In modern smart manufacturing and flexible production systems, AGVs (Automated Guided Vehicles) have become a standard component of intralogistics operations. In many factories, they are no longer an optional upgrade but a core infrastructure for material transportation.
However, after deployment, many companies face a practical issue: even though AGVs are introduced, overall efficiency does not always improve as expected. During peak hours, congestion, waiting, and even temporary deadlocks can still occur, affecting production rhythm.
The root cause is the transition from single-vehicle operation to multi-vehicle coordination. The challenge is no longer "how one vehicle takes the shortest path," but how multiple vehicles operate efficiently in a shared space without interfering with each other.
1. How problems emerge in multi-AGV systems
In early-stage applications, AGVs are mainly used for simple point-to-point transportation, such as warehouse-to-line delivery. Routes are fixed and system complexity is low.
As production speed increases and SKU variety grows, multiple AGVs begin operating in overlapping areas, and typical issues gradually appear:
Intersection congestion caused by multiple vehicles competing for the same node
Head-on deadlocks in narrow lanes
Imbalanced route utilization, where some paths are overloaded while others are underused
These issues are often seen as scheduling problems, but in essence they come from insufficient global path coordination and incomplete traffic rules.
2. Path optimization: a layered approach is more practical
In real industrial projects, starting with overly complex algorithms often leads to poor practicality. A more effective approach is to implement path optimization in layers, from static planning to dynamic adjustment.
The first layer is static map modeling and route planning. The factory is abstracted into a graph structure, where workstations, buffers, and charging stations become nodes, and lanes become edges. For high-frequency tasks such as material feeding or pallet return, multiple predefined routes with priorities can be prepared. Algorithms like A* or Dijkstra are sufficient at this stage. The key is not algorithm complexity, but whether the main routes are well designed.
The second layer introduces dynamic path adjustment. Since factory environments are constantly changing, time must be considered in path planning. When the system detects that a segment is likely to be occupied soon, local rerouting can be triggered to avoid congestion.
For bottleneck areas that cannot be bypassed, a path reservation mechanism is required, often referred to as time windows or path locking. In simple terms, only one AGV is allowed to pass through a critical section within a specific time slot, while others must wait.
In practical implementations, system performance is not only determined by algorithms but also by execution quality at the hardware level. For example, integrated AGV drive systems such as those provided by Plutools (布路托) focus on execution-layer performance, including smooth acceleration and deceleration, precise low-speed control, and stable load handling. These factors significantly influence overall coordination in high-density multi-vehicle environments.
Conflict handling is more effective when categorized rather than unified:
Node conflicts are resolved through priority rules
Head-on conflicts are handled via zone occupation logic
Following conflicts are managed through spacing control and dynamic speed adjustment
3. Traffic management is often more important than algorithms
In many projects, significant effort is spent on path algorithms, yet congestion still occurs after deployment. The main reason is often incomplete traffic rule design.
From practical experience, dividing the factory into functional zones is essential. Separating raw material areas, processing zones, and finished goods areas helps regulate vehicle flow and prevents local congestion. In high-density layouts, a unidirectional loop layout is often more stable, as it naturally reduces head-on conflicts.
At key intersections or narrow passages, clear traffic rules are necessary, such as:
Only one direction allowed at a time
Main corridors have priority over secondary lanes
Emergency tasks can override normal rules
Buffer and waiting areas are also critical. Without sufficient buffering space, localized congestion can quickly propagate. Properly placed holding zones allow early redistribution of vehicles.
From an engineering perspective, execution hardware also plays an important role. Variations in drive wheel performance or motor controller responsiveness can become amplified in multi-vehicle systems, affecting overall smoothness.
4. Practical considerations often overlooked
1. Shortest path is not always optimal
The shortest route is often also the most frequently used, leading to congestion. Allowing 10–20% detours can improve overall throughput.
2. Lack of simulation leads to costly rework
Before deployment, it is recommended to validate scenarios using tools such as
AnyLogic or Siemens Plant Simulation
Key metrics include deadlock risk, waiting time, and system recovery capability.
3. Visualization should be built step by step
Early-stage systems only need basic visibility such as vehicle location and task status. Advanced analytics like heatmaps and utilization analysis can be added later.
4. Human-machine interaction must be considered early
Fully unmanned operation is unrealistic in most factories. Systems should support automatic speed reduction, warning signals, and dedicated pedestrian lanes.
5. Centralized scheduling is recommended
Path planning and dispatching are better handled by a centralized system, while AGVs focus on execution and local obstacle avoidance. A "centralized scheduling + distributed execution" architecture is more stable for multi-vehicle systems.
Conclusion
The performance of an AGV system is not determined by a single algorithm, but by the clarity of operational rules and the reliability of execution.
Path optimization defines how vehicles move, while traffic management determines whether they can keep moving smoothly under real-world constraints. When system design, traffic rules, and hardware execution work together, multi-AGV systems can achieve stable and efficient material handling performance.
