Fleet coordination

Many robots. One system.

Multi-robot warehouse coordination

Shared traffic planning and task allocation bring a mixed robot fleet together.

YF / 04 Fleet coordination Multi-robot warehouse coordination — conceptual system illustration
System concept Illustration / 04
Industry
E-commerce fulfilment
Our role
Fleet software & traffic management
Core tools
Open-RMF · ROS 2 · Fleet scheduling
01 / The challenge

A practical problem.

A growing warehouse fleet was creating congestion in narrow aisles. Separate vendor systems could not coordinate the robots, and static routes broke down whenever traffic changed.

Engagement
10 months · Sep 2023–Jun 2024
Project setting
E-commerce fulfilment centre · Leeds
02 / Our approach

Built around the constraints.

  1. Plan together.

    A central traffic manager reserves routes and resolves conflicts between robots.

  2. Connect the fleet.

    Open-RMF adapters bring three different robot models into a common coordination system.

  3. Keep work moving.

    Task allocation and charging schedules account for battery levels, distance, and congestion.

03 / The system

How it comes together.

Components → processing → output

Warehouse requests become coordinated robot tasks, with live fleet state feeding back into routing and allocation.

  1. Dispatch

    WMS API · Task allocation

    Incoming warehouse jobs are assigned using task priority, robot battery state, travel distance, and congestion.

    OutputPrioritised robot assignments

  2. Coordinate

    Open-RMF · Conflict-Based Search

    Fleet planning reserves space-time corridors. Vendor adapters let different robot models share the traffic plan.

    OutputCoordinated routes & reservations

  3. Navigate

    ROS 2 Nav2 · Local avoidance

    Each robot handles its local navigation while following the fleet plan. Waiting zones help manage busy intersections.

    OutputOn-robot motion & progress

  4. Update

    Zenoh / DDS · Fleet state

    Position, task progress, and battery state return to the coordinator to inform rerouting and charging schedules.

    OutputUpdated fleet state & next tasks

04 / The outcome

Results from this project.

Robots coordinated
50Expanded from an initial fleet of 10
Picking throughput
+35%From about 800 to 1,080 picks per hour
Robot utilisation
85%Compared with 60% before the rollout

Before

Separate vendor systems and static routes created bottlenecks as the fleet grew.

After

Three robot models share task allocation and traffic planning, with coordinated charging.

In context. Results cover a single facility with up to 50 robots. A dedicated network and zone-based coordination were needed as the fleet expanded.

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