manage any necessary configuration changes for all air sensing machines in all locations created within the Pollution Monitoring Made Simple organization.

Diagram of the Pollution Monitoring Made Simple organization. In it are two locations: Antonia's HOme and Robots R Us. Robots R Us contains two sub-locations, each containing some machines. The Antonia's Home location contains two machines (and no sub-locations).

Organize your fleet

For this tutorial, we will walk through how to set up your fleet based on the example above. You can choose to manage your fleet of machines differently based on what makes sense for your use case; if you’re only configuring one or two sensors for personal use, feel free to add all your machines to one location and skip to the next section.

  1. Navigate to the Viam app in a web browser. Create an account and log in.

  2. Click the dropdown in the upper-right corner of the FLEET page and use the + button to create a new organization for your air quality machine company. Name the organization and click Create.

  3. Click FLEET in the upper-left corner of the page. A new location called First Location is automatically generated for you. Rename it so you can use it for Antonia’s machines:

    Click the pencil icon next to the location name, change it to Antonia's Home, then click Save.

  4. Now, create a separate location for RobotsRUs:

    On the left side of the FLEET page, find the New location interface. Type in RobotsRUs and click Add.

  5. Add sub-locations to the RobotsRUs location to group the machines at each of their offices:

    Add a new location called Oregon Office using the same New location interface. Then, find the New parent location dropdown on the Oregon Office page. Select RobotsRUs and click Change.

    Repeat to add the New York office: Add a new location called New York Office, then change its parent location to RobotsRUs.

    The New York Office fleet page. The left Locations navigation panel lists Antonia's Home and RobotsRUs, with New York Office and Oregon Office nested inside RobotsRUs.

    In the next section, you’ll add machines to the locations.

Connect your machines to the Viam app

With your organizational structure in place, let’s add some machines:

  1. Connect your first single-board computer to power. For this tutorial, we’ll treat this as the machine for our first customer, Antonia. If the computer does not already have a Viam-compatible operating system installed, follow the Prepare your board steps in the Installation Guide to install a compatible operating system. You do not need to follow the “Install viam-server” section; you will do that in the next step!

  2. Enable serial communication so that the SBC can communicate with the air quality sensor. For example, if you are using a Raspberry Pi, SSH to it and enable serial communication in raspi-config.

  3. Click Antonia’s Home in the left navigation menu to navigate to that location’s page. In the New machine field near the top-right corner of the screen, type in a name for the machine, such as Home Air Quality Sensor, and click Add machine.

  4. You’ll be taken to the machine details page and prompted to set up your machine part. Click View setup instructions. You can find these instructions later if you need them by clicking the part status indicator (which currently reads Awaiting setup).

  5. Follow the Set up your machine part instructions to install viam-server on the machine and connect it to the Viam app. viam-server is the binary that runs on the single-board computer (SBC), providing functionality including sensor data collection and connection to the Viam app.

    The setup page will indicate when the machine is successfully connected.

  6. If Antonia has more than one air sensing machine, add a new machine to her location and set it up in the same way.

This is how you set up one machine. If you are following along for the RobotsRUs business from our example, create additional machines in each sub-location, that is, in the Oregon Office location and in the New York Office location.

Set up your hardware

For each sensing machine:

  1. Connect the PM sensor to a USB port on the machine’s SBC.

  2. Position your sensing machines in strategic locations, and connect them to power. Here are some ideas for where to place sensing machines:

    • At home:
      • In an outdoor location protected from weather, such as under the eaves of your home
      • In the kitchen, where cooking can produce pollutants
      • Anywhere you spend lots of time indoors and want to measure exposure to pollutants
    • At work:
      • At your desk to check your exposure throughout the day
      • Near a door or window to see whether pollutants are leaking in

Configure your air quality sensors

You need to configure your hardware so that each of your machines can communicate with its attached air quality sensor.

