Engineering Commons^™

Design Optimization Of A Pneumatic Soft Robotic Actuator Using Model-Based Optimization And Deep Reinforcement Learning, Mahsa Raeisinezhad, Nicholas Pagliocca, Behrad Koohbor, Mitja Trkov

Henry M. Rowan College of Engineering Faculty Scholarship

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 …