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Accelerate your smart logistics upgrade.
From automated warehousing and AGV navigation to parcel DWS systems, our sensing technology permeates every stage of smart logistics.
Logistics
Outdoor Low-Speed Unmanned Vehicle: Forward Blind Spot Suppression and 3D Environment Perception
Low-speed autonomous vehicles, such as unmanned delivery vehicles and unmanned sweepers, typically operate on outdoor, unstructured roads. Although the main lidar mounted on the roof has a long detection range, it creates a significant near-field blind spot beneath the vehicle’s front end. As a result, the main lidar often struggles to detect low-lying obstacles—such as curbs, pets, or traffic cones—as well as road surface potholes (negative obstacles). Moreover, intense sunlight outdoors places extremely high demands on the sensors’ anti-interference capabilities; ordinary depth cameras tend to fail under strong lighting conditions.
Autonomous Forklift: Lateral Clearance Monitoring and Collision Avoidance in Narrow Aisles
In automated stereoscopic warehouses pursuing high-density storage, rack aisles are typically designed to be extremely narrow (VNA). When unmanned forklifts travel at high speeds within these aisles, even a slight deviation or an unexpected protrusion of goods from the racks into the aisle can easily lead to accidents such as goods scraping against racks or the forklift becoming stuck. Traditional single-point ranging sensors can only detect fixed points and cannot cover the entire three-dimensional space along the sides, making it difficult to identify irregularly protruding obstacles.
In automated warehouses with dense storage, AGVs and AMRs need to swiftly navigate through narrow aisleways between shelves. Traditional 2D LiDAR sensors can only scan a single plane at a fixed height above the ground—typically around 20 cm—leaving significant vertical blind spots. As a result, low obstacles on the ground—such as dropped delivery boxes or abandoned pallet blocks—or suspended objects just slightly above the ground—such as pallet corners extending from the bottom of shelves—often go completely unnoticed by 2D LiDAR. This makes robots highly susceptible to undercarriage scrapes, cargo collisions, or even impacts against shelf uprights, potentially leading to serious safety incidents.
Autonomous Forklift: 3D Obstacle Avoidance and Spatial Safety Protection
When operating among densely packed storage racks, unmanned forklifts face complex three-dimensional spatial challenges. Traditional 2D obstacle-detection radars can only scan a plane approximately 20 cm above the ground, leaving a significant vertical blind zone. As a result, these radars often “fail to see” overhead rack beams, partially protruding goods, or low-level pallets on the floor. This makes forklifts highly susceptible to accidents during operation—such as collisions between the mast and overhead objects or damage to goods from rubbing against surrounding facilities. Such incidents not only cause costly damage to logistics equipment but also pose a serious threat to the safety of personnel on site.
Autonomous Forklift: Precision Forking Assistance and Pallet Hole Position Recognition
In the entire operational process of unmanned forklifts, “precise fork positioning” is the stage with the highest failure rate. Due to uneven ground surfaces, deviations in pallet orientation, or deformation of goods, relying solely on vehicle navigation and positioning often fails to ensure perfect alignment between the forklift forks and the pallet’s hole positions. Once the deviation exceeds a certain threshold, the forks may collide with the pallet’s upright posts or even topple the goods, leading to serious logistics accidents. Traditional mechanical limit switches or 2D radar systems struggle to capture depth information about the pallet’s cross-section, making it impossible for them to handle pallets with varying orientations.
Autonomous Forklift: Cargo Posture Monitoring and 3D Pathway Protection
When unmanned forklifts are transporting goods at high speeds, they face a dual risk: First, there’s the safety of the cargo itself—emergency stops or sharp turns could cause stacked goods to tilt, slide, or even collapse. Second, there’s the issue of blind spots: Stacked goods often obstruct the line of sight of sensors located beneath the vehicle body, causing the forklift to “fail to see” low-lying obstacles or suspended objects directly in front of it. If the forklift cannot实时 detect the status of the cargo and identify blind-spot risks in real time, it will easily lead to severe damage to the goods or collision accidents.
