Abstract
In the article, a method of terrain recognition for robotic application has been described. The main goal of the research is to support the robot's motor system in recognizing the environment, adjusting the motion parameters to it, and supporting the location system in critical situations. The proposed procedure uses differences between calculated statistics to detect the diverse type and quality of ground on which wheeled robot moves. In the research IMU (Inertial Measurement Unit) has been used as a main source of data, especially 3-axis accelerometer and gyroscope. The experiment involved collecting data with a sensor mounted on a remotely controlled wheeled robot. This data was collected from 4 hand-made platforms that simulated different types of terrain. For terrain recognition, a neural network-based analytical model has been proposed. In this paper authors present results obtained from the application model to experimental data. The paper describes the structure of NN and the whole analytical process in detail. Then, based on a comparison of the obtained results with the results from other methods, the value of the proposed method was shown.
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Acknowledgment
This work is a part of the project which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 780883.
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Skoczylas, A., Stachowiak, M., Stefaniak, P., Jachnik, B. (2021). Terrain Classification Using Neural Network Based on Inertial Sensors for Wheeled Robot. In: Hong, TP., Wojtkiewicz, K., Chawuthai, R., Sitek, P. (eds) Recent Challenges in Intelligent Information and Database Systems. ACIIDS 2021. Communications in Computer and Information Science, vol 1371. Springer, Singapore. https://doi.org/10.1007/978-981-16-1685-3_35
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