Biobot-based insects have been investigated so far for various applications such as search and rescue operations, environmental monitoring, and discovering the environment. These applications need a strong international collaboration to complete the tasks. However, during the COVID-19 pandemic, most people could not easily move from one country to another because of the travel ban. In addition, controlling biobots is challenging because only experts can operate the cockroach behavior with and without stimulated response. In order to solve this issue, we proposed a user-friendly teleoperation user interface (UI) to monitor and control the biobot between Japan and Bangladesh without onsite operation by experts. This study applied Madagascar hissing cockroaches (MHC) as a biobot hybrid robot. A multithreading algorithm was implemented to run multiple parallel computations concurrently on the UI. Virtual network computing (VNC) was implemented on the teleoperation UI as remote communication for streaming real-time video from Japan to Bangladesh and sending remote commands from Bangladesh to Japan. In the experiments, a remote operator successfully steered the biobot to follow a predetermined path through a developed teleoperation UI with a time delay of 275 ms. The proposed interactive and intuitive UI enables a promising and reliable system for teleoperated biobots between two remote countries.

Cockroaches can traverse unknown obstacle-terrain, self-right on the ground and climb above the obstacle. However, they have limited motion, such as less activity in light/bright areas and lower temperatures. Therefore, the movement of the cyborg cockroaches needs to be optimized for the utilization of the cockroach as a cyborg insect. This study aims to increase the search rate and distance traveled by cockroaches and reduce the stop time by utilizing automatic stimulation from machine learning. Multiple machine learning classifiers were applied to classify the offline binary classification of the cockroach movement based on the inertial measuring unit input signals. Ten time-domain features were chosen and applied as the classifier inputs. The highest performance of the classifiers was implemented for the online motion recognition and automatic stimulation provided to the cerci to trigger the free walking motion of the cockroach. A user interface was developed to run multiple computational processes simultaneously in real time such as computer vision, data acquisition, feature extraction, automatic stimulation, and machine learning using a multithreading algorithm. On the basis of the experiment results, we successfully demonstrated that the movement performance of cockroaches was importantly improved by applying machine learning classification and automatic stimulation. This system increased the search rate and traveled distance by 68% and 70%, respectively, while the stop time was reduced by 78%.

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