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Managing Multi-Robot Fleets⿻

Estimated time to read: 6 minutes

Managing a fleet of robots whether drones, AMRs, or robotic arms demands a systematic approach to software lifecycle, observability, configuration consistency, and scalability. Robotair provides core building blocks such as robots, fleets, builds, and deployments to help you orchestrate large-scale systems with confidence.

This guide captures industry best practices drawn from drone swarms, warehouse automation, and cloud-native infrastructure to help you manage robotics fleets like a pro.

Why It Matters⿻

Poor fleet management leads to configuration drift, hard-to-debug failures, inconsistent deployments, and poor scalability. By applying these practices, your team can:

  • Reduce human error through naming, automation, and grouping
  • Enable fast and targeted OTA (over-the-air) deployments
  • Monitor health at scale and detect issues early
  • Seamlessly onboard and retire robots in the field

1. Use Standardized Naming Conventions⿻

Naming is the first step in fleet-scale clarity. It impacts your ability to automate, search, monitor, and deploy effectively.

<role>-<location>-<zone>-<robot_id>

Examples:

  • amr-eu-berlin-l3-r08
  • drone-us-west-field03-r22
  • picker-in-bangalore-zone2-r11

Best Practices⿻

  • Use lowercase and dashes (-) for consistency
  • Encode role, geography, and a stable ID
  • Avoid humanized names like Sparky, Bob, or RoverOne

Tip

Cattle, not pets: Robots in a fleet should be replaceable assets not individually maintained systems. See Cattle vs Pets.

2. Organize Robots into Fleets and Sub-Fleets⿻

Group robots using Robotair Fleets and Sub-Fleets. This simplifies operations, testing, and rollback.

Group by:

  • Environment: dev, qa, production
  • Location: usa-east, warehouse-42, berlin-hub
  • Function: pickers, sorters, explorers
  • Hardware Version: rev-a, rev-b, jetson, x86

This enables:

  • Deploying stable releases to production fleets
  • Progressive rollouts to staging/test fleets
  • Filtering dashboards and metrics per group

3. Combine Multiple Agents for Robots with Multiple Computers⿻

Many robots today contain multiple onboard computers (e.g., main controller + perception unit + edge AI module). In such cases, you can:

Register Multiple robotair-agents to the Same Logical Robot⿻

Robotair lets you associate multiple agents (running on different onboard machines) under a single logical robot in the dashboard. This enables unified monitoring and coordinated deployment.

How to structure multi-agent robots:

  • Assign each agent a unique agent ID (e.g., r08-main, r08-vision)
  • Group them under the same logical robot (e.g., amr-eu-berlin-l3-r08)
  • Optionally label agents by role using tags (compute, vision, ai, aux)

Example:

Logical Robot: amr-eu-berlin-l3-r08

  ├── Agent: amr-eu-berlin-l3-r08-main (main control node)
  └── Agent: amr-eu-berlin-l3-r08-vision (camera + GPU processing node)

This structure allows:

  • Deploying services to specific agents (e.g., only vision)
  • Independent failure recovery
  • Unified visibility under one robot identity

Tip

You can also configure services to run only on selected agents using Robotair's Services-to-Agent mapping in the deployment dashboard.

4. Version and Track Everything⿻

Ensure code, configurations, builds, and environment definitions are tracked in version control systems.

Application and CI/CD⿻

  • Use Git for source code
  • Use semantic versioning: v1.0.0, v2.1.3-beta
  • Tie builds to Git SHAs for traceability

Configuration Files⿻

  • Version control your launch files, param files, and deployment specs
  • Use .repos or .rosinstall for ROS dependencies
  • Use requirements.txt for Python packages

Note

Robotair maintains build provenance, enabling safe, auditable rollbacks across your fleet.

5. Assign Deployments at the Fleet Level⿻

Avoid assigning deployments one robot at a time. Robotair allows deployment targeting by:

  • Fleet or sub-fleet
  • Tags or labels
  • Hardware architecture or location

This enables:

  • One-click deployments to 10, 100, or 1000 robots
  • Canary rollouts with sub-fleets
  • Scheduled updates or progressive releases

Note

Try deploying v2.3.1 to surveyors/zone-east before rolling out to surveyors.

6. Monitor Fleet Health Continuously⿻

Use Robotair’s metrics and logs dashboard to:

  • Monitor deployment success/failure
  • Spot resource anomalies across robots (CPU, RAM, disk)
  • Correlate issues with builds or services

Info

Coming soon: Audit logs, historical rollouts, and per-agent system snapshots for compliance and debugging.

7. Automation for Fleet-Wide Consistency⿻

Automate with CI pipelines reduce risk and enforce consistency.

Recommended flow:

  1. Commit → trigger CI build
  2. Push container to registry
  3. Create new Robotair Deployment
  4. Assign to staging fleet
  5. Monitor and scale to production

Use Robotair Builds generated CI templates for GitHub and GitLab to automate the build → release → deploy lifecycle.

Summary⿻

Managing multi-robot fleets is all about structure, automation, and observability. Robotair gives you the tools and infrastructure. Your success depends on applying best practices:

  • Use structured naming with versionable, role-based IDs
  • Group into fleets for rollout control
  • Treat agents as cattle, not pets
  • Use multi-agent support for robots with multiple computers
  • Version and track everything
  • Deploy by fleet, not robot
  • Automate and monitor continuously