## 5 Latency Mitigation Strategies
### Control Latency Approaches
Approaches such as supervisor control involve the operator sending high-level commands, minimising communication requirements and delay, where the remote robot or vehicle needs planning and control algorithms. However, it does not involve continuous teleoperation [114]. Onboard control autonomy capabilities can be activated to mitigate the effects of delay. Such approaches can be classified as adjustable autonomy, collaborative control, mixed-initiative control, and sliding autonomy [51]. They are all impacted by communication delays and are computationally expensive [45]. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Reference** & **Domain** & **Teleoperation Entity** & **Algorithm** \\ \hline
[45] & Vehicle teleoperation & Control delay and sensor delay & Model free predictive framework \\ \hline
[69] & Robotic teleoperation & Delay and control prediction & LSTM-based bilateral active estimation model (BAEM) \\ \hline
[70] & Telesurgery, control framework & Control and force feedback & Kalman filter and RL-based DDPG algorithm \\ \hline
[49] & Robotic teleoperation & Control, steering & PD controller \\ \hline
[42] & Teleoperated military UGVs & Control, steering, and heading prediction & Steering-model-based feed-forward predictor, model-free \\ \hline
[50] & Robotic teleoperation & Control and haptic data & Input-to-state stability (ISS) controller \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Mitigation strategies for control latency. \begin{table}
\begin{tabular}{p{56.9 pt} p{85.4 pt} p{85.4 pt} p{85.4 pt}} \hline
**Reference** & **Domain** & **Teleoperation Entity** & **Algorithm** \\ \hline
[51] & Robotic tele-operation & Control and haptic & Llewellyn’s criterion and a passivity-based criterion with and without the wave transformation. \\ \hline
[123] & Robotic tele-operation (medical) & Control prediction & Gated recurrent units (GRUs) integrated with a double deep Q-learning network (DDQN) algorithm \\ \hline
[87] & Robotic tele-operation & Control & Adaptive interval type-2 fuzzy neural network, Lyapunov–Krasovskii method \\ \hline
[124] & Space robotic teleoperation & Control interface & Interactive planning and supervised execution (IPSE) teleoperation system \\ \hline
[115] & Robotic tele-operation & Control uncertainty & Integrate RL with model-mediated teleoperation (MMT) \\ \hline
[74] & Telepresence robot & Predict control commands & Integrate RNN and LSTM with RL-DDPG for predicting the behaviour of the teleoperator \\ \hline
[44] & Teleoperation of multiple robots & Control & A wave-variable-based time-delay-compensated four-channel architecture for multilateral teleoperation \\ \hline
[114] & Bilateral tele-operation & Control & Adaptive NN based on Markov jump, partial feedback linearisation using nominal dynamics, Lyapunov–Krasovskii functional \\ \hline
[54] & Robotic tele-operation & Control— handwritten drawing & K-means, Gaussian mixture model (GMM), hidden semi-Markov models (HSMMs), and linear quadratic tracking (LQT) for motion recognition and segmentation \\ \hline
[48] & Bilateral robot teleoperation & Control and force feedback & Force controller and time-domain passivity approach (TDPA) \\ \hline
[125] & Bilateral tele-operation & Improve control & Integral-order and fractional-order PD controller, along with frequency-domain analysis for maximum upper bound of delay, Lyapunov–Krasovskii functional \\ \hline
[118] & Train to ground communication & State and network prediction & LSTM and high-degree polynomial linear regression (HPLR) \\ \hline \end{tabular}
\end{table} 
Table 5: _Cont._