Fig 6 : Comparison of succession rate of different approaches
We have illustrated the pathways of the navigation challenges, as shown
in Figure 7, to illustrate the benefits of our approach more clearly.
The graph clearly shows that our proposed method produces softer paths
while the paths produced by the comparative methods oscillate to be
different degrees.
Conclusion: We proposed a novel approach to control the
telepresence robot during delayed signals by integrating LSTM with the
DDPG model. It utilizes supervised and reinforcement learning to combine
the indication and assessment signals. The proposed hybrid technique
uses RNN in addition to the off-policy actor-critic architecture to
identify the best dynamic treatments. The comprehensive experiments on
the real-world manufactured telepresence robot generate a dataset by
multiple traversing of the same path in a healthcare environment. The
proposed approach showed appreciative results in simulation experiments
compared to other methods. After the data generation, our proposed
approach was used and revealed that the suggested method could boost
controllability by up to 2.3% and offer more control during the lack of
communication or commanding signals.