No matter how many sensing machines you use, you can configure them efficiently by using a reusable configuration block called a fragment. Fragments are a way to share and manage identical machine configurations across multiple machines. Instead of going through all the configuration steps for each machine, you’ll start by configuring just one machine and create a fragment based on that machine’s configuration. Then, you’ll add the fragment to each of your machines. With all your machines configured using the same fragment, if you need to update the config in the future, you can just update the fragment and all machines will automatically get the update.

Configure your first machine

Configure the sensor

  1. Navigate to the CONFIGURE tab of the machine details page in the Viam app for your first machine.

  2. Click the + (Create) button and click Component from the dropdown. Click sensor, then search for sds011 and click sds001:v1 from the results.

  3. Click Add module. This adds the module that provides the sensor model that supports the specific hardware we are using for this tutorial.

    The Add Module button that appears after you click the model name.

  4. Give the sensor a name like PM_sensor and click Create.

  5. In the newly created PM_sensor card, replace the contents of the attributes box (the empty curly braces {}) with the following:

    {
      "usb_interface": "<REPLACE WITH THE PATH YOU IDENTIFY>"
    }
    
  6. Now you need to figure out which port your sensor is connected to on your board. SSH to your board and run the following command:

    ls /dev/serial/by-id
    

    This should output a list of one or more USB devices attached to your board, for example usb-1a86_USB_Serial-if00-port0. If the air quality sensor is the only device plugged into your board, you can be confident that the only device listed is the correct one. If you have multiple devices plugged into different USB ports, you may need to choose one path and test it, or unplug something, to figure out which path to use.

    Now that you have found the identifier, put the full path to the device into your config, for example:

    {
      "usb_interface": "/dev/serial/by-id/usb-1a86_USB_Serial-if00-port0"
    }
    
  7. Save the config. Your machine config should now resemble the following:

    Configure tab showing PM sensor and the sensor module configured.

Configure data capture and sync

You have configured the sensor so the board can communicate with it, but sensor data is not yet being saved anywhere. Viam’s data management service lets you capture data locally from each sensor and then sync it to the cloud where you can access historical sensor data and see trends over time. Once you configure the rest of your sensing machines, you’ll be able to remotely access data from all sensors in all locations, and when you’re ready, you can give customers access to the data from the sensors in their locations.

Configure data capture and sync as follows:

  1. Click the + (Create) button and click Service from the dropdown.
  2. Click data management.
  3. Give your data manager a name such as the auto-populated name data_manager-1 and click Create.
  4. Toggle Syncing to the on position. Set the sync interval to 0.05 minutes so that data syncs to the cloud every 3 seconds. You can change the interval if you like, just don’t make it too long or you will have to wait a long time before you see your data!
  5. Let’s add a tag to all your data so that you can query data from all your air quality sensors more easily in later steps. In the Tags field, type air-quality and click + Tag: air-quality when it appears to create a new tag. This tag will now automatically be applied to all data collected by this data manager.
  6. Now the data management service is available to any components on your machine, and you can set up data capture on the sensor:
  7. On your PM_sensor card, click Add method.
  8. From the Type dropdown, select Readings.
  9. Set the Frequency to 0.1 readings per second. This will capture air quality data once every ten seconds. It is useful to capture data frequently for testing purposes, but you can always change this frequency later since you probably don’t need to capture data this frequently all day forever.
  10. Save the config.

Create a fragment

While you configured your machine with the builder UI, the Viam app generated a JSON configuration file with all your parameters. This is the file that tells viam-server what resources are available to it and how everything is connected. Click JSON in the upper-left corner of the CONFIGURE tab to view the generated JSON file. You can manually edit this file instead of using the builder UI if you are familiar with JSON.

In any case, now that the JSON is generated, you are ready to create a fragment:

  1. Select and copy the entire contents of the JSON config.

  2. Navigate to the FLEET page and click Fragments at the bottom of the left nav.

  3. Type in a name for your fragment, such as air-sensing-machine and click Add fragment.

  4. Replace the empty curly braces {} with the config you copied from your machine.

  5. Click Save fragment.

  6. Now, you can actually delete the entire config from your machine! In the next section, you will replace it with the fragment you just created so that it gets updated alongside all your other machines when you update the fragment in the future.

