Insect-Inspired Robots: Bridging Biological and Artificial Systems

Manoonpong, Poramate; Patané, Luca; Xiong, Xiaofeng; Brodoline, Ilya; Dupeyroux, Julien; Viollet, Stéphane; Arena, Paolo; Serres, Julien

doi:10.3390/s21227609

Cited by 49 publications

(33 citation statements)

References 276 publications

(336 reference statements)

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“…There are studies that achieve complex locomotion by machine learning methods [49,50]. They perform the adjustment or optimization of control parameters, including CPG parameters, by a learning method based on a lot of sensing information.…”

Section: Discussionmentioning

confidence: 99%

Gait Transition from Pacing by a Quadrupedal Simulated Model and Robot with Phase Modulation by Vestibular Feedback

et al. 2021

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We propose a method to achieve autonomous gait transition according to speed for a quadruped robot pacing at medium speeds. We verified its effectiveness through experiments with the simulation model and the robot we developed. In our proposed method, a central pattern generator (CPG) is applied to each leg. Each leg is controlled by a PD controller based on output from the CPG. The four CPGs are coupled, and a hard-wired CPG network generates a pace pattern by default. In addition, we feed the body tilt back to the CPGs in order to adapt to the body oscillation that changes according to the speed. As a result, our model and robot achieve stable changes in speed while autonomously generating a walk at low speeds and a rotary gallop at high speeds, despite the fact that the walk and rotary gallop are not preprogramed. The body tilt angle feedback is the only factor involved in the autonomous generation of gaits, so it can be easily used for various quadruped robots. Therefore, it is expected that the proposed method will be an effective control method for quadruped robots.

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Section: Discussionmentioning

confidence: 99%

Gait Transition from Pacing by a Quadrupedal Simulated Model and Robot with Phase Modulation by Vestibular Feedback

et al. 2021

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“…Biomimetic robots have been used for decades to better understand the mechanisms underlying motor control of insects and other animals (for a review, see Manoonpong et al, 2021 ). Robotic models of animals complement purely computational models in that they must confront the full physics of the behavior being modeled.…”

Section: Introductionmentioning

confidence: 99%

Adaptive load feedback robustly signals force dynamics in robotic model of Carausius morosus stepping

2023

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Animals utilize a number of neuronal systems to produce locomotion. One type of sensory organ that contributes in insects is the campaniform sensillum (CS) that measures the load on their legs. Groups of the receptors are found on high stress regions of the leg exoskeleton and they have significant effects in adapting walking behavior. Recording from these sensors in freely moving animals is limited by technical constraints. To better understand the load feedback signaled by CS to the nervous system, we have constructed a dynamically scaled robotic model of the Carausius morosus stick insect middle leg. The leg steps on a treadmill and supports weight during stance to simulate body weight. Strain gauges were mounted in the same positions and orientations as four key CS groups (Groups 3, 4, 6B, and 6A). Continuous data from the strain gauges were processed through a previously published dynamic computational model of CS discharge. Our experiments suggest that under different stepping conditions (e.g., changing “body” weight, phasic load stimuli, slipping foot), the CS sensory discharge robustly signals increases in force, such as at the beginning of stance, and decreases in force, such as at the end of stance or when the foot slips. Such signals would be crucial for an insect or robot to maintain intra- and inter-leg coordination while walking over extreme terrain.

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“…For example, the muscle tissue and body morphology of an animal are quite different from the actuators of robots, and the animal's neural system is also much more complex than the control system of robots. To bridge the gap between biology and bio-inspired robots, these factors must be taken into account (Manoonpong et al, 2021 ). Therefore, further research and discussions relating to the multimodal concept of robot behavior still need to be conducted.…”

Section: Introductionmentioning

confidence: 99%