Jiaqi Suo’s research while affiliated with Purdue University and other places

Shape‐morphing devices, a crucial branch in soft robotics, hold significant application value in areas like human–machine interfaces, biomimetic robotics, and tools for biological systems. To achieve 3D programmable shape morphing (PSM), the deployment of array‐based actuators is essential. However, a critical knowledge gap in 3D PSM is controlling the complex systems formed by these soft actuator arrays to mimic the morphology of the target shapes. This study, for the first time, represents the configuration of shape‐morphing devices using point cloud data and employing deep learning to map these configurations to control inputs. Shape Morphing Net (SMNet), a method that realizes the regression from point cloud to high‐dimensional control input vectors, is proposed. It has been applied to 3D PSM devices with three different actuator mechanisms, demonstrating its universal applicability to inversely reproduce the target shapes. Further, applied to previous 2D PSM devices, SMNet significantly enhances control precision from 82.23% to 97.68%. In the demonstrations of morphology mimicking, 3D PSM devices successfully replicate arbitrary target shapes obtained either through 3D scanning of physical objects or via 3D modeling software. The results show that within the deformable range of 3D PSM devices, accurate reproduction of the desired shapes is achievable.

Programmable surfaces (PSs) consist of a 2D array of actuators that can deform in the third dimension, providing the ability to create continuous 3D profiles. Discrete PSs can be realized using an array of independent solid linear actuators. Continuous PSs consist of actuators that are mechanically coupled, providing deformation states that are more similar to real surfaces with reduced complexity of the control electronics. However, continuous PSs have been limited in size by the lack of the control systems required to take into account the complex internal coupling between actuators in the array. In this work, we computationally explore the deformation of a fully continuous PS with 81 independent actuation pixels based on ionic bending actuator. We establish a control strategy using machine learning (ML) regression models. Both forward and inverse control are achieved based on the training datasets which are derived from the finite element analysis (FEA) data of our PS. The prediction of surface deformation achieved by forward control with accuracy under 1\% is 15000 times faster than FEM. And the real-time inverse control of continuous PSs that is to reproduce any arbitrary pre-defined surfaces, which possess high practical value for tactile display or human-machine interactive devices, is first proposed in the paper.

Nowadays, more and more researchers are pursuing miniaturized and lightweight structure of robots, However, robots with multiple actuators require large control systems if each actuator needs to be controlled independently. In addition, the cables and circuits for control and power supply are the obstacles in reducing size and weight. In this paper, a wireless multiplexing control system based on magnetic coupling resonance (MCR) is proposed. The control system can realize wireless energy transmission and control simultaneously. By decomposing a composite signal, it can control multiple actuators with only one input signal. However, in previous researches, their applications are primary and simple due to the switch control without feedback and the lack of systematic design method for robot application. Thus, based on the discrete form of composite signal, the closed-loop of wireless multiplexing control is presented, which makes this promising method a step closer to the practical application. Besides, based on the theoretical model of load power and transmission efficiency, 5 parameters to be optimized are extracted in accordance with the actual design requirements. The optimization algorithm for load power is proposed using particle swarm optimization (PSO). As for its applications in robots, a Delta robot with flexible linkage and an untethered multi-drive pipe robot for sampling operation are designed to demonstrate the proposed control method. The experiment results of the Delta robot show the reliability and accuracy of the system while the results of the pipe robot prove its potential use in the untethered robot system.