Analyze the Environmental Perception and Ontological Perception of Soft Robots

As the application of soft robots in complex environments continues to expand, their precise perception and their own state has become a key foundation for achieving safe, autonomous, and intelligent behaviors. This paper aims to systematically summarize the basic principles of environmental and ontological perception in soft robots, as well as the current development statecologicaly used sensor types and , such as pressure and tactile sensing, have been relatively mature and have demonstrated good application potential in fields like oceanography and healthcare. In contrast, ontological perception still faces challenges such as structural nonlinearity and complex deformation. In recent years, the development of flexible electrical, optical, and liquid metal sensors has provided feasible solutions for shape and curvature perception. Meanwhile, machine learning and multimodal data fusion technologies have effectively improved perception accuracy and system safety. The analysis provides a reference for the design and optimization of soft robot perception systems.