Automated use of technology allows humans to complete more tasks. In the field of logistics, the potential of automation is huge and the benefits it brings are obvious, especially when there are huge changes in operation methods or increasing demands. Expansion of the scale of operations usually requires the addition of additional employees, and these employees are usually unable to start their jobs immediately, especially when other industries have similar needs. How to respond quickly in the event of market fluctuations requires rapid action and other additional capabilities throughout the operation.
Logistics automation can quickly increase capacity according to changes in demand. After elevating logistics automation to a strategic position, it can not only increase productivity, but also reduce human errors, thereby increasing work efficiency. With appropriate logistics automation software, hardware, and platform resources, even in periods of low demand, the impact on operating costs is relatively small, far lower than the cost required to maintain a large amount of manpower. As demand increases, operational capabilities are ready and can be started quickly. Although these methods can bring the required flexibility to logistics companies and can quickly respond to changes in demand, there are still opportunities to do more.
Artificial intelligence will amplify the influence of logistics automation
The introduction of artificial intelligence into logistics automation will greatly enhance the influence of artificial intelligence. Artificial intelligence can reduce errors in common semi-skilled tasks such as sorting and sorting products. The use of autonomous mobile robot AMR can improve the efficiency of package delivery, including the most expensive last mile delivery. Artificial intelligence helps autonomous mobile robot AMR to plan routes and identify features, such as people, obstacles, delivery portals, and doorways.
Integrating logistics automation into any environment brings certain challenges. It can be as simple as a power conveyor belt instead of repetitive processes, or as complicated as introducing autonomous robots with collaborative capabilities into the workplace. When artificial intelligence is added to the process of automation and integration, the challenges will become more complex, but the benefits will also increase.
With the interconnection between solutions and the deeper understanding of other stages in the process, the efficiency of each automation element will also increase. Putting artificial intelligence near the devices that generate data and take actions is called edge artificial intelligence. The adoption of edge artificial intelligence is redefining logistics automation.
The development of edge artificial intelligence is extremely rapid, and its use is not limited to logistics automation. The benefits of placing artificial intelligence at the edge of the network must be balanced with the availability of resources, such as electricity, environmental operating conditions, logistics locations, and available space.
Implement reasoning at the edge
Edge computing brings computing and data closer together. In traditional IoT applications, most of the data is sent to the (cloud) server through the network, where the data is processed, and the result is returned to the edge of the network (such as the physical device at the edge). Only cloud computing introduces latency considerations, and this approach is unacceptable for time-sensitive systems. Here is an example of the role of edge computing. In the sorting process, capturing and processing the image data of the package locally can allow the logistics automation system to respond in as little as 0.2 seconds. The network delay in this part of the system will make the sorting process slower, but edge computing can eliminate this potential bottleneck.
Although edge computing brings computing closer to data, the introduction of artificial intelligence to the edge can make the process more flexible and less error-prone. Similarly, the logistics of the last mile relies heavily on labor, but AMR, an autonomous mobile robot using edge artificial intelligence, can improve this situation.
The introduction of artificial intelligence will have a significant impact on the hardware and software used in logistics automation, and there are more and more potential solutions. Generally, solutions for training artificial intelligence models are not suitable for deploying models on the edge of the network. The processing resources used for training are designed for the server, and its demand for resources such as energy consumption and memory is almost unlimited. At the edge, energy consumption and memory are limited.
In terms of hardware, large multi-core processors are not suitable for edge artificial intelligence applications. On the contrary, developers are specializing in heterogeneous hardware solutions optimized for edge artificial intelligence deployment. This kind of scheme includes CPU and GPU, of course, can also be expanded to ASIC, MCU and FPGA. Certain architectures (such as GPU) are good at parallel processing, while other architectures (such as CPU) are better at sequential processing. Today, there is no single architecture that can truly provide the best solution for artificial intelligence applications. The overall trend is to configure the entire system with hardware that can provide the best solution, rather than using multiple instances of the same architecture.
