AliExpress: This Monday (23), Chinese e-commerce giant Alibaba, known for owning AliExpress, announced that it will deploy 1,000 autonomous delivery robots in Chinese urban communities this year.

Dubbed Xiaomanlvs—Little Donkeys in Mandarin—the devices can carry up to 50 packages and deliver 500 boxes in a single day, covering 100 kilometers on a single full battery charge. The company also stressed that the self-employed delivery people “do not stop to smoke” or make detours from their path.

The objective of this “test drive” is to teach devices to avoid obstacles in alleys and on low-speed streets. Next, a team from Damo Academy, Alibaba’s scientific research center, will analyze the data acquired to improve the Xiaomanlvs and enable commuting on busier public streets.

The little ones collect packages at pre-established delivery points and travel to the client’s house, passing along sidewalks and bike paths. According to the company, the device’s algorithm identifies and plans the fastest route to arrival, and the robot is able to predict the movement of pedestrians and other vehicles 5 to 10 seconds in advance.

“In three to five years, we expect to progress autonomously in faster speed scenarios and deliver longer distances,” said Wang Gang, head of the Autonomous Driving Laboratory at Damo Academy. In the future, robots will be able to replace forklifts, transport medical waste and carry luggage at airports.

a justified change

According to the company, the covid-19 pandemic accelerated the demand for delivery without physical contact. The number of deliveries in the country increased to 830 billion in 2020 – almost nine times higher than in 2013.

Nevertheless, Alibaba reports that China has one of the oldest populations in the world. According to the World Health Organization, 28% of Chinese will be over 60 in 2040.

“We don’t have enough workforce for so many consumers. It’s impossible without autonomous technologies,” Wang said.

The academic believes that the robots algorithm is robust enough not to need high definition sensors. “We can learn more from our Algorithm to achieve mass and cost-effective deployment of devices,” he concluded.


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