Time Series Classification of IMU Data for Point of Impact Localization

Abstract

Collision detection is a crucial part of every mobile robot system. The field of collision detection has received a lot of attention in recent years. Proper handling of a collision event involves many challenges. Once a collision has occurred, the robot needs to decide on how to proceed. However, prior to taking action it is important to localize the point of impact. This can be done efficiently and accurately using machine learning methods. We show how the recent method FRUITS can be used for point of impact localization using IMU data on a mobile robot. We also compare it with the very efficient algorithm ROCKET. Our results show that both methods are able to accurately identify discrete points of impact but FRUITS has a quicker response time.