    Navigate back to your machine’s CONFIGURE tab, select JSON mode, and delete the entire contents of the config. When you try to save, you’ll get an invalid JSON error because it can’t be empty. Put in a set of curly braces {} and then save the config successfully.

Add the fragment to all your machines

Add the fragment you just created to each of your machines including the first one:

  1. Click the + button, then click Insert fragment in the dropdown menu.

  2. Search for and click the name of your fragment, for example air-sensing-machine.

    The insert fragment UI.

  3. Click Insert fragment. The module, sensor, and data manager will appear in your config.

  4. Save the config.

  5. Repeat these steps on the machine details page for each of your air quality sensing machines.

Test your sensors

Now that all your hardware is configured, it’s a good idea to make sure readings are being gathered by the sensors and sent to the cloud before proceeding with the tutorial. For each machine:

  1. Go to the machine details page in the Viam app and navigate to the CONTROL tab.

  2. Within the Sensors section, click Get Readings for the PM_sensor. If the sensor software and hardware is working, you should see values populate the Readings column.

    The sensor readings on the control tab.

    If you do not see readings, check the LOGS tab for errors, double-check that serial communication is enabled on the singe board computer, and check that the usb_interface path is correctly specified (click below).

    Click here for usb_interface troubleshooting help

Test data sync

Next, check that data is being synced from your sensors to the cloud:

  1. Open your DATA page.

  2. Click the Sensors tab within the data page.

  3. If you have sensor data coming from machines unrelated to this project, use the filters on the left side of the page to view data from only your air quality sensors. Click the Tags dropdown and select the air-quality tag you applied to your data. You can also use these filters to show the data from one of your air quality sensors at a time by typing a machine name into the Machine name box and clicking Apply in the lower-left corner.

    The sensor readings that have synced to the DATA page.

Once you’ve confirmed that data is being collected and synced correctly, you’re ready to start building a dashboard to display the data. If you’d like to graph your data using a Grafana dashboard, try our Visualize Data with Grafana tutorial. If you’d like to create your own customizable dashboard using the Viam TypeScript, continue with this tutorial.

Code your custom TypeScript dashboard

The Viam TypeScript SDK allows you to build custom web interfaces to interact with your machines. For this project, you’ll use it to build a page that displays air quality sensor data for a given location. You’ll host the website locally on your personal computer, and view the interface in a web browser on that computer.

As you’ll find out in the authentication step, you can set each customer up with credentials to access the data from only their location, or you can create a dashboard showing data from all sensors in your entire organization.

The air quality dashboard you’ll build. This one has PM2.5 readings from two different sensor machines displayed, and a key with categories of air quality.

Set up your TypeScript project

Complete the following steps on your laptop or desktop. You don’t need to install or edit anything else on your machine’s single-board computer (aside from viam-server which you already did); you’ll be running the TypeScript code from your personal computer.

  1. Make sure you have the latest version of Node.JS installed on your computer.

  2. Install the Viam TypeScript SDK by running the following command in your terminal:

    npm install --save @viamrobotics/sdk
    
  3. Create a directory on your laptop or desktop for your project. Name it aqi-dashboard.

  4. Create a file in your aqi-dashboard folder and name it package.json. The package.json file holds necessary metadata about your project. Paste the following contents into it:

    {
      "name": "air-quality-dashboard",
      "description": "A dashboard for visualizing data from air quality sensors.",
      "scripts": {
        "start": "esbuild ./main.ts --bundle --outfile=static/main.js --servedir=static --format=esm",
        "test": "echo \"Error: no test specified\" && exit 1"
      },
      "author": "<YOUR NAME>",
      "license": "ISC",
      "devDependencies": {
        "esbuild": "*"
      },
      "dependencies": {
        "@viamrobotics/sdk": "^0.13.0",
        "bson": "^6.6.0"
      }
    }
    

Authenticate your code to your Viam app location

Your TypeScript code requires an API key to establish a connection to your machines. You can set up credentials to access data from all the sensor machines in your organization, or from just one location.

