team four

1. Team four

2. Sensor fusion with inverse-variance weighting For this project, we plan to use the Arduino development board as our hardware device.Additionally, we have chosen distance as the measurement data for our sensor because we need a convenient and easy-to-measure variable to provide data to the sensor. In terms of specific models, we have selected the HC-SR04 ultrasonic module, VL6180X ranging sensor, and US-100 ultrasonic module.

3. Inverse-variance weighting This method combines the data from all sensors by calculating the variance of each sensor's data and assigning a higher weight to sensors with smaller variances in order to reduce the error.

4. Layout diagram in the simulation software.

5. Implement We intend to connect all three sensors to the Arduino simultaneously, and use a relatively large, smooth object as the measurement target to ensure that the distance measured by each sensor is relatively consistent. In terms of the algorithm, our first step is to write code to calculate the average and variance of the data from each group of sensors, and obtain the verified results based on the formula.

6. Problem to solve However, we do not know how many sets of data are needed to achieve the desired effect, so we need to experiment with it. We start with a small amount of training data to calculate the weights, and then gradually increase the amount of training data until the results of calculating the weights are almost the same for several times. We repeat this process to check for generality or consistency. After obtaining the final number of training iterations, we will compare its calculated value with the real data in subsequent tests to check for errors.