Design Optimization of a Pneumatic Soft Robotic Actuator Using Model-Based Optimization and Deep Reinforcement Learning
Frontiers in Robotics and AI
We present two frameworks for design optimization of a multi-chamber pneumatic-driven soft actuator to optimize its mechanical performance. The design goal is to achieve maximal horizontal motion of the top surface of the actuator with a minimum effect on its vertical motion. The parametric shape and layout of air chambers are optimized individually with the firefly algorithm and a deep reinforcement learning approach using both a model-based formulation and finite element analysis. The presented modeling approach extends the analytical formulations for tapered and thickened cantilever beams connected in a structure with virtual spring elements. The deep reinforcement learning-based approach is combined with both the model- and finite element-based environments to fully explore the design space and for comparison and cross-validation purposes. The two-chamber soft actuator was specifically designed to be integrated as a modular element into a soft robotic pad system used for pressure injury prevention, where local control of planar displacements can be advantageous to mitigate the risk of pressure injuries and blisters by minimizing shear forces at the skin-pad contact. A comparison of the results shows that designs achieved using the deep reinforcement based approach best decouples the horizontal and vertical motions, while producing the necessary displacement for the intended application. The results from optimizations were compared computationally and experimentally to the empirically obtained design in the existing literature to validate the optimized design and methodology.
Raeisinezhad, M., Pagliocca, N., Koohbor, B., & Trkov, M. (2021). Design Optimization of a Pneumatic Soft Robotic Actuator Using Model-Based Optimization and Deep Reinforcement Learning. Frontiers in Robotics and AI 8, 10.3389/frobt.2021.639102.
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This article was supported by the Rowan University Libraries Open Access Publishing Fund.