Unmanned forklift technology is an innovative solution that integrates 3D real-time positioning, map construction, and autonomous driving technologies. This technology primarily utilizes multi-line LiDAR as the main sensor to capture 3D point cloud data of the surrounding environment, enabling unmanned forklifts to achieve autonomous positioning, map construction, path planning, and obstacle avoidance. Some unmanned forklifts can achieve autonomous navigation and positioning by incorporating auxiliary positioning equipment, such as QR codes and reflectors, into the environment, or by leveraging SLAM technology. These capabilities allow for high-precision navigation and operations in complex indoor and outdoor scenarios.

JHCTECH’s KMDA-3305 can be installed in the electrical compartment of the unmanned forklift body. By collecting data from sensors such as LiDAR, millimeter-wave radar, and stereo cameras, and utilizing data fusion algorithms, it enables autonomous path planning, navigation, obstacle avoidance, and pallet docking. Through CAN bus communication with servo motors and steering wheel control, it supports vehicle movement, steering, braking, and arm lifting. Additionally, it manages various sensors, indicator lights, and touchscreens on the vehicle, enabling human-computer interaction and ensuring safety. With wireless transmission capabilities such as WiFi and 5G, it facilitates communication with dispatch servers, supporting complex tasks like multi-vehicle scheduling, traffic management, and cross-scenario transportation.

The most common applications of machine vision include visual inspection and defect detection, part positioning and measurement, and product identification, classification, and tracking. A typical machine vision application system consists of image capture, a light source system, an image digitization module, a digital image processing module, an intelligent decision-making module, and a mechanical control execution module. Industrial computers are indispensable as the processing centers of machine vision systems. JHC’s BRAV-7721 was selected as the main controller for the entire intelligent industrial machine vision inspection system. It connects to multiple cameras and sensors to collect data on the inspected products. The AI capabilities analyze the various visual data captured, while the built-in inspection software compares the data against standards and outputs results. The system visualizes the data and sends operational instructions to the next stage of mechanical control, automating defect and quality inspection and improving production line efficiency.

As the core component of smart ports, the quality of intelligent warehousing management directly determines the efficiency of port operations. Modern warehousing systems involve complex items, diverse shapes and functions, and intricate processes. These systems must handle storage, movement, sorting, and assembly tasks. To efficiently manage and respond to these processes, technologies such as automatic control, intelligent robotic stacking, smart information management, wireless RFID, mobile computing, human-computer interaction, and data mining are essential. These technologies enable automatic inventory, picking, data queries, location management, dynamic stock control, and inbound/outbound logistics. A suitable, high-performance industrial panel PC serves as the main control system, facilitating the seamless construction of an intelligent warehousing system.

The low-altitude economy is hailed as a key driver of future economic growth, with low-altitude logistics serving as one of its major driving forces, offering immense market potential. Logistics distribution based on drone technology addresses traditional logistics challenges such as high labor costs, low efficiency, and limited coverage. By leveraging three-dimensional distribution networks, it provides faster, more efficient “door-to-door” delivery services, helping to solve the “last-mile” bottleneck in urban logistics.

Typically, surgical robots are composed of a surgical console, a surgical cart with robotic arms, and an imaging system. The need for precise control, stable operation, and real-time response requires the computer hardware responsible for the system’s control to meet strict performance and quality standards. Consequently, embedded computer products with higher performance, better scalability, and greater durability have become the ideal choice for controlling surgical robot systems.

The control system is the core component of the surgical robot. JHCTECH provides the KMDA-7920 series products to empower surgical robots.

The BRAV-7601 is utilized in the telematics-based Automatic Train Control System. It receives data from acquisition modules installed on the train, analyzes the information using pre-installed software, and sends the processed data to the management center.

Based on the above pain points in the development of the smart industry, it is particularly essential to design a dedicated controller that meets industry needs and offers high versatility across various scenarios. For this reason, JHCTECH has developed specialized industrial controllers for smart industries, KMDA-3303/3305, to support the efficient growth of smart industries.

KMDA-3303/3305 is a high-performance, compact embedded computer with an SGCC cabinet structure and an aluminum-extruded heat-dissipating shell. Equipped with Intel® Tiger-Lake U Soc series CPU, it features rich IO functions and shock-absorbing design, making it suitable for AGV/AMR, industrial automation production lines, and smart warehousing applications.

As an NVIDIA NPN partner, JHCTECH works closely with NVIDIA and actively invests in the research and development process of AI edge computing controllers based on the NVIDIA platform.

With the problems of scene adaptation, performance, functional redundancy, etc. reflected in the application process of BRAV-7131, in order to meet the needs of different scene applications, JHCTECH quickly entered the R&D plan of new products.

