A hierarchical model for external electrical control of an insect, accounting for inter-individual variation of muscle force properties

Cyborg control of insect movement is promising for developing miniature, high-mobility, and efficient biohybrid robots. However, considering the inter-individual variation of the insect neuromuscular apparatus and its neural control is challenging. We propose a hierarchical model including inter-individual variation of muscle properties of three leg muscles involved in propulsion (retractor coxae), joint stiffness (pro- and retractor coxae), and stance-swing transition (protractor coxae and levator trochanteris) in the stick insect Carausius morosus. To estimate mechanical effects induced by external muscle stimulation, the model is based on the systematic evaluation of joint torques as functions of electrical stimulation parameters. A nearly linear relationship between the stimulus burst duration and generated torque was observed. This stimulus-torque characteristic holds for burst durations of up to 500ms, corresponding to the stance and swing phase durations of medium to fast walking stick insects. Hierarchical Bayesian modeling revealed that linearity of the stimulus-torque characteristic was invariant, with individually varying slopes. Individual prediction of joint torques provides significant benefits for precise cyborg control.

Omnidirectional Jump Control of a Locust-Computer Hybrid Robot

Jumping locomotion is critical for microrobots to overcome obstacles. Among the microjumping robots, the development of an omnidirectional jumping mechanism is challenging. To avoid the complicated microfabrication process, we present an insect-computer hybrid robot by controlling the locomotions of an Oriental Migratory Locust (Locusta migratoria manilensis, Meyen 1835). The insect-computer hybrid robot achieves repetitive omnidirectional jumps of ∼100 mm high. A series of experiments on jumping control, turning control, and collaborative directional jumping control are carried out. We also demonstrate the implementation of a wireless stimulator backpack that provides remote locomotion control, which transforms the insect into a hybrid robot. Moreover, a feedback jump control system is subsequently presented. The results indicate that the hybrid robot could easily achieve an omnidirectional jump and maintain body righting after landing. This robot is well-suited for applications that require locomotion on uneven terrains, such as environmental surveillance and search and rescue.

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%.

Will microfluidics enable functionally integrated biohybrid robots?

The next robotics frontier will be led by biohybrids. Capable biohybrid robots require microfluidics to sustain, improve, and scale the architectural complexity of their core ingredient: biological tissues. Advances in microfluidics have already revolutionized disease modeling and drug development, and are positioned to impact regenerative medicine but have yet to apply to biohybrids. Fusing microfluidics with living materials will improve tissue perfusion and maturation, and enable precise patterning of sensing, processing, and control elements. This perspective suggests future developments in advanced biohybrids.

Functionalized carbon nanotube microfibers for chronic neural implants

BACKGROUND

Much progress has been made at the interface between neural tissue and electrodes for neurophysiology. However, there continues to be a need for novel materials that integrate well with the nervous system and facilitate neural recordings with longer-term sustainability and stability. Such materials have the potential to improve clinical approaches and provide important tools for basic neuroscience research.

NEW METHOD

In this paper, we explore the use of dry-spun untreated or functionalized carbon nanotube fibers as implantable electrodes for neural recordings from insects over extended time periods.

RESULTS

Measurements of fly eyes responding to light flashes illustrate the suitability of these materials for recording both the low- and high-frequency components of neural signals. Repeated recordings show good sustainability, especially with functionalized carbon nanotube fibers. In particular, recordings from the optic lobes of Madagascar hissing cockroaches last for at least 8 weeks.

COMPARISON WITH EXISTING METHOD(S)

Electrophysiological research continues to rely heavily on metal electrodes that are good for short-lived preparations but less suitable for longer-term recordings, as scar tissue formation and cytotoxicity tend to cause a gradual reduction in signals.

CONCLUSIONS

Functionalized carbon nanotubes are a promising novel material that can be used to obtain long-term or repeated stable recordings, which are necessary for longitudinal studies, or to maintain other neural tissue interfaces such as those in insect-machine hybrid robots. The introduced insect preparation can also be used for the relatively rapid and cost-efficient testing of novel electrode materials.

Insect-machine hybrid robot

Recently, insect-machine hybrid robots have been developed that incorporate insects into robots or incorporate machines into insects. Most previous studies were motivated to use the function of insects for robots, but this technology can also prove to be useful as an experimental tool for neuroethology. We reviewed hybrid robots in terms of the closed-loop between an insect, a robot, and the real environment. The incorporated biological components provided the robot sensory signals that were received by the insects and the adaptive functions of the brain. The incorporated artificial components permitted us to understand the biological system by controlling insect behavior. Hybrid robots thus extend the roles of mobile robot experiments in neuroethology for both model evaluation and brain function analysis.

Research on Key Techniques of Insect Flapping Onset Control Based on Electrical Stimulation

In this paper, an insect flapping onset control method based on electrical stimulation is proposed. The beetle (Allomyrina dithotomus, Coleoptera) is employed for the research carrier, and it’s left and right longitudinal muscles are electrically stimulated to control the flapping onset behavior. The control principle of insect flapping onset utilizing electrical stimulation is analyzed firstly followed by the movement function of the dorsal longitudinal muscle. Subsequently, a micro-control system, which is composed of a PC controller, coordinator and electronic backpack, is designed to realize the wireless control of beetle movements. Finally, the verification experiment is implemented to verify the effectiveness of dorsal longitudinal muscle stimulation with respect to the beetle flapping onset, whereas the comparative experiment emphasizes on determining optimal simulating parameters. The experimental results demonstrate that when the period, duty ratio, number of and amplitude of pulses stimulation signal are assigned to 5 ms, 20%, 90 and 3.3 V respectively, the beetle flapping onset can be controlled with an average response time of 1.69 s. Simultaneously, the optimization of duty ratio from 20% to 40%, and the number of pulses from 90 to 100, is proved to the best parameter configuration.