3D-Printing and Machine Learning Control of Soft Ionic Polymer-Metal Composite Actuators

Abstract

This paper presents a new manufacturing and control paradigm for developing soft ionic polymer-metal composite (IPMC) actuators for soft robotics applications. First, an additive manufacturing method that exploits the fused-filament (3D printing) process is described to overcome challenges with existing methods of creating custom-shaped IPMC actuators. By working with ionomeric precursor material, the 3D-printing process enables the creation of 3D monolithic IPMC devices where ultimately integrated sensors and actuators can be achieved. Second, Bayesian optimization is used as a learning-based control approach to help mitigate complex time-varying dynamic effects in 3D-printed actuators. This approach overcomes the challenges with existing methods where complex models or continuous sensor feedback are needed. The manufacturing and control paradigm is applied to create and control the behavior of example actuators, and subsequently the actuator components are combined to create an example modular reconfigurable IPMC soft crawling robot to demonstrate feasibility. Two hypotheses related to the effectiveness of the machine-learning process are tested. Results show enhancement of actuator performance through machine learning, and the proof-of-concepts can be leveraged for continued advancement of more complex IPMC devices. Emerging challenges are also highlighted.

Challenges and Perspectives in Control of Ionic Polymer-Metal Composite (IPMC) Actuators: A Survey

“…Furthermore, there are methods, like RC, which are more suitable for repetitive motions of the IPMC actuator, improving the performance from one operating cycle to the next. Such technique can minimize the requirements for sensor feedback, relying only on periodic measurements of cumulative performance (like distance traveled by a walking robot or fluid pumped) rather than on continuous monitoring of a system output (like actuator displacement) [71]. Note, however, that non-strictly repetitive factors (such as iteration-varying reference trajectory, system parameters or disturbances) can limit the application of the RC.…”

Developing next generation ionic polymer–metal composite materials: perspectives for enabling robotics and biomimetics

“…While developing the additive manufacturing of the Nafion® material, some initial work on the machine learning control of IPMCs was developed which utilized Bayesian optimization to overcome the inherent difficulties in controlling these materials. 16 This is a very exciting prospect for future studies. Machine learning and artificial intelligence may offer tremendous benefits for controlling and modeling IPMC actuators as well as analyzing the complex sensing signals generated by the multimodal deformation of IPMCs.…”

“…These precursor materials have been critical for the additive manufacturing of ionomers, which has paved the way for the fabrication of novel IPMC geometries. 2,16 Related to the thermal properties of ionomers are the shape-memory effects exhibited in Nafion® based IPMCs. 17 The combination of novel geometry, unlocked via additive manufacturing, with ionomeric materials optimized for thermal performance and shape-memory will undoubtedly be one of the next major areas of research for IPMCs (Fig.…”

3D printing and spine surgery

“…Emerging technologies such as machine learning and artificial intelligence may help to reduce technical assistance as well as provide a faster production process. 48…”