Work

IPT-DCD: Interpolation Predictor for Teleoperation Under Dynamic Communication Delay Using Deep Learning Approach

Year: 2025

Type: article

Abstract: Teleoperation systems experience degraded control stability and safety due to dynamic communication delays. This study proposes an Interpolation Predictor for Teleoperation under Dynamic Communication Delay (IPT-DCD), a predictor that reconstructs asynchronously received control commands via interpolation and predicts future commands using an encoder-decoder LSTM architecture. To restore the temporal consistency of delayed signals, a signal preprocessing technique called the Backward Shifting and Interpolation (BSI) was applied, enabling the transformation of received data into an undelayed and uniformly sampled format. As a result, the proposed model was capable of generating real-time steering command outputs through a many-to-many time series structure. Furthermore, to evaluate its effectiveness, IPT-DCD was experimentally compared with a baseline model, a Predictor for Teleoperation under Dynamic Communication Delay (PT-DCD). The results reveal that IPT-DCD exhibits significantly greater robustness to large communication delay outliers than the baseline, highlighting its effectiveness in dynamic and unstable teleoperation environments.

Source: Sensors

DOI: 10.3390/s25134118

Authors: Hwanhee Kang, Eugene Kim, Myeonghwan Hwang, Jaeguk Byeon, Jonghyeok An, Hyunrok Cha

Institutions: Korea University of Science and Technology, Korea Institute of Industrial Technology

Language: English

Topic: Teleoperation and Haptic Systems

Subfield: Mechanical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funder: Korea Institute of Industrial Technology