Four-dimensional printing (4DP) is a newly emerged technology that uses smart materials for additive manufacturing and thus enables shape and/or property change upon stimulus after the printing process. Present study on 4DP has been focused on open loop stimulus, which can hardly ensure high shape precision and predictable final states. In this paper, a new closed loop 4DP (CL4DP) process supplementing 4D printed actuation with closed loop control methods is proposed. Image feedback is used for enhancing the conventional open loop 4DP morphing process and a controller is implemented to regulate the intensity of the stimulus accordingly in real-time. To achieve precise control, a nonlinear affine system model is built by model identification with measurement data to describe the dynamic shape recovery process of the 4D printed Shape Memory Polymer (SMP). Precise shape control is achieved and the effects of controller parameters on the precision of CL4DP are studied. Traditionally, SMP has a discrete number of selected steady states. With CL4DP, such steady states can be continuous and arbitrary.

4D printing technology, as a new generation of Additive Manufacturing methods, enables printed objectsto further change their shapes or other properties upon external stimuli. One main category of 4D printingresearch is 4D printed thermal Shape Memory Polymer (SMP). Its morphing process has large time delay, isnonlinear time variant, and susceptible to unpredictable disturbances. Reaching an arbitrary position with highprecision is an active research question. This paper applies the Reinforcement Learning (RL) method to developan optimal control method to perform closed loop control of the SMP actuation. Precise and prompt shapemorphing is achieved compared with previous control methods using a PI controller. The training efforts of RLare further reduced by simplifying the optimal control policy using the structural property of the prior trainedresults. Customized protective visors against COVID-19 are fabricated using the proposed control method.

Combining 3D printing and smart materials, 4D printing technologies enable the printed actuators to furtherchange their shapes or other properties after prototyping. However, the shape morphing of 4D printed actuatorssuffers from poor controllability and low precision. One of the main challenges is that the 4D printed actuatorsare hard to be modeled and it is difficult to develop an appropriate controller for them. In this study, variouspopular reinforcement learning (RL) methods are applied to address the problem of online and adaptive model-free control of 4D printed shape memory polymer (SMP). Their training efficiencies are compared and anadaptive LQR controller based on Q learning is developed to realize efficient online learning. The RL controllerachieves precise and quick shape control within 2 − −3 learning episodes and is adaptive to the changingproperties of SMP. The RL controller performance is then compared with a model-based LQR controller andshows high control precision and excellent adaptability to the varying control plant.

The COVID-19 pandemic makes protective visors important for protecting people in close contacts. However, the production of visors cannot be increased greatly in a short time, especially at the beginning of the pandemic. The 3D printing community contributed largely in fabricating the visor frames using the rapid and adaptive manufacturing ability. While there are many open source designs of face visors for affordable 3D printers, all these designs fabricate mono-sized frames without considering diverse users’ dimensions. Here, a new method of visor post-processing technology enabled by closed loop controlled 4D printing is proposed. The new process can further deform the printed visor to any customized size for a more comfortable user experience. FEM analysis of the customized visor also shows consistent wearing experience in different circumstances compared with the old visor design. The fabrication precision and time cost of the method is studied experimentally. A case study regarding the reducing, reusing and recycling (3R) of customized visors in classrooms is proposed to enable the customized visors manufactured in a more sustainable way.

Quadruped robots have the advantages of traversing complex terrains that are difficult for wheeled robots. Most of the reported quadruped robots are built by rigid parts. This paper proposes a new design of quadruped robots using soft actuators driven by tendons as the four legs. Compared to the rigid robots, the proposed soft quadruped robot has inherent safety, less weight and simpler mechanism for fabrication and control, but the corresponding challenge is that the accurate mathematical model applicable to model-based control design of the soft robot is difficult to derive by dynamics. To synthesize the optimal gait controller of the soft-legged robot, the paper makes the following contributions. First, the flexible components of the quadruped robot are modeled with different finite element and lumped parameter methods. The model accuracy and computation efficiency are analyzed. Second, soft actor–critic methods and curriculum learning are applied to learn the optimal gaits for different walking tasks. Third, The learned gaits are implemented in an in-house robot to transport hand tools. Preliminary results show that the robot can walk forward and correct the walking directions.

Using soft actuators as legs, soft quadruped robots have shown great potential in traversing unstructured and complex terrains and environments. However, unlike rigid robots whose gaits can be generated using foot pattern design and kinematic model of the rigid legs, the gait generation of soft quadruped robots remains challenging due to the high DoFs of the soft actuators and the uncertain deformations during their contact with the ground. This study is based on a quadruped robot using four Compressible Tendon-driven Soft Actuators (CTSAs) as the legs, with the actuator's compression motion being utilized to improve the walking performance of the robot. For the gait design, an inverse kinematics model considering the compression of the CTSA is developed and validated in simulation. Based on this model, walking gaits realizing different motion speeds and directions are generated. Closed loop direction and speed controllers are developed for increasing the robustness and precision of the robot walking. Simulation and experimental results show that omnidirectional locomotion and complex walking tasks can be realized by tuning the gait parameters and the motions are resistant to external disturbances.

Soft robots, made of soft materials such as di-electric elastomer or shape memory polymers, have receivedtremendous attentions due to its dexterousness, flexibility andsafety compared with rigid robots. However, wider applicationof soft robots is limited due to their complex fabrication processand poor controllability. Here, we introduce a closed loopcontrolled soft actuator that is fully 3D printed with flexiblematerial. The structure of the soft actuator is optimized withFinite Element Method (FEM) to acquire shortest fabricationtime and highest deformation for same stimulus input. A desk-top Fused Deposition Modeling (FDM) 3D printer is used forlow-cost fabrication of such actuators. A webcamera is used forthe image feedback which offers the real time shape monitoringof the soft actuator. An output feedback Proportional IntegralDerivative (PID) controller with lowpass filter is developed withpole placement design method based on a data-driven modelof the 3D printed soft actuator. The controller is implementedto regulate the input air pressure to ensure a fast-response, precise and robust shape changing for any work environments.