AI Unmanned Overhead Crane Dispatching: Multi-Crane Coordination

An in-depth look at the core technologies behind AI-powered unmanned crane dispatching systems, covering multi-crane collaborative architecture, intelligent task scheduling algorithms, dynamic path planning with collision avoidance, and MES/WMS integration—helping heavy industry achieve fully automated workshop logistics.

This article breaks down the technical architecture of AI-driven unmanned crane dispatching systems—including the coordination layer for multi-crane collaboration, priority- and shortest-path-based intelligent scheduling algorithms, dynamic path planning with conflict avoidance, and MES/WMS integration—offering a practical reference for automating workshop logistics.

From Single-Crane Automation to Multi-Crane Intelligent Dispatching

Crane dispatching is a system technology that uses AI algorithms to assign tasks, plan routes, and manage conflicts across multiple overhead cranes. While automating a single crane answers the question of how to lift, the dispatching system determines which crane handles the job, what gets lifted first, and which path avoids collisions.

In traditional workshops, multiple overhead cranes rely on human dispatchers to coordinate—a process that is both inefficient and error-prone. Dispatchers must simultaneously monitor the MES task queue, crane positions, conflict zones, and priorities, leading to severe information overload. Data from Kelude Heavy Industry's real-world projects shows that after deploying the AI dispatching system, average task response time dropped from 8 minutes to 45 seconds, crane utilization rose from 55% to 82%, and multi-crane collision incidents fell to zero. Kelude Heavy Industry's AI application solutions now cover multi-crane dispatching scenarios across a range of industries.

Three-Tier Dispatching Architecture for Seamless Integration

The AI dispatching system employs a three-tier architecture—enterprise layer, coordination layer, and control layer—that integrates seamlessly with the four-tier structure of the overhead crane control system:

The coordination layer serves as the brain of the entire dispatching system. It receives task queues from MES (e.g., moving steel coil No. 3 from storage location A5 to station B12), evaluates each overhead crane's current position, task status, travel speed, and collision avoidance zones, and makes optimal assignment decisions within 100ms, which are then sent to each crane's PLC via OPC UA.

AI Task Scheduling Algorithm for Cranes

The scheduling engine of the dispatching system uses a priority-based dynamic weighting algorithm that evaluates four key dimensions:

Scheduling Workflow: When a new task enters the queue, the system calculates a composite score (weighted total) for each idle overhead crane, assigns the task to the highest-scoring crane, and locks that assignment until completion, after which the crane is released and re-scored. In multi-crane, multi-task scenarios, the dispatching engine uses a greedy + local search strategy: it first assigns tasks greedily by score, then checks whether swapping tasks between two cranes reduces total completion time, approaching optimality in O(N×M) time. In real-world tests with 100 tasks and 6 cranes, this approach reduced total completion time by an additional 12% compared to a purely greedy algorithm.

Dynamic Path Planning and Collision Avoidance

When multiple overhead cranes operate within the same workshop bay, path intersections and travel conflicts are core challenges that must be resolved. The path planning module of the dispatching system employs a two-layer strategy: trajectory pre-occupation and real-time monitoring and prevention:

Trajectory Pre-occupation: When a task is dispatched, the system discretizes the crane's expected travel path into a series of position-time nodes (one checkpoint per meter, with timestamps calculated based on travel speed) and writes them to a shared conflict memory. Other cranes automatically avoid these pre-occupied space-time points when planning their own paths.

Real-time Monitoring and Prevention: If a crane deviates from its planned path due to a fault or manual intervention, the system issues a deceleration or pause command within 100ms using a safety distance algorithm (triggered when the head-to-head spacing between two cranes is <5m). The reserved safety distance is dynamically adjusted based on crane speed — 3m at 0.5m/s, 5m at 1.0m/s, and 8m at 1.5m/s.

Restricted Zone Management: Maintenance stations, material staging areas, and personnel walkways are pre-configured as virtual fences, and the crane path planning automatically routes around them. In emergency situations, the safety PLC can directly apply the brakes.

MES/WMS Integration for Automated Logistics Loop

The AI dispatching system connects to MES via REST API to retrieve production tasks and interacts with WMS via OPC UA for storage location information, creating a fully automated logistics loop:

Integration Workflow: MES issues a task (including material ID, source storage location, and target workstation) → the dispatching system queries WMS to confirm the storage location → the crane executes automatically → the system updates WMS with in-transit status → upon arrival, WMS updates the storage location and MES logs the event. Kelude has integrated with mainstream systems including SAP ME, Digiwin, and Yonyou, with an integration cycle of 2–4 weeks. All interfaces include idempotent design and exception compensation; data is cached during network interruptions and automatically re-transmitted upon recovery.

Deployment and Measured Benefits

Deployment Steps: ① Survey to determine the number of cranes, work areas, and conflict hotspots ② Deploy the dispatching server and OPC UA communication ③ Tune scheduling algorithm parameters ④ Verify single-crane automatic operation ⑤ Run multi-crane coordinated simulations ⑥ Integrate with MES for joint commissioning.

Measured Benefits: In an unmanned dispatching project at a steel plant with 6 cranes, crane idle time dropped from 38% to 14%, with zero operator errors. The system supports hot-plugging of additional cranes and one-click switching between manual and automatic modes.

Kelude Heavy Industry Dispatching Solutions

Kelude Heavy Industry offers complete solutions ranging from a single unmanned overhead crane to plant-wide multi-crane dispatching. The dispatching system supports coordinated management of 6 to 30 cranes, is compatible with major PLC platforms including Siemens, Mitsubishi, and Beckhoff, and can integrate with any ERP/MES/WMS system.

Free workshop logistics diagnostics and intelligent dispatching solution design are available upon request.

Frequently Asked Questions (FAQ)

How do multiple cranes operating simultaneously avoid collisions?

A dual-layer strategy of trajectory pre-occupation and real-time monitoring is used: when a task is issued, the expected path is discretized into position-time nodes written to a shared conflict memory, which other cranes automatically avoid. Additionally, a safety distance algorithm issues deceleration/pause commands within 100ms, with the reserved distance dynamically adjusted based on speed.

Can the AI dispatching system integrate with existing MES?

Yes. It connects to MES via REST API to retrieve production tasks and interacts with WMS via OPC UA for storage location information. Kelude has integrated with mainstream systems including SAP ME, Digiwin, and Yonyou, with an integration cycle of 2–4 weeks. All interfaces include idempotent design and exception compensation mechanisms.

What benefits can deploying an AI crane dispatching system deliver?

In real-world deployments, task response time was reduced from 8 minutes to 45 seconds, crane utilization increased from 55% to 82%, and multi-crane collision incidents dropped to zero. The system supports coordinated management of 6–30 cranes and is compatible with major PLC platforms including Siemens, Mitsubishi, and Beckhoff.

Relevant Standards: GB/T 28264 Safety Monitoring and Management System for Lifting Appliances, ISO 13849-1 Safety-related parts of control systems, IEC 61508 Functional safety of electrical/electronic/programmable electronic safety-related systems

Keywords: AI crane dispatching, unmanned overhead crane, multi-crane coordination, intelligent scheduling, crane dispatching system, automated crane dispatching, Kelude Heavy Industry