DFRobot was founded in 2008 by Ricky Ye following his PhD in robotics and several years working in UK university research laboratories on projects for Airbus and in agricultural robotics. Now in its 17th year, the Shanghai-based company has grown from building mobile robot platforms for university research into a broad hardware and software ecosystem serving both the maker community and professional engineers. Its mission, as Ye describes it, is to “empower creation” — taking the friction out of system development so that engineers can move faster from concept to deployable product.
While DFRobot built its reputation in the maker space, Ye highlights a strategic evolution at this year’s embedded world. “We are not just focused on makers, we are also focused on engineers now,” he explained. The company’s presence at the show was deliberately aimed at demonstrating how its modular platforms can accelerate professional engineering work, particularly in the areas of AIoT and industrial smart systems. The core value proposition remains consistent across both audiences: eliminating the tedious integration work that consumes so much engineering time.
Central to that philosophy is DFRobot’s Gravity plug-and-play connector ecosystem, which allows hardware modules — sensors, computing boards, and peripherals — to be combined without the usual driver wrestling and compatibility headaches. “It plugs, and it works,” said Ye, allowing engineers to stay focused on system design rather than low-level integration.
One standout product range on show was DFRobot’s millimetre wave radar sensors, which offer superior capabilities that traditional PIR sensors. Because they analyse radio signals rather than heat signatures or images, they can detect a person even when completely still — a limitation that rules out PIR in many applications. The sensors also function reliably in darkness, dust, and fog, and raise no data privacy concerns.
Ye highlighted their suitability for smart building applications, including presence detection, fall detection, and sleep monitoring, noting that accurate occupancy data allows HVAC and lighting systems to respond only when genuinely needed.
On the computing side, DFRobot’s LattePanda Mu Edge computing modules address what Ye identifies as one of the biggest hidden costs in embedded development: designing a custom x86 board from scratch. “It will easily eat up a year of your dev time even if you didn’t see a working product,” he says. By packaging validated PCIe routing, memory layout, and signal integrity into a single proven module, DFRobot lets engineers concentrate on their application logic and I/O rather than the computing core. A related product, the LattePanda IOTA, pairs an Intel processor with an RP2040 microcontroller to solve the real-time control problem that arises because mainstream operating systems such as Windows and Linux are not deterministic. The MCU handles time-critical tasks like sensor sampling and motion control while the main processor manages AI inference and the user interface — an architecture Ye sees as particularly valuable for robotics and medical devices.
Rounding out DFRobot’s AIoT showcase was the HuskyLens 2, a vision module with built-in computing power, 30 pre-loaded models, and the ability to let engineers deploy custom AI object recognition algorithms to the device in minutes.
Asked what engineers working on complex AIoT projects under tight deadlines should take away from all of this, Ye’s advises: “Start from reliable building blocks. Making a prototype is not that hard, but the real pressure comes from reliable testing. If the groundwork is solid, life will be much easier.”
Watch the full interview here.
