Modeling Soft Robots

This chapter explores the evolving landscape of soft robotic kinematic and dynamic modeling, organized into four distinct sections to encompass a broad spectrum of methodologies. The first section investigate into Continuous Curvature Models, addressing the challenges associated with the inherently continuous and deformable nature of soft robots. Various approaches within continuum mechanics and finite element analysis are discussed, highlighting the complexities involved in capturing the intricate motion and shape changes exhibited by these systems. The second section focuses on Lumped Parametric Models, providing insights into techniques that discretize soft robots into simpler, interconnected elements. This section explores the advantages and limitations of such models, emphasizing their efficacy in simulating the dynamic behavior of soft robots with reduced computational complexity. The third section introduces Hybrid Models, which amalgamate the strengths of continuous curvature and lumped parametric models. This approach seeks to strike a balance between accuracy and computational efficiency, offering a versatile framework for modeling soft robotic systems in various applications. The fourth section explores Learning-Based Models, a burgeoning field leveraging machine learning and data-driven approaches to model the complex kinematics and dynamics of soft robots. The chapter provides an overview of neural networks, reinforcement learning, and other learning-based techniques, showcasing their potential in capturing intricate soft robotic behaviors and adapting to real-world scenarios. The chapter concludes by addressing the critical question of “How to Select Suitable Models” for soft robotic applications. It offers guidance on the criteria for model selection, taking into account factors such as system complexity, computational efficiency, and the availability of training data. By providing a comprehensive overview of these modeling approaches, this chapter aims to equip researchers, engineers, and practitioners with a nuanced understanding of the diverse methodologies available for soft robotic kinematic and dynamic modeling, paving the way for advancements in the field.