Millions of households are using autonomous robot cleaners every day. The latest generations of vacuum cleaners offer smart features, such as the possibility to use drops of water to clean floors before hoovering. Such advanced features require a high degree of environmental awareness, including the need to recognize the type of floor, so the robot can change cleaning modes and, for example, avoid spraying water on carpet floors. Using artificial intelligence in robot vacuum cleaners makes these devices more environmentally aware.
The type of floor can be identified based on its level of softness. If the robot vacuum cleaner detects soft material, like a carpet, it can change the cleaning mode to avoid using water on this type of surface.
- In this project we used the signal data from the VL53L5 Time-of-Flight (ToF) sensor with 8 x 8 multi-zone detection, which was integrated in the front of a robot cleaner (4.5cm above the floor and 21.5 degrees tilted).
- Then, we created a collection of different types of material (including soft and hard floor materials) and trained the neural network (NN) model before pre-processing and post-processing the information to improve accuracy.
- Finally, we implemented the NN model into an MCU thanks to the STM32Cube.AI software package.
In comparison with standard programming, AI algorithms offer higher levels of accuracy and can easily adapt to special use cases.
Time-of-Flight (reference: VL53L5CX)
Dataset: signal rate from ToF (output: hard or soft)
Data format: 8×8 range @15Hz
Model: Multilayer Perceptron (MLP)
68 Kbytes of flash memory for weights
1.6 Kbyte of RAM for activations
Accuracy: 96% on more than 50 pieces of material around 200,000 samples
Performance on STM32F401 @84MHz
Inference time: 7 ms