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Soft robots, valued for their compliance and deformable nature, have demonstrated their outstanding abilities in complex environments. However, the nonlinear dynamics make it challenging to derive efficient locomotion patterns from analytical methods. This is largely due to the high computational cost associated with simulating soft-bodied models. Conversely, rigid-body models, such as those used in Gazebo, offer computational efficiency but cannot directly represent soft robots. We address these challenges by introducing customized Gazebo plugins that enable the simulation and analysis of soft robot locomotion dynamics. These plugins are complemented by a novel JointStiffnessPlugin, integrated with ROS services, for fine-tuning effort-controlled parameters. The system identification process is followed to match the simulation dynamics with the real soft robot to minimize the sim-to-real gap. Utilizing the proposed simulation framework and Bayesian Optimization, we derived a body-induced locomotion strategy that achieves enhanced efficiency. This strategy, relying solely on periodic spine bending and robot pose for forward propulsion, demonstrably reduces energy consumption compared to conventional gaits. Experimental results confirm a 42 % energy expenditure reduction relative to four-legged crawling. 

Fruit flies or Drosophila larvae exhibit a diverse range of locomotion gaits enabled by their soft, segmented bodies and intricate muscle arrangements. Their bodies, composed of multiple segments, are synchronously activated to propel forward through a combination of muscle elongation and contraction. Soft robotic systems, inspired by such biological marvels, face significant challenges in replicating these complex behaviors due to the intricate interplay between muscle activation, soft body dynamics, and frictional forces. To address these challenges, we propose a reduced-order model that captures the essential features of larval crawling. By modeling segments as a combination of prismatic and revolute joints, we can simulate the nonlinear motion resulting from muscle activation and body deformation. Our model demonstrates the potential of this approach to accurately describe larval movement, as validated by comparisons with actual larval trajectories. This research offers valuable insights into the design and control of soft robots and provides a framework for biologists to investigate the complex mechanisms of neuromuscular coordination in larvae.