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Deeply rooted in vertical industries, we understand technology—and even more, we understand you.
Every industry has its own unique process flows and pain points. By combining cutting-edge sensing technologies with deep industry expertise, we create tailor-made, proprietary solutions for ten core industries—including road transportation, smart logistics, and high-end manufacturing.
By industry
Collision Prevention and Stockpile Scanning for Large Port Stacking Machines
At bulk cargo terminals or coal storage yards at power plants, bucket-wheel stacker-reclaimers are massive machines with an extremely large working radius. During fully automated or remote operations, the boom arm is highly susceptible to collisions with nearby coal piles, other machinery, or vehicles, which can result in severe equipment damage. Moreover, harbor sea breezes—laden with salt spray and containing high levels of dust at the operation site—can cause ordinary sensor probes to fail rapidly, effectively “blinding” the machine.
Forward Active Collision Avoidance and Foreign Object Detection for Rail Transit Vehicles
The operating environment of rail transit systems—such as mine locomotives, subway engineering vehicles, or trams—is complex and characterized by long braking distances. In the dimly lit conditions of tunnels or under harsh outdoor weather, drivers find it difficult to spot with the naked eye rocks falling onto the tracks, intruders, or stalled vehicles in a timely manner. Traditional video surveillance is heavily affected by lighting conditions, while millimeter-wave radar struggles to precisely outline the contours of obstacles, making serious collision and derailment accidents highly likely and causing significant loss of life and property.
In-Ground Vehicle (IGV): All-Weather, Outdoor 3D Obstacle Avoidance and Environmental Perception
Port terminals are typical all-weather, unstructured outdoor environments. When operating, unmanned ground vehicles (IGVs/ARTs) face complex lighting challenges: intense glare at noon, low illumination at night, and reflections from waterlogged surfaces—all of which can severely interfere with the visual cameras’ ability to make accurate judgments. Moreover, terminal surfaces often littered with low, small objects such as container locks, maintenance tools, or traffic cones—traditional single-line LiDAR systems are highly prone to “missing” these objects when scanning from above, potentially leading to vehicle tires being punctured or damaged by running over such objects, thereby impacting port operational efficiency.
Autonomous Sweeper: Edge-Adaptive Cleaning Guidance and Ground Obstacle Detection
Unmanned cleaning vehicles primarily operate in outdoor settings such as parks, plazas, or sidewalks. To ensure comprehensive cleaning coverage, these vehicles typically perform “edge-cleaning”—that is, they drive closely alongside curbs. This task places extremely high demands on perception accuracy: the vehicle must not only identify the exact position of the curb to maintain its course but also prevent its wheels from scraping against the curb. Moreover, outdoor environments feature dramatic changes in lighting conditions—such as shade from trees and intense sunlight—and often include low stone pillars, steps (negative obstacles), or non-rigid debris like piles of fallen leaves. As a result, conventional sensors struggle to reliably distinguish between “debris that can be safely traversed” and “obstacles that must be avoided.”
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.
Service Hotline:
400-050-0550
Product Inquiry:
19910537875
19910627875
Corporate email:
kf@yintaili.cn
Beijing Headquarters Address:
Building 3, Zone 3, Yongfeng Technology Enterprise Accelerator, Haidian District, Beijing Municipality, 2nd Floor
North China Operations Center:
Building 5B, Jindi Weixin·Yongtai Intelligent Manufacturing Park, Anfu Road, Xiqing District, Tianjin City
Henan Branch:
Unit 902, Building 1, Xinyuan International Plaza, Jianshe Road Subdistrict, Zhongyuan District, Zhengzhou City, Henan Province
Jiangsu Branch:
Room 605B, Building A, Anke Building, Jianye District, Nanjing City, Jiangsu Province