Based on the NVIDIA Jetson Orin platform, three new BRAV series edge computing MECs developed based on Jetson Orin Nano, Jetson Orin NX and Jetson AGX Orin platforms were launched-BRAV-7120/7121/7134. Successfully completed the global deployment of JHCTECH on the NVIDIA Jetson Orin platform, further broadening the coverage of JHCTECH products.

In the industrial field, energy efficiency is a key factor. Based on utilization and demand, supply and maintenance have become critical aspects. To address this, energy consumption monitoring and analysis by region are required. JHCTECH offers the ECC-U5000 energy controller, equipped with Intel® Elkhart Lake Soc CPU, for this solution. On the one hand, it directly handles data collection and analysis for the energy monitoring system, while on the other hand, it also monitors faults and anomalies during operation, playing a critical role in promptly protecting equipment and ensuring safety.

2024 is also known as the first year of humanoid robot commercialization. Humanoid robots are the result of a deep integration of artificial intelligence and robotics technologies. They can freely enter and exit various environments without being limited by scenarios or regions and are equipped with brains capable of thinking. Humanoid robots integrate advanced technologies such as artificial intelligence, high-end manufacturing, and new materials, and are expected to become a disruptive innovation following computers, smartphones, and new energy vehicles, leading to a leap in productivity.

The BRAV-7131 AI edge computing controller is developed and designed based on NVIDIA Jetson AGX Orin 64GB, with an AI computing power of up to 275 TOPS. Its efficient AI performance meets the requirements of algorithms such as spatial fusion and multimodality, enabling it to complete a large number of high-performance, high-precision target recognition, image processing and analysis tasks in a short time. This greatly enhances the perception and decision-making capabilities of humanoid robot controllers.

It has a rich IO interface to meet the functional requirements of humanoid robots: multiple LAN ports support peripherals such as servo control and machine vision; independent CAN communication satisfies the communication needs for high reliability, such as automatic charging; and multiple USB 3.0 and above interfaces meet the needs of high-speed transmission, effectively completing the control and interaction of humanoid robots.

At present, the scale of the artificial intelligence market is continuously expanding. From the perspective of market development, AI technologies such as machine learning and AI machine vision are being deeply applied across various industries, promoting the implementation of a large number of intelligent scenarios such as smart cities, smart healthcare, and smart manufacturing. Examples include urban smart transportation systems, AI-driven medical inspections, AGV/AMR, industrial robots, etc., helping to improve the digitalization, automation, and intelligence levels of various industries. These applications help achieve better cost reduction and efficiency improvement and accelerate the move toward an intelligent society.

JHCTECH keeps up with the development trend and gradually increases its investment in the research and development of embedded products. To meet the requirements of various AI fields, including low power consumption, high energy efficiency, high scalability, high reliability, and high security, JHCTECH has launched a new embedded motherboard MITX-I984 based on Intel® 12th/13th generation Alder Lake-S/Raptor Lake-S LGA1700 series processors.

Travel is an important part of people’s lives, and with the continuous expansion of subway networks, the subway has gradually become the preferred mode of transportation. The traditional manual ticketing and inspection methods can no longer meet the massive and efficient travel demands of the public. Therefore, the application of an Automatic Fare Collection (AFC) System in urban rail transit is of great significance. Based on the continuous innovation and iteration of the AFC System, it is particularly necessary to design a new AFC dedicated controller that meets the requirements of innovation and environmental requirements. To this end, JHCTECH has developed the AFC System controller SIGM-2660/2661 for the rail transit industry based on the Intel® Elkhart Lake Soc J6412 CPU to help the construction of smart city rail transit.

The SIGM-2660 is a fanless rugged in-vehicle computer powered by an Intel® Elkhart lake SoC processor. Single-channel DDR4 3200MHz, memory up to 32GB. Power input DC 9~36V wide voltage power supply, suitable for rail AFC and rail screen door industry applications.

The shipboard comprehensive monitoring system integrates radar, AIS, CCTV, GPS, and other detection technologies, along with highly integrated real-time situational awareness and cognitive technologies, to effectively monitor the vessel and vessels within the vicinity. It extends the supervision range of shore-based monitoring centers, ensuring navigational safety and providing various technical means for maritime safety management authorities to achieve shore-based controllable data communication and dynamic data acquisition.

In the process of railway freight transportation, timely detection of abnormal issues such as over-limit, over-biased, and overloaded goods, and identifying vehicles with such goods is crucial for ensuring driving safety. It is one of the effective measures to ensure safe driving. Abnormal cargo loading is a significant cause of railway transportation accidents. Therefore, real-time dynamic monitoring of the loading status of freight cars is a vital means to ensure their safe operation.

In this system, PADR-S501 realizes intelligent recognition and voice warning of nine major types of cargo inspection issues, such as cargo loading reinforcement, door and window cover valve closure, tarpaulin covering, container transportation, and sealing. It outputs the inspection results in real-time, automatically generates inspection reports, and performs other operating steps.