Fig 3: Proposed hybrid network framework
The algorithm for predicting teleoperator behaviour using LSTM and DDPG to control a telepresence robot during delayed communication is as follows:
ALGORITHM 1: A hybrid approach of integrating LSTM and DDPG to predict teleoperator behaviour
1: define and declare the prediction function of teleoperator behaviour
2: // preprocess data
3: processed_data = preprocess_data(data)
4: // train LSTM network
5: lstm_model = train_lstm(processed_data)
6: // make predictions using LSTM network
7: teleoperator_actions = lstm_model.predict(processed_data)
8: // train DDPG algorithm
9: ddpg_model = train_ddpg(processed_data)
10: // use DDPG algorithm to choose actions for telepresence robot
11: telepresenceRobot_actions= ddpg_model.choose_actions(teleoperator
12: // update LSTM and DDPG models with new data DDPG algorithm to choose actions for telepresence robot
13: lstm_model.update(new_data)
14: ddpg_model.update(new_data)
15: // use reinforcement learning to reward DDPG algorithm for positive outcomes
16: ddpg_model.reward(positive_outcomes)
17: // continuously update telepresence robot actions in real-time based on predicted teleoperator actions and current state
18: while True:
19: teleoperator _actions = lstm_model.predict(current_data)
20: telepresenceRobot_actions =ddpg_model.choose_actions(teleoperator
21: execute_actions(telepresenceRobot_actions)
22: end while
The above algorithm outlines the basic steps for predicting teleoperator behaviour using LSTM and DDPG to control an autonomous car. It includes preprocessing the data, training the LSTM and DDPG models, making predictions using the LSTM model, choosing actions for the telepresence robot using the DDPG model, continuously updating the models with new data, and using reinforcement learning to reward the DDPG model for positive outcomes. The telepresence robot’s actions are continuously updated in a loop based on the predicted teleoperator actions and the current state of the robot.
Experiment Setup and Result Discussion: In this section, the experimental setup and its results are both explained with the proposed approach of controlling the telepresence robot during the delayed communication with the teleoperator.
The setup consists of a custom-manufactured telepresence robot and a remote-controlled setup of a teleoperator, as shown in Figure 4. The telepresence robot is powered by two DC-geared motors of 200 watts. The telepresence robot was equipped in the actual experiments with two Lidar sensors at various heights with a maximum measurement range of ten meters.