Localization of Biobotic Insects Using Low-Cost Inertial Measurement Units

The Walking Control of a Crab Biorobot in Amphibious Environment

This article describes development of a crab biorobot that is capable of traversing diverse environments including both land and water. We have transformed a living rainbow crab into a walking biorobot by attaching wireless controller. An anatomical and physiological investigation revealed the rainbow crabs have sensory system on the carapace. Based on this finding, we implanted electrodes into the carapace. The walking direction of the robot is controlled through electrical stimulation provided by the controller. Depending on this site, the crab biorobot is induced to walk forward, leftward, and rightward in both terrestrial and underwater conditions. There is no significant difference in the mean walking direction between the two conditions. Smooth transition from land to water of the crab biorobot further demonstrates the adaptability to amphibious environment. This biorobot is compact, measuring 5 cm in carapace and weighing 50 g including the wireless controller. The crab biorobot in this scale has a potential for application narrow and unstructured in waterfront environments.

Movement Optimization for a Cyborg Cockroach in a Bounded Space Incorporating Machine Learning

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%.

Simultaneous Localization of Biobotic Insects using Inertial Data and Encounter Information

Several recent research efforts have shown that the bioelectrical stimulation of their neuro-mechanical system can control the locomotion of Madagascar hissing cockroaches (Gromphadorhina portentosa). This has opened the possibility of using these insects to explore centimeter-scale environments, such as rubble piles in urban disaster areas. We present an inertial navigation system based on machine learning modules that is capable of localizing groups of G. portentosa carrying thorax-mounted inertial measurement units. The proposed navigation system uses the agents' encounters with one another as signals of opportunity to increase tracking accuracy. Results are shown for five agents that are operating on a planar (2D) surface in controlled laboratory conditions. Trajectory reconstruction accuracy is improved by 16% when we use encounter information for the agents, and up to 27% when we add a heuristic that corrects speed estimates via a search for an optimal speed-scaling factor.