High Dimensional Modeling and Motion Control Technology for Soft Robotic
TL;DR
This paper analyzes in detail the principles, advantages and limitations of four core control methods based on motion models, machine learning, morphological computation and sensor feedback, and point out the key technical difficulties such as underactuated system dynamics, nonlinear hysteresis effect and flexible sensor integration.
Abstract
Soft robots made of low Young's modulus flexible materials have advantages over traditional rigid robots in terms of adaptability and safety in unstructured environments, and show great application potential in fields such as industrial grasping, medical assistance, and special operations. However, the inherent nonlinearity, viscoelasticity, and infinite degrees of freedom of soft robots make precise control extremely challenging, becoming a major obstacle to their practical application. This paper reviews the mainstream control strategies in the field of soft robotics, analyzes in detail the principles, advantages and limitations of four core control methods based on motion models, machine learning, morphological computation and sensor feedback, and point out the key technical difficulties such as underactuated system dynamics, nonlinear hysteresis effect and flexible sensor integration. The single control method is insufficient to fully address existing challenges. In the future, soft robot control will develop towards a hybrid strategy that integrates physical modeling accuracy, machine learning data processing capabilities, and passive adaptation through morphological computation.