Generation of complex motor patterns in american grasshopper via current-controlled thoracic electrical interfacing

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.

Progresses of animal robots: A historical review and perspectiveness

Animal robots have remarkable advantages over traditional mechatronic ones in terms of energy supply, self-orientation, and natural concealment and can provide remarkable theoretical and practical values for scientific investigation, community service, military detection and other fields. Given these features, animal robots have become high-profile research objects and have recently attracted extensive attention. Herein, we have defined animal robots, reviewed the main types of animal robots, and discussed the potential developing directions. We have also detailed the mechanisms underlying the regulation of animal robots and introduced key methods for manipulating them. We have further proposed several application prospects for different types of animal robots. Finally, we have presented research directions for their further improvement.

Experimental Verification on Steering Flight of Honeybee by Electrical Stimulation

The artificial locomotion control strategy is the fundamental technique to ensure the accomplishment of the preset assignments for cyborg insects. The existing research has recognized that the electrical stimulation applied to the optic lobes was an appropriate flight control strategy for small insects represented by honeybee. This control technique has been confirmed to be effective for honeybee flight initiation and cessation. However, its regulation effect on steering locomotion has not been fully verified. Here, we investigated the steering control effect of honeybee by applying electrical stimulation signals with different duty cycles and frequencies on the unilateral optic lobes and screened the stimulus parameters with the highest response successful rate. Moreover, we confirmed the effectiveness of steering control by verifying the presence of rotation torque on tethered honeybees and the body orientation change of crawling honeybees. Our study will contribute some reliable parameter references to the motion control of cyborg honeybees.

Cyborg Moth Flight Control Based on Fuzzy Deep Learning

Cyborg insect control methods can be divided into invasive methods and noninvasive methods. Compared to invasive methods, noninvasive methods are much easier to implement, but they are sensitive to complex and highly uncertain environments, for which classical control methods often have low control accuracy. In this paper, we present a noninvasive approach for cyborg moths stimulated by noninvasive ultraviolet (UV) rays. We propose a fuzzy deep learning method for cyborg moth flight control, which consists of a Behavior Learner and a Control Learner. The Behavior Learner is further divided into three hierarchies for learning the species’ common behaviors, group-specific behaviors, and individual-specific behaviors step by step to produce the expected flight parameters. The Control Learner learns how to set UV ray stimulation to make a moth exhibit the expected flight behaviors. Both the Control Learner and Behavior Learner (including its sub-learners) are constructed using a Pythagorean fuzzy denoising autoencoder model. Experimental results demonstrate that the proposed approach achieves significant performance advantages over the state-of-the-art approaches and obtains a high control success rate of over 83% for flight parameter control.

A review on animal-robot interaction: from bio-hybrid organisms to mixed societies

Living organisms are far superior to state-of-the-art robots as they have evolved a wide number of capabilities that far encompass our most advanced technologies. The merging of biological and artificial world, both physically and cognitively, represents a new trend in robotics that provides promising prospects to revolutionize the paradigms of conventional bio-inspired design as well as biological research. In this review, a comprehensive definition of animal-robot interactive technologies is given. They can be at animal level, by augmenting physical or mental capabilities through an integrated technology, or at group level, in which real animals interact with robotic conspecifics. Furthermore, an overview of the current state of the art and the recent trends in this novel context is provided. Bio-hybrid organisms represent a promising research area allowing us to understand how a biological apparatus (e.g. muscular and/or neural) works, thanks to the interaction with the integrated technologies. Furthermore, by using artificial agents, it is possible to shed light on social behaviours characterizing mixed societies. The robots can be used to manipulate groups of living organisms to understand self-organization and the evolution of cooperative behaviour and communication.

Insect-computer hybrid legged robot with user-adjustable speed, step length and walking gait

We have constructed an insect-computer hybrid legged robot using a living beetle (Mecynorrhina torquata; Coleoptera). The protraction/retraction and levation/depression motions in both forelegs of the beetle were elicited by electrically stimulating eight corresponding leg muscles via eight pairs of implanted electrodes. To perform a defined walking gait (e.g., gallop), different muscles were individually stimulated in a predefined sequence using a microcontroller. Different walking gaits were performed by reordering the applied stimulation signals (i.e., applying different sequences). By varying the duration of the stimulation sequences, we successfully controlled the step frequency and hence the beetle's walking speed. To the best of our knowledge, this paper presents the first demonstration of living insect locomotion control with a user-adjustable walking gait, step length and walking speed.

Effective Stimulus Parameters for Directed Locomotion in Madagascar Hissing Cockroach Biobot

Swarms of insects instrumented with wireless electronic backpacks have previously been proposed for potential use in search and rescue operations. Before deploying such biobot swarms, an effective long-term neural-electric stimulus interface must be established, and the locomotion response to various stimuli quantified. To this end, we studied a variety of pulse types (mono- vs. bipolar; voltage- vs. current-controlled) and shapes (amplitude, frequency, duration) to parameters that are most effective for evoking locomotion along a desired path in the Madagascar hissing cockroach (G. portentosa) in response to antennal and cercal stimulation. We identified bipolar, 2 V, 50 Hz, 0.5 s voltage controlled pulses as being optimal for evoking forward motion and turns in the expected contraversive direction without habituation in ≈50% of test subjects, a substantial increase over ≈10% success rates previously reported. Larger amplitudes for voltage (1–4 V) and current (50–150 μA) pulses generally evoked larger forward walking (15.6–25.6 cm; 3.9–5.6 cm/s) but smaller concomitant turning responses (149 to 80.0 deg; 62.8 to 41.2 deg/s). Thus, the radius of curvature of the initial turn-then-run locomotor response (≈10–25 cm) could be controlled in a graded manner by varying the stimulus amplitude. These findings could be used to help optimize stimulus protocols for swarms of cockroach biobots navigating unknown terrain.