Modified Bilateral Active Estimation Model: A Learning-Based Solution to the Time Delay Problem in Robotic Tele-Control

The ubiquitous presence of three types of delay in robotic teleoperation systems, i.e., computation delay, transmission delay, and mechanical delay, is the major factor of the system degradation. It is noticeable that the transmission latency over the communication network shows a periodic trend due to the network flux changing. Accordingly, in our previous work, a neural network-based open-loop approach named Bilateral Active Estimation Model (BAEM) was proposed to compensate for the upcoming transmission delay in a unilateral teleoperation system by sending predicted trajectories as commands. In this letter, a modified version of BAEM (m-BAEM) is proposed to compensate for all these three types of delay explicitly, and a real-time robotic teleoperation system based on Robot Operating System 2 (ROS 2) framework is built to evaluate the performance of the m-BAEM in constant and varying delay scenarios with both pre-defined and human-input trajectories. The results of pre-defined trajectories present the satisfactory performance of the m-BAEM even in the presence of transmission delay up to 1000 miliseconds with large variations. The main limitation of the m-BAEM is that it is yet unable to handle unknown trajectories.

Automated summary

The presence of computation delay, transmission delay, and mechanical delay in robotic teleoperation systems is a major factor in system degradation. A neural network-based open-loop approach called BAEM has been proposed to compensate for transmission delay by sending predicted trajectories as commands. A modified version of BAEM (m-BAEM) is proposed to explicitly compensate for all three types of delay, and a real-time robotic teleoperation system based on the ROS 2 framework is built to evaluate its performance.