MavLink and Google Cloud Monitoring Integration

Input and output integration overview

This plugin collects metrics from MavLink-compatible flight controllers like ArduPilot and PX4, enabling live data ingestion from unmanned systems such as drones and boats.

The Stackdriver plugin allows users to send metrics directly to a specified project in Google Cloud Monitoring, facilitating robust monitoring capabilities across their cloud resources.

Integration details

MavLink

The MavLink plugin is designed to gather metrics from MavLink-compatible flight controllers such as ArduPilot and PX4. It provides a mechanism to live ingest flight metrics from various unmanned systems, including drones, planes, and boats. By utilizing the ArduPilot-specific MavLink dialect, the plugin parses a wide range of messages as documented in the MavLink documentation. It enables seamless integration of telemetry data, allowing for detailed monitoring and analysis of flight operations. Users must be cautious, as this plugin may generate a substantial volume of data; thus, filters are available to limit the metrics collected and transmitted to output plugins. Additionally, configuration options allow customization of which messages to receive and how to connect to the flight controller.

Google Cloud Monitoring

This plugin writes metrics to a project in Google Cloud Monitoring, which used to be known as Stackdriver. Authentication is a prerequisite and can be achieved via service accounts or user credentials. The plugin is designed to group metrics by a namespace variable and metric key, facilitating organized data management. However, users are encouraged to use the official naming format for enhanced query efficiency. The plugin supports additional configurations for managing metric representation and allows tags to be treated as resource labels. Notably, it imposes certain restrictions on the data it can accept, such as not allowing string values or points that are out of chronological order.

Configuration

MavLink

[[inputs.mavlink]]
  ## Flight controller URL supporting serial port, UDP and TCP connections.
  ## Options are documented at
  ##   https://mavsdk.mavlink.io/v1.4/en/cpp/guide/connections.html.
  ##
  ## Examples:
  ## - Serial port: serial:///dev/ttyACM0:57600
  ## - TCP client:  tcp://192.168.1.12:5760
  ## - UDP client:  udp://192.168.1.12:14550
  ## - TCP server:  tcpserver://:5760
  ## - UDP server:  udpserver://:14550
  # url = "tcp://127.0.0.1:5760"

  ## Filter to specific messages. Only the messages in this list will be parsed.
  ## If blank or unset, all messages will be accepted. Glob syntax is accepted.
  ## Each message in this list should be lowercase camel_case, with "message_"
  ## prefix removed, eg: "global_position_int", "attitude"
  # filter = []

  ## Mavlink system ID for Telegraf. Only used if the mavlink plugin is sending 
  ## messages, eg. when `stream_request_frequency` is 0 (see below.)
  # system_id = 254

  ## Determines whether the plugin sends requests to subscribe to data.
  ## In mavlink, stream rates must be configured before data is received.
  ## This config item sets the rate in Hz, with 0 disabling the request.
  ## 
  ## This frequency should be set to 0 if your software already controls the 
  ## rates using REQUEST_DATA_STREAM or MAV_CMD_SET_MESSAGE_INTERVAL
  ## (See https://mavlink.io/en/mavgen_python/howto_requestmessages.html)
  # stream_request_frequency = 4

Google Cloud Monitoring

[[outputs.stackdriver]]
  ## GCP Project
  project = "project-id"

  ## Quota Project
  ## Specifies the Google Cloud project that should be billed for metric ingestion.
  ## If omitted, the quota is charged to the service account’s default project.
  ## This is useful when sending metrics to multiple projects using a single service account.
  ## The caller must have the `serviceusage.services.use` permission on the specified project.
  # quota_project = ""

  ## The namespace for the metric descriptor
  ## This is optional and users are encouraged to set the namespace as a
  ## resource label instead. If omitted it is not included in the metric name.
  namespace = "telegraf"

  ## Metric Type Prefix
  ## The DNS name used with the metric type as a prefix.
  # metric_type_prefix = "custom.googleapis.com"

