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For most multi-robot projects, start with ROS 2. It provides the communication, components and integration foundation that robot software can share. Add Open-RMF when robots from different fleets must share space, tasks or building infrastructure. Use ROS2swarm for distributed swarm behaviours, Aerostack2 for cooperative aerial robots, Robofleet for fleet communication and remote operations, and ReMRoC to simulate and benchmark coordination methods.
These projects are complementary rather than interchangeable. The right stack depends on robot type, whether fleets are homogeneous or mixed, how coordination decisions are made, and which services you need beyond basic message passing.
Which framework should you choose?
Use this decision rule before evaluating individual packages:
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- Need different vendors’ fleets to share maps, traffic lanes, tasks, doors or elevators? Add Open-RMF.
- Need flocking, formation, consensus or other distributed swarm behaviours? Add ROS2swarm to a ROS 2 system.
- Need multiple drones or other aerial vehicles to cooperate? Evaluate Aerostack2.
- Need central communication, monitoring and remote tasking for ROS-enabled service robots? Evaluate Robofleet.
- Need repeatable physics-based tests of coordination algorithms before deployment? Use ReMRoC.
A production architecture can contain several of these. ROS 2 is the base; the other projects address fleet-level operations, specialised behaviours, aerial autonomy, operations management or evaluation.
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What each open-source framework does
ROS 2: the communication and component foundation
ROS 2 is general middleware for robotics. It supplies the communication mechanisms, composable nodes and integration substrate on which navigation, perception, control and fleet software run. Each robot can expose capabilities through ROS 2 interfaces while retaining robot-specific hardware drivers and autonomy.
ROS 2 alone does not define a complete warehouse or hospital fleet policy. Its multi-robot ecosystem includes additional packages for task semantics, scheduling and infrastructure integration. Treat ROS 2 as the layer that connects software components, not as an out-of-the-box fleet manager.
Open-RMF: interoperability, scheduling and infrastructure
Open Robotics describes the Open Robotics Middleware Framework (Open-RMF) as “a free, open source, modular software system that enables sharing and interoperability between multiple fleets of robots and physical infrastructure, like doors, elevators and building management systems.”
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Open-RMF Core includes traffic scheduling, ROS 2 integration, task planning, battery estimation and utility libraries. Its traffic tools are intended both to prevent conflicts and to resolve them when they occur. A fleet adapter connects a robot or a vendor fleet manager to RMF, translating between that system’s APIs and RMF’s fleet and task concepts.
Open-RMF is therefore the strongest fit when the hard problem is coordination across independent fleets or a robot’s interaction with a building. It does not remove integration work: every robot type or vendor manager still needs a suitable adapter, configuration and testing against its real capabilities.
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ROS2swarm: reusable distributed swarm behaviours
ROS2swarm is a ROS 2 package that provides ready-to-use primitives for swarm behaviour. It is aimed at research and prototyping involving flocking, formation control and related distributed behaviours. Algorithms can be composed with each robot’s sensing, localisation and motion-control nodes.
ROS2swarm is not a complete warehouse fleet-management system. It does not replace the scheduling, charging, elevator and door integration that a building-scale deployment may require. Use it when the research question is collective behaviour rather than vendor-fleet interoperability.
Aerostack2: multi-robot aerial autonomy
Aerostack2 is an open-source ROS 2 framework designed for multi-robot aerial systems. It is a natural candidate when cooperative flight, aerial autonomy or coordination among drones is central to the project. Aerial missions introduce requirements such as flight-state management, three-dimensional trajectories and vehicle safety constraints that are outside a typical indoor mobile-robot fleet manager.
For ground robots sharing corridors and elevators, Aerostack2 is not the first choice. For aerial research, evaluate it alongside the vehicle drivers, localisation system and simulator required by the aircraft.
Robofleet: communication and remote fleet operations
Robofleet is an open-source communication and management system for fleets of ROS-enabled service robots. Its documented capabilities include inter-robot communication, remote monitoring and remote tasking through a central server.
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Choose Robofleet when connectivity and operational visibility are the primary problems: sending work to robots, observing their state and maintaining a communications path between robots and a server. If the central challenge is deconflicting traffic among independent fleets or controlling doors and elevators, Open-RMF supplies more directly relevant functions.
ReMRoC: simulation and benchmarking
ReMRoC is a physics-based simulation and benchmark framework for multi-robot coordination methods. It lets a team compare algorithms in repeatable simulated scenarios before risking hardware. ReMRoC is an evaluation environment, not a replacement for robot middleware, fleet adapters or production task management.
