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Live+8 todayUpdated 49m ago

Hesai to Supply 10 Million Lidars to Dreame's Robotic Lawn Mowing Ecosystem

Hesai Technology will supply 10 million JT series lidars to Dreame's robotic lawn mower ecosystem, marking the largest lidar order in history.

Order size

10 million

Market growth

$1.48B to $4.04B (2021-2028)

Units delivered

300,000+

What Happened

Hesai Technology has secured a record-breaking 10 million unit order as the exclusive lidar supplier for Dreame's and MOVA's robotic lawn mowers. The JT series lidar, weighing just 200 grams, offers a 360° × 40° ultra-wide field of view and is designed for precise positioning and obstacle avoidance in complex outdoor environments.

Order size

10 millionlidars

Largest lidar order in history

Since its launch, over 300,000 JT series lidars have been delivered, demonstrating strong reliability. The global robotic lawn mower market is projected to grow from $1.48 billion in 2021 to $4.04 billion by 2028, driven by technological breakthroughs.

MOVA, a brand within the Dreame ecosystem equipped with Hesai's JT series lidar, leads the European market and was recognized by Frost & Sullivan as the top-selling brand worldwide for lidar-enabled robotic lawn mowers by sales volume in 2025. At CES 2026, MOVA unveiled NAVAX AWD for off-road terrain and LiDAX Ultra for medium to small lawns.

Why this matters

This deal shows lidar technology is becoming a standard, affordable sensor for everyday robots, not just luxury cars, and could accelerate adoption of autonomous lawn mowers.

Terms in This Story

Lidar
A sensor that uses laser light to measure distances and create 3D maps of surroundings.
Field of View (FOV)
The extent of the observable world that a sensor can see at any given moment.
Point Cloud Engine
A system that processes lidar data to filter out noise and interference.
Physical AI
AI that can understand and interact with the physical world, enabling robots to perform tasks.
Read Original: Hesai Technology

Summarised from the linked release; details can be imperfect — always verify against the original source.