Momentum Observer-Based Collision Detection Using LSTM for Model Uncertainty Learning
Daegyu Lim, Donghyeon Kim, Jaeheung Park
Abstract
As robots begin to collaborate with people in real life, safety needs to be rigorously ensured to reliably employ robots nearby. In addition to collision prevention algorithms, studies are being actively conducted on collision handling methods. Momentum Observer (MOB) was developed to estimate disturbance torque without using joint acceleration. However, the estimated disturbance from MOB contains not only the applied external torque but also model uncertainty such as friction and modeling error due to imprecise system identification. Our proposed method handles this problem by learning the model uncertainty with Long Short-Term Memory (LSTM) and thereby estimates the purely applied external torque with only proprioceptive sensors. The proposed method can be applied even when the information on the robot model is not available. The experiments using a real robot show that the external torque can be estimated and collisions can be detected accordingly even in a limited situation where a precise dynamics model and friction model are not available.