Autonomous Collaboration of AMR Swarms

No longer just the intelligence of individual robots, but the swarm intelligence of the entire system.

AMR Core Highlights

Distributed swarm intelligence and collaborative decision-making

Context-Aware Obstacle Avoidance

Not just avoiding 'obstacles', but understanding the nature of objects

Distinguish permanent obstacles (walls, pillars) vs temporary obstacles (cargo pallets)
Identify dangerous areas (elevator entrances, charging stations) vs work areas
Predict dynamic object trajectories (person movement, forklift travel paths)

Adaptive Environment Modeling

Real-time environment model updates, adapting to dynamic changes

Initial Mapping: Complete 5000㎡ high-precision map within 2 hours
Online Updates: Automatically identify and update map when environment changes (shelf movement, temporary stacking)
Multi-floor Memory: Unified modeling of cross-floor environments, support automatic elevator calling

Edge Computing Optimization

Multi-sensor fusion, real-time environmental perception, edge computing enables swarm intelligence

90% perception computing completed on edge, latency <50ms
Only transmit semantic information to cloud, bandwidth requirement reduced by 80%
Support offline operation, can continue working for 4 hours even during network interruption

Autonomous Mobility vs Fixed Path

No ground modification required, autonomous navigation, 60% deployment cost reduction. Compared to fixed paths, AMR can flexibly adapt to environmental changes without re-laying guide lines.

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Adaptive Navigation vs Preset Path

Dynamic path planning, real-time obstacle avoidance, adapting to complex environments, 30% efficiency improvement. Based on real-time perception, AMR can autonomously plan optimal paths.

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