In our example you could create a dashboard for Antonia with an API key to see the data from her location, and create a separate dashboard for RobotsRUs with a different API key to access the data from their location. If RobotsRUs wanted to separate their dashboards by sub-locations, you could set up API keys for RobotsRUs to access data for each of their sub-locations separately, or you could modify the example code to filter data by location name.

You can then either deploy each dashboard on a web server you manage, or add a web server on one machine per customer that hosts the dashboard for the respective customer so that they can access their data on their local network. We leave this step to the reader.

The following instructions describe how to set up an API key for one location.

  1. Create another file inside the aqi-dashboard folder and name it main.ts. Paste the following code into main.ts:

    // Air quality dashboard
    
    import * as VIAM from "@viamrobotics/sdk";
    import { BSON } from "bson";
    
    async function main() {
      const opts: VIAM.ViamClientOptions = {
        credential: {
          type: "api-key",
          // Key with location operator permissions
          // Replace <API-KEY> (including angle brackets)
          payload: "<API-KEY>",
          // Replace <API-KEY-ID> (including angle brackets)
          authEntity: "<API-KEY-ID>",
        },
      };
    
      const orgID: string = "<ORGANIZATION ID>"; // Replace
      const locationID: string = "<LOCATION ID>"; // Replace
    
      // <Insert data client and query code here in later steps>
    
      // <Insert HTML block code here in later steps>
    }
    
    // <Insert getLastFewAv function definition here in later steps>
    
    main().catch((error) => {
      console.error("encountered an error:", error);
    });
    
  2. Now you need to get the API key and the organization and location IDs to replace the placeholder strings in the code you just pasted.

    In the Viam app, navigate to the location page for the location containing your air quality machines.

    The location secret with a Copy button next to it.

    Copy the Location ID and paste it into your code in place of <LOCATION ID>, so that that line resembles const orgID: string = "abcde12345".

  3. Use the dropdown menu in the upper-right corner of the page to navigate to your organization settings page. Copy the Organization ID found under Details near the top of the page. Paste it in place of <ORGANIZATION ID> in your code.

  4. Under the API Keys heading, click Generate Key.

  5. Name your key something such as air-sensors-key.

  6. Select Resource and choose the location you have all your air quality sensing machines in.

  7. Set the Role to Owner, then click Generate key.

  8. Copy the ID and corresponding key you just created and paste them in place of <API-KEY> and <API-KEY-ID> in your code. For example, you’ll now have something of the form

    authEntity: '1234abcd-123a-987b-1234567890abc',
    payload: 'abcdefg987654321abcdefghi'
    

Add functionality to your code

  1. Now that you have the API key and org and location IDs, you are ready to add code that establishes a connection from the computer running the code to the Viam cloud where the air quality sensor data is stored. You’ll create a Viam dataClient instance which accesses all the data in your location, and then query this data to get only the data tagged with the air-quality tag you applied with your data service configuration. The following code also queries the data for a list of the machines that have collected air quality data so that later, you can make a dashboard that has a place for the latest data from each of them.

    Paste the following code into the main function of your main.ts script, directly after the locationID line, in place of // <Insert data client and query code here in later steps>:

    // Instantiate data_client and get all
    // data tagged with "air-quality" from your location
    const client = await VIAM.createViamClient(opts);
    const myDataClient = client.dataClient;
    const query = {
      $match: {
        tags: "air-quality",
        location_id: locationID,
        organization_id: orgID,
      },
    };
    const match = { $group: { _id: "$robot_id" } };
    // Get a list of all the IDs of machines that have collected air quality data
    const BSONQueryForMachineIDList = [
      BSON.serialize(query),
      BSON.serialize(match),
    ];
    let machineIDs: any = await myDataClient?.tabularDataByMQL(
      orgID,
      BSONQueryForMachineIDList,
    );
    // Get all the air quality data
    const BSONQueryForData = [BSON.serialize(query)];
    let thedata: any = await myDataClient?.tabularDataByMQL(
      orgID,
      BSONQueryForData,
    );
    
  2. For this project, your dashboard will display the average of the last five readings from each air sensor. You need a function to calculate that average. The data returned by the query is not necessarily returned in order, so this function must put the data in order based on timestamps before averaging the last five readings.

