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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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Modular Controllers Facilitate the Co-Optimization of Morphology and Control in Soft Robots
Soft robotics is a rapidly growing area of robotics research that would benefit greatly from design automation, given the challenges of manually engineering complex, compliant, and generally non-intuitive robot body plans and behaviors. It has been suggested that a major hurdle currently limiting soft robot brain-body co-optimization is the fragile specialization between a robot's controller and the particular body plan it controls, resulting in premature convergence. Here we posit that modular controllers are more robust to changes to a robot's body plan. We demonstrate a decreased reduction in locomotion performance after morphological mutations to soft robots with modular controllers, relative to those with similar global controllers - leading to fitter offspring. Moreover, we show that the increased transferability of modular controllers to similar body plans enables more effective brain-body co-optimization of soft robots, resulting in an increased rate of positive morphological mutations and higher overall performance of evolved robots. We hope that this work helps provide specific methods to improve soft robot design automation in this particular setting, while also providing evidence to support our understanding of the challenges of brain-body co-optimization more generally.
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- Award ID(s):
- 2008413
- PAR ID:
- 10458459
- Date Published:
- Journal Name:
- Proceedings of the Genetic and Evolutionary Computation Conference
- Page Range / eLocation ID:
- 174 to 183
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3583131.3590416
