JT-Navi Autonomous Navigation
It integrates LiDAR/inertial navigation/GNSS/vision/odometer multi-sensor data, realizes sensor complementary advantages through a dynamic confidence judgment model, and maintains high-precision positioning in extreme scenarios such as laser degradation, satellite signal denial, and indoor and outdoor switching.
Real-time mapping enables one-time deployment and efficient mapping of large scenes. The system supports switching between multiple maps, breaking through the traditional scene boundary restrictions and realizing full-area mapping.
Dynamic obstacle avoidance strategies enable real-time detection of dynamic obstacles such as pedestrians and vehicles, enabling flexible avoidance.
Close-range circular ultrasonic collision avoidance
Multi-lidar 360° collision avoidance with no blind spots
Multi-level risk response, including collision-sensing emergency stop, software and hardware emergency stop, and operational control. The VCU provides millisecond-level response for instant safety protection.
Based on IOT technology, relying on cloud platform and APP multi-terminal control, it provides scheduled operation, intelligent operation scheduling, and operation data statistics.
Based on multi-source data fusion and embedded intelligent diagnosis algorithm, it can realize status monitoring, accurate alarm, fault diagnosis and predictive maintenance in complex equipment operating environments.
The remote takeover system based on 5G communication, including remote driving and remote escape, is used to remotely take over the sweeper in specific situations, ensuring the safety and stability of vehicle operation in all aspects.
Centimeter-level path control maintains centimeter-level precise spacing along the edges of walls, guardrails, etc. to ensure corner cleaning. Adaptive contour tracking automatically identifies complex boundaries (such as flower beds and steps).
Intelligent bow-shaped planning: Automatically generates the optimal coverage path, supports customized partition planning, can match cleaning strategies for different ground materials, automatically recommends the optimal cleaning path based on environmental semantics, and shortens deployment time.
Patrol-style planning: Based on deep learning technology, it supports automatic identification of dozens of types of garbage, realizes patrol-style cleaning, and adapts to cleaning needs in different scenarios.
Based on the unique structural design and vision guidance + force dual feedback control system, it revolutionizes the automatic recognition, precise grasping and intelligent replacement of 120L standard trash cans.
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