    Paste the following code into main.ts after the end of your main function, in place of // <Insert getLastFewAv function definition here in later steps>:

    // Get the average of the last few readings from a given sensor
    async function getLastFewAv(alltheData: any[], machineID: string) {
      // Get just the data from this machine
      let thedata = new Array();
      for (const entry of alltheData) {
        if (entry.robot_id == machineID) {
          thedata.push({
            PM25: entry.data.readings["pm_2.5"],
            time: entry.time_received,
          });
        }
      }
    
      // Sort the air quality data from this machine
      // by timestamp
      thedata = thedata.sort(function (a, b) {
        let x = a.time.toString();
        let y = b.time.toString();
        if (x < y) {
          return -1;
        }
        if (x > y) {
          return 1;
        }
        return 0;
      });
    
      // Add up the last 5 readings collected.
      // If there are fewer than 5 readings, add all of them.
      let x = 5; // The number of readings to average over
      if (x > thedata.length) {
        x = thedata.length;
      }
      let total = 0;
      for (let i = 1; i <= x; i++) {
        const reading: number = thedata[thedata.length - i].PM25;
        total += reading;
      }
      // Return the average of the last few readings
      return total / x;
    }
    
  3. Now that you’ve defined the function to sort and average the data for each machine, you’re done with all the dataClient code. The final piece you need to add to this script is a way to create some HTML to display data from each machine in your dashboard.

    Paste the following code into the main function of main.ts, in place of // <Insert HTML block code here in later steps>:

    // Instantiate the HTML block that will be returned
    // once everything is appended to it
    let htmlblock: HTMLElement = document.createElement("div");
    
    // Display the relevant data from each machine to the dashboard
    for (const mach of machineIDs) {
      let insideDiv: HTMLElement = document.createElement("div");
      let avgPM: number = await getLastFewAv(thedata, mach._id);
      // Color-code the dashboard based on air quality category
      let level: string = "blue";
      switch (true) {
        case avgPM < 12.1: {
          level = "good";
          break;
        }
        case avgPM < 35.5: {
          level = "moderate";
          break;
        }
        case avgPM < 55.5: {
          level = "unhealthy-sensitive";
          break;
        }
        case avgPM < 150.5: {
          level = "unhealthy";
          break;
        }
        case avgPM < 250.5: {
          level = "very-unhealthy";
          break;
        }
        case avgPM >= 250.5: {
          level = "hazardous";
          break;
        }
      }
      // Create the HTML output for this machine
      insideDiv.className = "inner-div " + level;
      insideDiv.innerHTML =
        "<p>" +
        mach._id +
        ": " +
        avgPM.toFixed(2).toString() +
        " &mu;g/m<sup>3</sup></p>";
      htmlblock.appendChild(insideDiv);
    }
    
    // Output a block of HTML with color-coded boxes for each machine
    return document.getElementById("insert-readings").replaceWith(htmlblock);
    

The full code is available for reference on GitHub.

Style your dashboard

You have completed the main TypeScript file that gathers and sorts the data. Now, you’ll create a page to display the data.

