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Automatic identification of pallets on intelligent unmanned forklift trucks based on ToF sensors

Recently, a famous logistics research company has successfully developed an automatic pallet identification technology based on Time-of-Flight (ToF) sensors, which enables unmanned forklifts to automatically recognize pallets and carry out transportation operations, greatly improving logistics efficiency and safety.
Automatic identification of pallets on intelligent unmanned forklift trucks based on ToF sensors

It is reported that this new technology uses ToF sensors to measure the flight time of light in real-time. By calculating the product of time and speed of light, it can obtain the distance between the object and the sensor. Multiple ToF sensors are installed on the unmanned forklift for high-precision distance measurement of the surrounding environment, so as to accurately grasp the position and size of the pallet, and avoid collisions and misoperations.

On the unmanned forklift equipped with this technology, the size and position of the pallet can be preset through the program, and the unmanned forklift can automatically recognize and transport the pallet, greatly reducing the time and cost of manual intervention. At the same time, the technology can also automatically detect the stability of the pallet and adjust its posture in a timely manner during transportation, ensuring the safe transportation of goods.

According to the relevant person in charge , this technology has been tested in its logistics center and has significantly improved logistics efficiency and safety. In the future, the company will continue to promote technological research and development to make greater contributions to achieving more efficient and safer logistics transportation.
Automatic identification of pallets on intelligent unmanned forklift trucks based on ToF sensors

The successful development of automatic pallet identification technology based on ToF sensors is an important application of unmanned driving technology, and is expected to be widely used in the future intelligent logistics field

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