  ## Metric Name Format
  ## Specifies the layout of the metric name, choose from:
  ##  * path: 'metric_type_prefix_namespace_name_key'
  ##  * official: 'metric_type_prefix/namespace_name_key/kind'
  # metric_name_format = "path"

  ## Metric Data Type
  ## By default, telegraf will use whatever type the metric comes in as.
  ## However, for some use cases, forcing int64, may be preferred for values:
  ##   * source: use whatever was passed in
  ##   * double: preferred datatype to allow queries by PromQL.
  # metric_data_type = "source"

  ## Tags as resource labels
  ## Tags defined in this option, when they exist, are added as a resource
  ## label and not included as a metric label. The values from tags override
  ## the values defined under the resource_labels config options.
  # tags_as_resource_label = []

  ## Custom resource type
  # resource_type = "generic_node"

  ## Override metric type by metric name
  ## Metric names matching the values here, globbing supported, will have the
  ## metric type set to the corresponding type.
  # metric_counter = []
  # metric_gauge = []
  # metric_histogram = []

  ## NOTE: Due to the way TOML is parsed, tables must be at the END of the
  ## plugin definition, otherwise additional config options are read as part of
  ## the table

  ## Additional resource labels
  # [outputs.stackdriver.resource_labels]
  #   node_id = "$HOSTNAME"
  #   namespace = "myapp"
  #   location = "eu-north0"

Input and output integration examples

MavLink

1. Real-Time Fleet Monitoring: Utilize the MavLink plugin to create a centralized dashboard for monitoring multiple drones in real-time. By ingesting metrics from various flight controllers, operators can oversee the status, health, and location of all drones, allowing for quick decision-making and enhanced situational awareness. This integration could significantly improve coordination during large-scale operations, like aerial surveys or search and rescue missions.

2. Automated Anomaly Detection: Leverage MavLink in conjunction with machine learning algorithms to detect anomalies in flight data. By continuously monitoring metrics such as altitude, speed, and battery status, the system can alert operators to deviations from normal behavior, potentially indicating technical malfunctions or safety issues. This proactive approach can enhance safety and reduce the risk of in-flight failures.

3. Data-Driven Maintenance Scheduling: Integrate the data collected through the MavLink plugin with maintenance management systems to optimize maintenance schedules based on actual flight metrics. Analyzing the collected data can highlight patterns indicating when specific components are likely to fail, thereby enabling predictive maintenance strategies that minimize downtime and repair costs.

4. Enhanced Research Analytics: For academic and commercial UAV research, MavLink can be used to gather extensive flight data for analysis. By compiling metrics over numerous flights, researchers can uncover insights related to flight patterns, environmental interactions, and the efficiency of different drone models. This can foster advancements in UAV technology and broader applications in autonomous systems.

Google Cloud Monitoring

1. Multi-Project Metric Aggregation: Use this plugin to send aggregated metrics from various applications across different projects into a single Google Cloud Monitoring project. This use case helps centralize metrics for teams managing multiple applications, providing a unified view for performance monitoring and enhancing decision-making. By configuring different quota projects for billing, organizations can ensure proper cost management while benefiting from a consolidated monitoring strategy.

2. Anomaly Detection Setup: Integrate the plugin with a machine learning-based analytics tool that identifies anomalies in the collected metrics. Using the historical data provided by the plugin, the tool can learn normal baseline behavior and promptly alert the operations team when unusual patterns arise, enabling proactive troubleshooting and minimizing service disruptions.

3. Dynamic Resource Labeling: Implement dynamic tagging by utilizing the tags_as_resource_label option to adaptively attach resource labels based on runtime conditions. This setup allows metrics to provide context-sensitive information, such as varying environmental parameters or operational states, enhancing the granularity of monitoring and reporting without changing the fundamental metric structure.

4. Custom Metric Visualization Dashboards: Leverage the data collected by the Google Cloud Monitoring output plugin to feed a custom metrics visualization dashboard using a third-party framework. By visualizing metrics in real-time, teams can achieve better situational awareness, notably by correlating different metrics, improving operational decision-making, and streamlining performance management workflows.

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