  1. Create a folder called static inside your aqi-dashboard folder. Inside the static folder, create a file called index.html. This file specifies the contents of the webpage that you will see when you run your code. Paste the following into index.html:

    <!doctype html>
    <html>
    <head>
     <link rel="stylesheet" href="style.css">
    </head>
    <body>
     <div id="main">
       <div>
         <h1>Air Quality Dashboard</h1>
       </div>
       <script type="module" src="main.js"></script>
       <div>
         <h2>PM 2.5 readings</h2>
         <p>The following are averages of the last few readings from each machine:</p>
       </div>
       <div id="insert-readings">
         <p><i>Loading data...
           It may take a few moments for the data to load.
           Do not refresh page.</i></p>
       </div>
       <br>
       <div class="key">
         <h4 style="margin:5px 0px">Key:</h4>
         <p class="good">Good air quality</p>
         <p class="moderate">Moderate</p>
         <p class="unhealthy-sensitive">Unhealthy for sensitive groups</p>
         <p class="unhealthy">Unhealthy</p>
         <p class="very-unhealthy">Very unhealthy</p>
         <p class="hazardous">Hazardous</p>
       </div>
       <p>
         After the data has loaded, you can refresh the page for the latest readings.
       </p>
     </div>
    </body>
    </html>
    
  1. Now you’ll create a style sheet to specify the fonts, colors, and spacing of your dashboard. Create a new file inside your static folder and name it style.css.

  2. Paste the following into style.css:

    body {
      font-family: Helvetica;
      margin-left: 20px;
    }
    
    div {
      background-color: whitesmoke;
    }
    
    h1 {
      color: black;
    }
    
    h2 {
      font-family: Helvetica;
    }
    
    .inner-div {
      font-family: monospace;
      border: .2px solid;
      background-color: lightblue;
      padding: 20px;
      margin-top: 10px;
      max-width: 320px;
      font-size: large;
    }
    
    .key {
      max-width: 200px;
      padding: 0px 5px 5px;
    }
    
    .key p {
      padding: 4px;
      margin: 0px;
    }
    
    .good {
      background-color: lightgreen;
    }
    
    .moderate {
      background-color: yellow;
    }
    
    .unhealthy-sensitive {
      background-color: orange;
    }
    
    .unhealthy {
      background-color: red;
    }
    
    .very-unhealthy {
      background-color: violet;
    }
    
    .hazardous {
      color: white;
      background-color: purple;
    }
    
    #main {
      max-width:600px;
      padding:10px 30px 10px;
    }
    

    Feel free to adjust any of the colors, margins, fonts, and other specifications in style.css based on your preferences.

Full tutorial code

You can find all the code in the GitHub repo for this tutorial.

Run the code

  1. In a command prompt terminal, navigate to your aqi-dashboard directory. Run the following command to start up your air quality dashboard:

    npm start
    

    Terminal window with the command ’npm start’ run inside the aqi-dashboard folder. The output says ‘start’ and then ’esbuild’ followed by the esbuild string from the package.json file you configured. Then there’s ‘Local:’ followed by a URL and ‘Network:’ follwed by a different URL.

  2. The terminal should output a line such as Local: http://127.0.0.1:8000/. Copy the URL the terminal displays and paste it into the address bar in your web browser. The data may take up to approximately 5 seconds to load, then you should see air quality data from all of your sensors. If the dashboard does not appear, right-click the page, select Inspect, and check for errors in the console.

    Air quality dashboard in a web browser with PM2.5 readings from three different sensor machines displayed.

    Great work. You’ve learned how to configure a fleet of machines, sync their data to one place, and pull that data into a custom dashboard using TypeScript.

Next steps

Now that you can monitor your air quality, you can try to improve it and see if your efforts are effective. You might try putting an air filter in your home or office and comparing the air quality data before you start running the filter with air quality after you have run the filter for a while. Or, try sealing gaps around doors, and check whether your seal is working by looking at your dashboard.

You could set up a text or email alert when your air quality passes a certain threshold. For instructions on setting up an email alert, see the Monitor Helmet Usage tutorial as an example. For an example of setting up text alerts, see the Detect a Person and Send a Photo tutorial.

For another example of a custom TypeScript interface, check out the Claw Game tutorial. Instead of displaying data, the claw game interface has buttons to control a robotic arm.

In this tutorial we covered configuring a fleet of machines using fragments, but to automate the setup process further, you can use the Viam Agent to provision machines.

Have questions, or want to meet other people working on robots? Join our Community Discord.

If you notice any issues with the documentation, feel free to file an issue or edit this file.