Mechanical control

Case:

In the automotive production line, the robotic arms used for automotive body welding need to complete complex welding tasks with high precision. For example, in the production workshop of a well-known automobile brand, the robot arm undertakes the work of accurately welding the body parts together, completing hundreds of welding operations every day, and the position accuracy and welding quality of the welding contact are extremely high.

Working mode:

The robotic arm is equipped with a navigation module based on LiDAR and a joint encoder. The LiDAR scans the work area in real time, creating a high-precision three-dimensional map that accurately identifies the position and attitude of the body parts. The joint encoder precisely measures the rotation Angle and displacement of each joint of the manipulator and feeds back to the control system. When receiving the welding task instruction, the control system calculates the motion trajectory of each joint of the robot arm through kinematic algorithm based on the data of LiDAR and joint encoder, guides the welding tool at the end of the robot arm to reach the welding point accurately, and performs welding operations according to the preset welding parameters and paths to ensure the stable and reliable quality of each welding point. The error is controlled within the millimeter level, thus ensuring the overall structural strength and safety of the car body.

Intelligent robot (track chassis as an example)

Case:

In the mine mining scenario, an intelligent robot for ore transportation uses a tracked chassis design to drive autonomously on complex and rugged mine roads, transporting mined ore from the mining area to the designated stacking site. For example, in a large open-pit iron mine, this intelligent robot effectively improves the efficiency of ore transportation, reducing labor costs and transportation risks.

Working mode:

The robot's track chassis is equipped with a variety of navigation devices, including inertial navigation system (INS), global Positioning System (GPS) and vision sensors. The inertial navigation system uses gyroscope and accelerometer to monitor the robot's attitude and acceleration changes in real time, and calculates its own position and driving direction. GPS provides the approximate position information of the robot in the global coordinate system, which is used to correct the accumulated error of the inertial navigation system. Vision sensors collect and analyze images of the surrounding environment to identify features such as road boundaries, obstacles and target stacking areas. During the driving process, the robot's control system uses advanced path planning algorithms, such as A * algorithm or Dijkstra algorithm, according to the data provided by these navigation modules to plan an optimal driving path from the current position to the target position. At the same time, by adjusting the speed and steering Angle of the track in real time, the robot can drive stably along the planned path, automatically avoid obstacles, and successfully complete the ore transportation task. Even in the harsh conditions such as dust and turbulence in the mining environment, it can maintain high positioning accuracy and driving stability, and the positioning error is usually within a few meters, meeting the actual needs of mining operations.