Framework comparison
| Framework | Primary role | Coordination style or service | Best fit | What it does not replace |
|---|---|---|---|---|
| ROS 2 | Middleware and component integration | ROS 2 communication and reusable nodes | Almost any robot project needing a shared software base | Complete fleet scheduling and building-infrastructure orchestration |
| Open-RMF | Fleet interoperability and facility coordination | Traffic scheduling, task planning, battery estimation and infrastructure integration | Heterogeneous indoor fleets, warehouses, hospitals and offices | Robot-specific drivers, navigation and the fleet adapter implementation |
| ROS2swarm | Swarm-behaviour primitives | Distributed flocking, formation and related behaviours | Research and prototypes focused on collective behaviour | Production fleet management, charging and building controls |
| Aerostack2 | Aerial multi-robot autonomy | Cooperative aerial mission behaviours on ROS 2 | Multi-drone and other aerial-robot research | General indoor fleet scheduling and non-aerial operations |
| Robofleet | Fleet communication and operations | Central-server communication, monitoring and remote tasking | ROS-enabled service-robot fleet connectivity | Full traffic deconfliction and facility-system control |
| ReMRoC | Simulation and benchmarking | Repeatable physics-based comparison of coordination methods | Algorithm evaluation before hardware deployment | Production middleware and robot operations |
A practical multi-robot architecture
1. Robot layer
Run ROS 2 nodes on each robot for hardware drivers, localisation, perception, navigation and motion control. Keep vendor-specific details behind stable ROS 2 interfaces so higher layers can issue goals and receive state without knowing motor-controller protocols.
2. Fleet and facility layer
Deploy Open-RMF when robots must negotiate shared spaces or interact with facility systems. RMF can represent fleets, schedule traffic, plan tasks, estimate battery use and coordinate resources such as doors and elevators. Each participating fleet is connected through an adapter that maps its native commands and status into RMF concepts.
3. Specialised behaviour layer
Add ROS2swarm for distributed formations or flocking, or Aerostack2 for aerial autonomy. These components can use ROS 2 communication while leaving facility scheduling and robot-specific control to the other layers.
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4. Operations and evaluation layer
Use Robofleet where a central server must monitor robots and issue remote tasks. Use RMF/Gazebo tooling or ReMRoC during development to test maps, traffic and coordination policies before connecting physical robots.
How to build the stack step by step
- Define the operating domain. Record whether the robots are indoor autonomous mobile robots, outdoor machines, manipulators, aerial vehicles or a mixture. Note map dimensions, shared spaces, mission duration and safety constraints.
- List capabilities and interfaces. For every robot, document locomotion, maximum speed, payload, localisation source, docking or charging method, battery telemetry, available sensors and the commands its existing controller accepts. Mark capabilities that are unavailable rather than assuming every robot supports the same task.
- Establish ROS 2 communication. Install the ROS 2 software required by the robot drivers and expose consistent topics, services or actions for state, navigation and task execution. Test one robot at a time before introducing fleet coordination.
- Integrate navigation and control. Verify localisation, obstacle handling, recovery behaviours and emergency-stop paths on each platform. A fleet scheduler cannot compensate for a robot that cannot reliably execute a navigation goal.
- Add Open-RMF for shared operations. Model lanes, destinations, charging points and restricted areas. Configure task types and battery information, then connect each robot implementation or vendor fleet manager through a fleet adapter.
- Connect physical infrastructure. Integrate doors, lifts and building-management interfaces through the appropriate RMF components. Define what happens when an infrastructure device is unavailable, occupied or reporting stale state.
- Add specialised frameworks only where needed. Introduce ROS2swarm for a swarm algorithm or Aerostack2 for aerial missions. Keep these behaviours behind clear interfaces so a change in formation logic does not alter safety-critical motor control.
- Simulate before deployment. Reproduce the target map, robot kinematics, communication assumptions and workload in simulation. Use repeatable scenarios to expose deadlocks, missed messages, battery depletion and recovery failures.
- Measure on representative hardware. Record task completion time, traffic waits, intervention rate, battery margin, communication loss and recovery time with the intended number and mix of robots. Scale up gradually rather than treating simulation results as a guaranteed production limit.
Choosing a coordination model
Centralised scheduling
A central scheduler maintains a global view of tasks, routes and shared resources. This model is appropriate for structured facilities where traffic must be predictable and infrastructure is shared. Open-RMF’s traffic and task-planning functions support this style. The trade-off is dependence on the scheduler’s availability, network quality and accurate state reports.
