Analysis of the differences in control and navigation between AGV and AMR: a technological revolution in intelligent logistics

2025-05-16

I. Navigation Technology: From "Fixed Path" to "Dynamic Perception"
Navigation is the core watershed of AGV and AMR, which directly determines the autonomy and flexibility of the vehicle.

1. AGV: "Track-based" navigation that relies on pre-set paths
AGVs need to realize path guidance through physical or virtual markings (such as magnetic strips, QR codes, laser reflectors, etc.), and their navigation logic is highly dependent on external infrastructure. For example:

Magnetic strip navigation:The magnetic field is generated by the magnetic strip attached to the floor, and the AGV's magnetic sensor tracks the magnetic field strength to mAintain the route.
Optical navigation: Uses a camera to identify the contrasting color strip or reflective tape on the ground, and locates it through the reflected light signal.
Fixed marker navigation: QR codes or laser markers are set at specific locations, and the AGV locates itself by scanning these points.

Although the stability of this technology is high, the path adjustment requires the re-paving of infrastructure, which is time-consuming and costly, and difficult to adapt to dynamic environments.

2.AMR: "Autonomous navigation" based on environmental perception
AMR uses **SLAM (Simultaneous Localization and Mapping)** technology, which combines lidar, visual sensors, ultrasonic, and other multimodal perception systems to build an environment map in real time and dynamically plan the path. For example:

LiDAR (Light Detection and Ranging): Generates high-precision 3D maps through laser scanning, supporting obstacle avoidance and path optimization.
Visual Simultaneous Localization and Mapping (VSLAM) : Uses cameras to capture environmental features and AI algorithms to identify dynamic obstacles (such as people and cargo).
Natural navigation: No need for physical markers, compare the pre-loaded map with the environment in real time, and Autonomously adjust the route.

This technology enables AMR to navigate obstacles in complex environments and even achieve multi-robot coordinated scheduling, significantly improving operational efficiency.

II. Control System: From "Central Dispatching" to "Edge Intelligence"
The differences in the design of the control system reflect the inherent differences in autonomous decision-making capabilities between AGVs and AMRs.

1. AGV: Centralized control and programmed instructions
The actions of AGV are completely dependent on the instructions of the central control system (such as PLC or upper computer), and its task execution path is fixed, lacking environmental perception and autonomous decision-making ability. For example:

- When the path is blocked by obstacles, the AGV can only stop running and wait for manual intervention.
- In the case of multi-machine collaboration, traffic control relies on a central dispatching system, which can easily lead to a decrease in efficiency due to path conflicts.

2. AMR: Distributed control and autonomous decision-making
AMR is equipped with edge computing modules and AI algorithms, which can independently process environmental data and adjust action strategies in real time. For example:

- Dynamic obstacle avoidance: detect obstacles in real time through sensors and choose a bypass route independently.
- Task optimization: based on machine learning analysis of historical data, optimize path planning and task priority.
- Multi-machine coordination: Communication between vehicles is achieved through the cloud or local network to avoid deadlocks and improve overall throughput.

This distributed control architecture makes AMR more adaptable to high-dynamic, variable operation scenarios, such as e-commerce warehouse sorting or hospital material distribution.

III. Environmental adaptability: from "structured" to "flexible"

1. AGV: Suitable for standardized scenarios
AGVs excel in scenarios with fixed paths and high repeatability, such as assembly lines in car manufacturing plants or pallet transport in tobacco logistics. Their advantages include:
- High stability: pre-set path reduces positioning error, suitable for high-precision transportation.
- Low cost: The technology is mature, and the initial investment is lower than AMR.

2. AMR: Adapting to dynamic and complex environments
AMR can quickly respond to changes in the environment with its autonomous navigation capability, such as:
- Layout adjustment: The map can be adapted to new paths through software updates, without physical modifications.
- Human-machine collaboration: safely avoid people in a hybrid work environment to reduce accident risks.

Typical applications include pharmaceutical distribution in medical institutions and dynamic picking in retail warehouses.

IV. Future Trends: Technology Integration and Scenario Complementarity

With the progress of AI and sensor technology, the boundaries between AGV and AMR are gradually blurred:
- Intelligent upgrade of AGV: Some AGVs start to integrate SLAM technology to realize "unguided" navigation, such as the mixed navigation model launched by Dolphin Star.
- Cost optimization of AMR: The popularity of low-cost sensors such as solid-state LiDAR has driven the penetration of AMR into small and medium-sized enterprises.
- Collaborative ecological construction: AGV and AMR coexist in the factory, AGV is responsible for main transportation, AMR handles flexible tasks at the end, forming an efficient logistics network.

Conclusion: The choice depends on the scene's requirements
The core difference between AGV and AMR lies in the degree of **navigation autonomy** and **control intelligence**. For enterprises with fixed paths and limited budgets, AGV is still cost-effective.

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