Application Overview
Hey friends, today we are going to talk about a super cool project—the Edge AI Siemens PLC Autonomous Driving Testing System! Doesn’t it sound exciting? I’ve been thrilled for a long time because this is a model that combines cutting-edge AI technology with classic PLC control!
The purpose of this project is to simulate the reactions and decisions of autonomous vehicles in complex environments, processing sensor data in real-time through edge computing, and then achieving precise control via Siemens PLC. This way, we can not only test the effectiveness of autonomous driving algorithms but also visually observe the vehicle’s performance under PLC control, which is simply a win-win!
Hardware Configuration
Before we get started, we need to prepare our tools. First, of course, we need a Siemens PLC; I chose the S7-1500 series this time, which has strong performance and excellent expandability. Then, we need various sensors and actuators, such as cameras, radars, motors, etc., which should be selected based on actual needs. Don’t forget about the edge computing device, as it is the core of our data processing!
Once the hardware is ready, the next step is wiring and debugging. This step requires caution, as incorrect wiring can cause damage! I spent quite a bit of time checking the manual while wiring, afraid of making even a small mistake. Fortunately, everything went smoothly in the end, and the hardware part was completed!
Program Design Approach
After the hardware is set up, we need to start programming. I pondered over this step for a long time, as it is the soul of the project! I decided to adopt a modular design approach, dividing the program into several functional blocks, such as data acquisition, data processing, decision control, and execution output. This way, not only is the program structure clear, but it is also convenient for later maintenance and expansion.
The data acquisition part involves obtaining environmental information around the vehicle through sensors, such as lane lines and obstacles. The data processing part uses the edge computing device to preprocess and extract features from this information. The decision control part generates control commands based on the processed information. Finally, the execution output part controls the vehicle’s actuators, such as steering and acceleration, through the PLC.
Program Implementation
Variable Definition
Before programming, we need to define various variables. For example, I defined variables for camera input, radar input, motor control, etc. These variables are crucial in the program, as they carry the flow of data and the transmission of control commands.
Main Program Implementation
The main program is implemented according to the modular approach mentioned above. First, initialize each functional block, then enter a loop to continuously collect data, process data, generate control commands, and execute outputs.
// Main program pseudocode
Initialize data acquisition functional block;
Initialize data processing functional block;
Initialize decision control functional block;
Initialize execution output functional block;
while (true) {
Collect data();
Process data();
Generate control commands();
Execute output();
}
Of course, the actual code is much more complex, with various condition checks and exception handling to consider. However, as long as the thought process is clear and we take it step by step, it’s not that difficult!
Template Functional Blocks
During programming, I also used template functional blocks. These templates correspond to the selected functional blocks based on the control program. For example, in the data acquisition part, I used camera acquisition templates and radar acquisition templates. In the data processing part, I used image processing templates and signal processing templates. This not only improved programming efficiency but also ensured code consistency and maintainability.
Function Expansion
At this point, the project can already meet basic requirements. However, I always like to tinker, so I thought about adding some new features. For instance, I added a vehicle positioning function, which can obtain real-time vehicle location information through GPS and map matching. This way, it can be used for path planning and also record the vehicle’s driving trajectory during testing.
Additionally, I added a remote monitoring function. Through Ethernet, the vehicle’s operating status can be transmitted in real-time to a remote server, allowing us to check the testing situation from the office at any time. Isn’t that convenient?
Debugging Methods
After programming is complete, the next step is debugging. This step requires technical skill and patience. I usually debug in modules first to ensure that each functional block works properly. Then, I conduct integrated debugging to see if the entire system can operate in coordination.
During debugging, I used several tools, such as PLC programming software and online monitoring software. These tools were incredibly helpful, allowing me to view the program’s running status and variable values in real-time, greatly improving debugging efficiency.
Application Expansion
Although this project is based on an autonomous driving testing system, its application range is quite broad. For example, in the field of industrial automation, we can use it for remote monitoring and control of intelligent devices. In the intelligent transportation field, it can be used for intelligent control and optimization of traffic signals. In short, as long as we are willing to think creatively, the potential of this project is limitless!
Troubleshooting
During the project implementation, I encountered several issues. For instance, there was a time when the sensor data wouldn’t come through, which made me anxious. After checking, I found that the wiring was loose. Another time, the program crashed during operation, and after some investigation, I discovered it was a memory overflow issue.
However, every time I encountered a problem, I patiently analyzed the cause and found a solution. After all, learning is a process of continuous trial and error and correction. As long as we maintain a positive attitude, there is no problem we cannot solve!
Conclusion
Alright, after all this, our project can be considered complete. Looking back over the entire process, I feel I have gained a lot. Not only did I learn how to combine edge AI and PLC to implement complex control systems, but I also improved my programming and debugging skills.
Of course, there are still many areas for improvement and expansion in this project. For example, we could add more sensors and actuators to enhance the system’s accuracy and reliability; we could also optimize algorithms to improve data processing speed and accuracy. In short, as long as we maintain a spirit of learning and exploration, we can make this project better and better!
Finally, I want to say that learning is a long and arduous process, but as long as we persevere, we will surely reap abundant results! I hope we can all continue to progress in our respective fields and become better versions of ourselves! Let’s keep it up!