Distributed swarm control
In a distributed model, robots make local decisions from neighbour or sensor information. This can produce flocking, formation and consensus behaviours without a single traffic authority. ROS2swarm is intended for this class of research. The trade-off is that global task guarantees, deadlock handling and facility-level priorities require additional design.
Hybrid coordination
A hybrid system uses central planning for missions and shared resources while allowing local controllers or swarm behaviours to handle motion. This is often practical: Open-RMF can schedule a delivery and reserve a corridor, while each robot’s ROS 2 navigation stack handles obstacle avoidance. Explicitly define which layer has authority when local avoidance conflicts with a reservation.
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Connecting heterogeneous robots successfully
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A fleet adapter should translate RMF tasks into the vendor manager’s API and translate telemetry back into common state. It must account for different command names, coordinate frames, speed limits, docking procedures and failure codes. If a vendor API cannot report a required state, expose that limitation and design a safe fallback instead of fabricating precision.
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Normalise maps and coordinate frames
Robots may use different map formats, origins and localisation conventions. Establish one documented facility frame and conversion rules for every robot. Test the conversion at destinations, charging points, lift entrances and narrow passages where a small error can produce a collision or failed task.
Represent capabilities explicitly
Task allocation should distinguish a robot that can carry a payload, use a lift, open a door or dock at a particular charger from one that cannot. Battery estimation is useful only when each adapter reports a meaningful battery model and the task planner understands the robot’s energy requirements.
Plan for degraded operation
Define behaviour for lost network connectivity, stale poses, blocked lanes, unavailable lifts and robots that stop responding. Decide which actions are safe locally, which require a central reschedule and how operators reclaim a task after a failure.
Common pitfalls and how to avoid them
- Assuming ROS 2 is a fleet manager: use additional scheduling and task software when robots must coordinate globally.
- Expecting Open-RMF to remove integration work: budget engineering time for every fleet adapter, capability mapping and facility API.
- Using swarm packages for warehouse dispatch: distributed behaviour primitives do not supply charging, vendor interoperability or building controls by themselves.
- Ignoring network behaviour: test latency, packet loss, reconnection and clock differences under realistic load.
- Testing only identical robots: include the slowest vehicle, the largest turning radius, the weakest battery and every vendor API in representative scenarios.
- Skipping safety boundaries: retain independent emergency-stop and safe-speed mechanisms; do not rely on a planner or wireless link as the sole safety control.
- Equating simulation with deployment: validate sensor noise, localisation drift, floor conditions, lift timing and human traffic on physical systems.
Simulation, benchmarking and evidence
Simulation should answer specific questions: Can traffic reservations avoid deadlock? How does task throughput change when a lift is unavailable? How much battery margin remains after a long route? Does a distributed algorithm converge when messages are delayed?
ReMRoC is suited to repeatable physics-based comparisons of coordination methods. RMF and Gazebo tooling can model facility layouts, fleets and traffic during development. Keep scenario files, robot parameters, random seeds and workload definitions under version control so results can be reproduced.
No authoritative cross-framework benchmark establishes a universal winner for speed, reliability or maximum fleet size. The available project descriptions explain capabilities and architectures, not a common laboratory ranking. Measure performance on your robots, network, maps and workload instead of quoting an unsupported fleet-size limit.
Framework choices by project type
| Project | Recommended starting point | Additional component | Reason |
|---|---|---|---|
| Single-vendor indoor delivery fleet | ROS 2 | Open-RMF if shared facilities or advanced scheduling are required | Begin with reliable robot interfaces; add fleet-level coordination as the environment demands it. |
| Hospital with robots from several vendors | ROS 2 plus Open-RMF | Robofleet for central monitoring where appropriate | Interoperability, traffic and lifts or doors are central requirements. |
| Formation or flocking research | ROS 2 | ROS2swarm | The research focus is distributed collective behaviour. |
| Cooperative drone mission | ROS 2 | Aerostack2 | Aerial autonomy and multi-vehicle coordination are the defining requirements. |
| Algorithm comparison before buying hardware | ROS 2-compatible simulation | ReMRoC | Repeatable physics-based evaluation reduces deployment risk. |
Bottom line for architecture decisions
Build around ROS 2, then add only the layer that solves your coordination problem. Open-RMF is the key addition for heterogeneous fleets, traffic scheduling, battery-aware tasks and building infrastructure. ROS2swarm and Aerostack2 target specialised swarm and aerial behaviours; Robofleet targets communication and remote operations; ReMRoC targets simulation and benchmarking. Validate the resulting combination on your actual robots and workload, because no published comparison proves that one framework is universally faster, more reliable or scalable to a particular fleet size.
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