Abstract
Manipulator teleoperation can liberate humans from hazardous tasks. Signal noise caused by environmental disturbances and the devices' inherent characteristics may limit the teleoperation performance. This paper proposes an approach for inertial measurement unit (IMU) state estimation based on particle swarm optimization (PSO) and modulated long short-term memory (ML-LSTM) neural networks to mitigate the impact of IMU cumulative error on the robot teleoperation performance. A motion mapping model for the human arm and a seven-degree-of-freedom (7-DOF) robotic arm are first established based on global configuration parameters and a hybrid mapping method. This model is used to describe the impact of IMU cumulative error on the robot teleoperation performance. Subsequently, the IMU pose state estimation model is constructed using PSO and ML-LSTM neural networks. The initial data of multiple IMUs and handling handles are used for training the estimation model. Finally, comparative experiments are conducted to verify the performance of the proposed state estimation model. The results demonstrate that the PSO-ML-LSTM algorithm can effectively eliminate the impact of IMU cumulative errors on robot teleoperation.