Acceleration prediction for mobile robots using deep neural networks for shock avoidance

Abstract

This study proposes a system designed to enhance the safety of mobile robots by predicting shocks and vibrations in advance during operation. Mobile robots deployed in disaster areas and hazardous environments are susceptible to tipping, sensor failures, or misdetections due to shocks and vibrations caused by traversing uneven terrain. These disruptions can lead to errors in terrain measurement and degrade mapping accuracy. The proposed system utilizes Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) networks to predict the maximum resultant acceleration in the near future based on data from a depthcamera, an inertial measurementunit (IMU), andacrawler encoder. Experimental results demonstrated that the systemcan predict acceleration with high accuracy and low latency, achieving an inference time of approximately 5.06ms and a prediction error of 0.98m/s². Future work will focus on deployment on real hardware, with the expectation that the system will contribute to improving the reliability of mobile robots operating in rough terrain.