This trend points to heterogeneity, where there are many hardware processing solutions with different architectures that work together through configuration instead of using multiple devices (all devices based on the same processor) of the same architecture. The ability to introduce the right solution for any given task, or integrate multiple tasks on a specific device, can provide greater scalability and optimized performance per watt and/or per dollar.
Moving from a homogeneous system to a heterogeneous processing requires a huge solution ecosystem and the mature ability to configure these solutions at the hardware and software levels. This is why it is necessary to cooperate with a supplier that has the ability to establish partnerships with all chip suppliers, because this supplier can provide solutions for edge computing and work with customers to develop systems with scalability and flexibility. .
In addition, these solutions use general open source technologies such as Linux, and professional technologies such as the robot operating system ROS2. In fact, more and more open source resources are being developed to support logistics and edge artificial intelligence. From this perspective, there is no single “correct” software solution, and the same is true for the hardware platform that runs the software.
Use modular methods to build edge computing
In order to increase flexibility and reduce vendor binding, ADLINK has developed a modular approach at the hardware level, which allows the hardware configuration in any solution to become more flexible. In fact, hardware-level modularity allows engineers to change any part of the system hardware, such as the processor, without causing system-wide interruptions.
When deploying new technologies such as edge artificial intelligence, the ability to “upgrade” the underlying platform (whether software, processor, etc.) is particularly important. Each new generation of processor and module technology usually provides a better power/performance balance for the inference engine at the edge of the network, so it can quickly utilize these performance and power gains to minimize the interruption of the entire logistics automation system. And the edge artificial intelligence system design is also an obvious advantage.
By using the microservice architecture and Docker container technology, the modularity in the hardware is extended to the software. If a more optimized processor solution is available, even if it comes from a different manufacturer, the software utilization processor is modular and can replace the previous processor without changing the rest of the system. Software containers also provide a simple and powerful way to add new functions to run in edge artificial intelligence.
The software in the container can also be modular. ADLINK’s Edge Vision Analytics (EVA) SDK (software development kit) for artificial intelligence vision products is a typical example. The platform is based on Gstreamer and focuses on the basic functions required to build artificial intelligence vision pipelines. Each stage of the artificial intelligence vision pipeline uses ready-made open source plug-ins (self-contained modules) to simplify the development of the pipeline. These plug-ins include image capture and processing, artificial intelligence inference, post-processing and analysis.
The modularity and container approach of hardware and software minimizes the risk of being tied up by vendors, which means that the solution does not depend on any specific platform. It also increases the abstraction between platforms and applications, making it easier for end users to develop their own applications that do not depend on any platform.
We simplify the upgrade process with a database that characterizes components when they are available. Using this database, engineers can choose suitable products to achieve a perfect balance between inference performance and system resources.
One of the most important requirements of logistics automation is to respond in real time. Therefore, it is very important to cooperate with a supplier who has extensive experience in software and hardware combined development systems and can meet application requirements. ADLINK’s approach is to use modules that can be integrated with professional third-party technologies (such as LiDAR sensors).
The deployment of edge artificial intelligence in logistics automation does not require the replacement of the entire system. First, you need to evaluate the workspace and determine the stages where you can really benefit from artificial intelligence. The main goal is to reduce operating expenses while improving efficiency, especially in times of labor shortage to cope with the increase in demand.
More and more technology companies are committed to developing artificial intelligence solutions, but most companies usually only focus on cloud computing, not edge computing. On the edge side, its operating conditions are different, resources may be limited, and a dedicated network may even be required.
Through the use of artificial intelligence and other technologies, automation will continue to grow and expand in logistics operations. These system solutions need to be specially designed to meet the harsh operating environment, which is completely different from the needs of the cloud or data center. We use a modular approach to solve this problem, which provides a highly competitive solution, a shorter development cycle and a flexible platform.