When the deployment scale of AGVs (Veículos Guiados Automatizados) is shifting from early “small groups of three or five” to “hundreds of units operating collaboratively,” new challenges arise. When dozens or even hundreds of AGVs move across the same space, avoiding collisions, resolving traffic deadlocks, and maintaining efficiency in highly dynamic environments have become the only criteria to evaluate the quality of a scheduling system.
At Remarkable, we will analyze the logic of AGV collision avoidance systems from three dimensions: hardware perception, software algorithms, and engineering practice.
Environmental Perception: The “Five Senses” and Safety Boundaries of AGV
The first line of defense for collision avoidance is the perception capability of a single AGV. It must accurately and continuously detect both static infrastructure and dynamic obstacles.
1. LiDAR: The Core Obstacle Avoidance Sensor
Currently, Remarkable’s mainstream industrial AGVs adopt single-line or multi-line LiDAR. By emitting high-frequency laser beams and receiving reflected signals, the system builds a 2D or 3D point cloud map of the environment.
- Safety Zoning: Engineers divide detection ranges into “deceleration zones” and “emergency stop zones.”
- Algorithm Support: Using SLAM technology, LiDAR is used not only for navigation but also for comparing with pre-built maps to filter out fixed racks and accurately identify people or unexpected objects.
2. Multi-Sensor Fusion
A single sensor has limitations (e.g., glass or strong light interference). Therefore, we typically adopt:
- Ultrasonic Sensors: Detect transparent objects or suspended obstacles above LiDAR range.
- 3D Cameras: Recognize object shapes, enabling the system to distinguish whether the obstacle is “a person” or “a forklift.”
Scheduling Brain: Decision Logic of the Central Control System (RCS)
If sensors are the eyes, then the RCS (Robot Control System) is the brain. The core of multi-AGV collision avoidance is not “avoidance,” but “planning.”
1. Predefined Traffic Rules (Static Management)
Similar to urban traffic systems, RCS defines mandatory rules in the virtual map:
- One-way Lanes: Enforce single-direction movement in narrow aisles.
- Intersection Locking: A “Token” mechanism is applied. When an AGV requests to pass an intersection, the system grants permission and locks access for others until the vehicle leaves the area.
2. Dynamic Path Planning Algorithms
- Improved A Algorithm: Adds a time dimension to traditional pathfinding. The system calculates the space occupied by each AGV at T1, T2, and T3. If overlap occurs at the same time, it adjusts the waiting time or reroutes one vehicle.
- Deadlock Prevention and Resolution:
Scenario: AGV A waits for B, B waits for C, and C waits for A. Solution: The system predicts resource occupation along routes. Once a circular wait is detected, one AGV is forced to retreat to a buffer zone to break the deadlock.
Multiple AGV collision avoidance relies on an efficient centralized scheduling system (traffic management system). It acts as a control center, ensuring safe and efficient operations through global planning and coordination.
| Strategy Category | Core Idea | Key Technologies | Features & Applications |
|---|---|---|---|
| Global Path Planning | Plan conflict-free routes in advance | MAPF, improved A, genetic algorithms | Suitable for predictable environments like warehouses |
| Real-Time Traffic Control | Resolve conflicts dynamically | Time windows, reservation tables, intersection priority | High flexibility for dynamic environments |
| Local Collision Avoidance | Real-time sensing and emergency response | 2D collision detection, ORCA, space-time corridors | Final safety layer for unexpected events |
| Deadlock Prevention | Avoid or resolve cyclic waiting | Deadlock prediction, rule design, safe zones | Prevents system-wide stagnation |
Three Engineering Pitfalls to Avoid
1. Impact of Load and Inertia
An empty AGV and one carrying 2 tons have completely different braking distances. A robust system must consider weight, speed, and friction in safety calculations.
2. Mixed Traffic Scenarios
In human-machine shared areas, simple stopping reduces efficiency. Advanced systems adopt ORCA algorithms to allow AGVs to adjust paths smoothly instead of stopping.
3. Importance of Simulation Testing
Before deployment, simulation tools such as FlexSim or Gazebo must be used:
- Test congestion under high-density conditions
- Evaluate extreme cases (power failure, sensor failure)
Conclusão
Multi-AGV collision avoidance is a systematic engineering project from hardware sensing to cloud computing. The future trend is the integration of edge computing and centralized scheduling: global optimization by the central system, while AGVs coordinate locally via V2V communication.
For industrial customers seeking maximum stability, selecting a system with mature deadlock prediction mechanisms and reliable communication protocols is essential for 24/7 smart factory operations.
If you have similar requirements, contactar-nos now. Our engineers have extensive experience and can provide professional support.
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FAQS
Q1. How do AGV systems prevent collisions in high-density environments?
A: AGV systems combine LiDAR, multi-sensor fusion, and centralized scheduling (RCS). Through global path planning, time-based conflict prediction, and real-time obstacle detection, they ensure safe operation even with hundreds of AGVs.
Q2. Can AGVs avoid deadlocks automatically?
A: Yes. Advanced systems use deadlock prediction algorithms and resource occupation analysis. When circular waiting is detected, one AGV is automatically redirected to a buffer zone to resolve the issue.
Q3. What sensors are essential for reliable AGV obstacle avoidance?
A: LiDAR is the core sensor, supported by ultrasonic sensors and 3D cameras. This multi-sensor fusion ensures accurate detection of both static and dynamic obstacles, including transparent or irregular objects.
Q4. How does the system handle human-AGV interaction?
A: In mixed environments, systems use ORCA algorithms to enable smooth path adjustments instead of stopping. This improves both safety and operational efficiency.
Q5. What factors affect AGV braking and safety distance?
A: Key factors include load weight, speed, and floor friction. A reliable system dynamically adjusts safety zones and braking models based on real-time operating conditions.
Q6. Is simulation necessary before AGV deployment?
A: Yes. Simulation tools like FlexSim or Gazebo are critical for testing congestion, peak loads, and extreme scenarios such as power or sensor failures before real-world implementation.
