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1. A framework for biomimetic robot design applied to the development of a robotic model of Drosophila melanogaster

   Goldsmith, C (May 2025, The Research Repository at WVU)

   For decades, the field of biologically inspired robotics has leveraged insights from animal locomotion to improve the walking ability of legged robots. Recently, “biomimetic” robots have been developed to model how specific animals walk. By prioritizing biological accuracy to the target organism rather than the application of general principles from biology, these robots can be used to develop detailed biological hypotheses for animal experiments, ultimately improving our understanding of the biological control of legs while improving technical solutions. Much of this work involves biologically inspired walking controllers informed by the morphology and dynamics of the insect nervous system, which necessitate a robot with highly animal-like structure to prevent a brain-body mismatch. However, methods for codifying suitable fidelity in biomimetic robots currently vary, with limited generalizable methods for robot design. In this work, I outline a general framework for developing biomimetic robots that ensures kinematic and dynamic similarity between the robot and target animal. I then use this framework to develop and validate the robot Drosophibot II, a meso-scale robotic model of an adult fruit fly, Drosophila melanogaster. The resulting robot is novel for its close attention to the kinematics and dynamics of Drosophila, an increasingly important model of legged locomotion. Each leg’s proportions and degrees of freedom are modeled after Drosophila 3D pose estimation data. The predominant actuators for the robot are characterized to determine their inertial, elastic, and viscous properties and subsequently dynamically scale the robot's motions. I then use a developed program to automatically solve the inverse kinematics and inverse dynamics necessary for walking for the robot's structure and that of a to-scale model of the fly. By comparing the output of these models, I demonstrate that the robot and fly are kinematically and dynamically similar. The robot's electromechanical design is presented, then validated by having the robot’s walk forward, backward, and up an incline via open-loop straight line stepping with biologically inspired foot trajectories. Strain data from locations throughout the robot's legs is also recorded during these tests as an analog for mechanosensory feedback in a freely walking animal. Through these experiments, Drosophibot II demonstrates its utility for neuromechanical investigations by providing plausible neural data currently unobtainable in the animal. 

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2. Buoyancy Enabled Non-Inertial Dynamic Walking

   https://doi.org/10.1109/IROS55552.2023.10341582  

   Yim, Mark; Gosrich, Walker; Miskin, Marc (October 2023, IEEE)

   We propose a mechanism for low Reynolds num- ber walking (e.g., legged microscale robots). Whereas loco- motion for legged robots has traditionally been classified as dynamic (where inertia plays a role) or static (where the system is always statically stable), we introduce a new locomotion modality we call buoyancy enabled non-inertial dynamic walking in which inertia plays no role, yet the robot is not statically stable. Instead, falling and viscous drag play critical roles. This model assumes squeeze flow forces from fluid interactions combined with a well timed gait as the mechanism by which forward motion can be achieved from a reciprocating legged robot. Using two physical demonstrations of robots with Reynold’s number ranging from 0.0001 to 0.02 (a microscale robot in water and a centimeter scale robot in glycerol) we find the model qualitatively describes the motion. This model can help understand microscale locomotion and design new microscale walking robots including controlling forward and backwards motion and potentially steering these robots. 

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3. Drosophibot: a fruit fly inspired bio-robot

   Goldsmith, C. A.; Szczecinski, N. S.; Quinn, R. D. (July 2019, 8th International Conference, Living Machines 2019)

   We introduce Drosophibot, a hexapod robot with legs designed based on the Common fruit fly, Drosophila melanogaster, built as a test platform for neural control development. The robot models anatomical aspects not present in other, similar bio-robots such as a retractable abdominal segment, insect-like dynamic scaling, and compliant feet segments in the hopes that more similar biomechanics will lead to more similar neural control and resulting behaviors. In increasing biomechanical modeling accuracy, we aim to gain further insight into the insect’s nervous system to inform the current model and subsequent neural controllers for legged robots. 

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4. Analyzing the interplay between local CPG activity and sensory signals for inter-leg coordination in Drosophila

   Nourse, W. R.; Szczecinski, N. S.; Haustein, M.; Bockemühl, T.; Büschges, A.; Quinn, R. D. (July 2019, 8th International Conference, Living Machines 2019)

   Leg coordination is important for walking robots. Insects are able to effectively walk despite having small metabolisms and size, and understanding the neural mechanisms which govern their walking could prove useful for improving legged robots. In order to explore the possible neural systems responsible for inter-leg coordination, leg positional data for walking fruit flies of the species Drosophila melanogaster was recorded, where one individual leg was amputated at the base of the tibia. These experiments have shown that when amputated, the remaining stump oscillates in a speed-dependent manner. At low walking speeds there is a wide range of possible stump periods, and this variance collapses down to a minimum as walking speed increases. We believe this behavior can be explained by noisy pattern generation networks (CPGs) within the legs, with intra-leg load feedback and inter-leg global signals stabilizing the network. In this paper, this biological data will be analyzed so that a simplified neuromechanical model can be designed in order to explain this behavior. 

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5. Central pattern generator with inertial feedback for stable locomotion and climbing in unstructured terrain.

   Guillaume Sartoretti, Samuel Shaw (May 2018, IEEE International Conference on Robotics and Automation)

   : Inspired by the locomotor nervous system of vertebrates, central pattern generator (CPG) models can be used to design gaits for articulated robots, such as crawling, swimming or legged robots. Incorporating sensory feedback for gait adaptation in these models can improve the locomotive performance of such robots in challenging terrain. However, many CPG models to date have been developed exclusively for open-loop gait generation for traversing level terrain. In this paper, we present a novel approach for incorporating inertial feedback into the CPG framework for the control of body posture during legged locomotion on steep, unstructured terrain. That is, we adapt the limit cycle of each leg of the robot with time to simultaneously produce locomotion and body posture control. We experimentally validate our approach on a hexapod robot, locomoting in a variety of steep, challenging terrains (grass, rocky slide, stairs). We show how our approach can be used to level the robot's body, allowing it to locomote at a relatively constant speed, even as terrain steepness and complexity prevents the use of an open-loop control strategy. 

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