Some models also support AI quality assessment, which analyzes historical data through machine learning algorithms, automatically identifies process deviation trends, and provides early warnings of equipment failures or process problems.
The system must have self-diagnosis and protection functions, monitor the status of key components in real time, and immediately stop and alarm when a fault occurs.
The overload protection mechanism needs to integrate temperature compensation function, and dynamically adjust the protection threshold through built-in temperature model or real-time temperature feedback.
Ultrasonic sensors serve as the last line of defense for near-field safety protection. They detect nearby obstacles around the robot by emitting ultrasonic waves and receiving reflected waves.
As a core piece of equipment in the field of warehouse automation, the fork picking robot's ability to adapt to complex warehousing environments directly determines the potential for improving logistics efficiency.
Operators need to adjust the velocity gradient, pressure threshold and holding time at each stage according to material properties, workpiece structure and assembly requirements.