Collaborative Operations in 3D Warehousing: 3D Safe Interconnection Between AGVs and Overhead Cranes
In modern smart factories, the operational areas of ground logistics (AGVs/AMRs) and aerial logistics (cranes and gantry robots) often overlap significantly. When a crane is lowering a suspended load, if an AGV suddenly enters the area below, a severe “Mount Tai crushing” collision accident can easily occur. Meanwhile, conventional AGVs are equipped only with 2D radar, which can only scan the ground surface and completely “fail to see” objects that are hovering mid-air or lifting devices that are in the process of descending—posing a critical vertical blind-spot hazard.
Autonomous Navigation and Obstacle Avoidance for Intelligent Warehouse Mobile Robots (AGVAMR)
In modern e-commerce warehouses or flexible manufacturing workshops, logistics paths are complex and ever-changing. Traditional navigation methods using magnetic strips or QR codes are cumbersome to implement and offer rigid routes, making them no longer suitable for the demands of “flexible production.” The next-generation mobile robots need to be equipped with SLAM-based natural navigation capabilities—enabling them to autonomously localize themselves and plan paths in environments without any auxiliary markers. Meanwhile, given the mixed human-robot flow within warehouses and the dynamic nature of goods placement, robots must possess highly sensitive, autonomous obstacle-avoidance capabilities to prevent collisions with shelves or personnel and ensure continuous, efficient operations.
Flexible Obstacle Avoidance and Area Safety Protection for Mobile Robots (AGV/AMR)
At e-commerce sorting or material-handling sites, a large number of recessed AGVs need to swiftly navigate through narrow aisleways between shelves, and human-machine mixed-mode operations have become the norm. Traditional mechanical bumpers can only trigger after a collision has already occurred—acting as a “post-event remedy”—and thus cannot prevent collision-related damage. Meanwhile, conventional infrared sensors have a short detection range and are heavily affected by light interference, making it difficult for them to detect personnel wearing dark-colored clothing or black obstacles. Moreover, when AGVs turn in narrow aisles, their fixed-shaped detection zones tend to easily scan the shelf uprights on either side, leading to frequent false alarms and unnecessary stops, which severely impacts logistics efficiency.
Autonomous Forklift: Autonomous Positioning, Navigation, and Operational Safety Protection
In automated warehousing and logistics, unmanned forklifts often need to operate in narrow aisleways between shelves and in busy transfer zones. Traditional laser reflector-based navigation requires installing numerous reflectors on walls and shelves, a process that is both labor-intensive and costly to maintain. Moreover, if the positions of shelves are changed, the navigation system must be reconfigured from scratch. Additionally, forklifts are heavy and have significant inertia; if they fail to promptly detect suddenly appearing personnel or scattered goods while moving, they can easily cause serious safety incidents or even lead to shelf collapses.
RGV Shuttle: Collision Prevention for High-Speed Operation of Rail Logistics Lines
In automated production lines or automated storage systems, RGVs (rail-guided shuttle vehicles) are responsible for high-frequency, straight-line material handling tasks. Since RGVs typically carry heavy loads, operate at high speeds, and have significant braking inertia, if a person accidentally enters the vehicle’s operating path or foreign objects—such as pallet fragments or dropped cargo—fall onto the track, the vehicle will be unable to swerve and avoid the obstacle, making severe collision accidents highly likely. Traditional contact-type anti-collision barriers can only trigger at the instant of impact and cannot provide sufficient buffer distance to counteract the vehicle’s tremendous kinetic energy, thus posing an extremely high risk of equipment damage and production line downtime.
All-round low-position collision prevention and safe obstacle avoidance for unmanned forklifts
Unmanned forklifts navigate busy warehouse or factory environments, where they encounter extremely complex ground conditions. Low obstacles—such as scattered pallets and goods—as well as suddenly appearing human feet and shelf supports in narrow passageways, all pose significant safety risks to operations. Traditional single-radar solutions often suffer from “blind spots” on either side of the vehicle’s front end; particularly when the vehicle is turning, this can easily lead to lateral scrapes or collisions, resulting in machine downtime, repairs, and even injuries to personnel.
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