Vertical farming, a sustainable key for urban agriculture, has garnered attention for its land use optimization and enhanced food production capabilities. The adoption of automation in vertical farming is a pivotal response to labor shortages, addressing the need for increased efficiency, particularly in labor-intensive tasks like harvesting. Although soft robotic grippers offer a significant promise for delicately handling fragile objects, the absence of sensors has hindered their full potential to execute precise and secure grasping. To address this challenge, we present a new solution: a detachable Fiber Bragg Grating-based flexible contact force sensor to capture gripper-vegetable interactions. The sensing module was 3D printed using soft material, and the FBG fiber was attached to the module using epoxy. From evaluation tests, this lightweight sensor demonstrated a wide measurement range of up to 9.87 N, with a high sensitivity of 141.7 pm/N, good repeatability, and a hysteresis of 7.96%. Compared to commercial load cells, our sensor achieves a small measurement RMSE of 0.41 N and a percentage error of 4.15%. The sensor was integrated into two robotic 3D-printed soft grippers to enable real-time monitoring of dynamic contact force during vegetable harvesting in vertical farming scenarios. By reflecting contact status, this sensor provides a promising glimpse into the future of agricultural automation, enhancing operational efficiency and strengthening situation awareness and decision-making capabilities in vertical farms. Beyond agriculture, the versatility of this sensor extends to application in areas such as warehousing, logistics, and the food and beverage industry.

Emerging additive manufacturing (AM) technologies, specifically additively manufactured electronics (AME), 4-D printing, and origami, are reshaping the design capabilities and functionalities of contemporary electronic devices. Cutting-edge 3-D/4-D printing technologies facilitate the prototyping and realization of complex electronic functions that are challenging to conventional methods. This article provides a comprehensive overview of the evolving techniques in AME, 4-D printing, and origami, employing multimaterials (conductive and dielectric materials) and shape-memory materials (SMMs) to fabricate functional electronic components and devices. Additionally, the overview delves into the state-of-the-art AME and 4-D-printed electronic components across diverse fields, including biomedical electronics, space engineering, and the advancements in the next-generation wireless communications and sensing.

This work demonstrates 3D printed soft actuators with complex shapes and remote actuation using an external magnetic field. Instead of embedding magnetic particles in a polymeric matrix, we fabricated a novel ferrofluid-based actuator, in which the fluid can be moved to different locations in the actuator to affect actuator response. We studied the effect of both the ferrofluid and the 3D printed material on the motion of simple actuators using 3D printed tubes. In addition, we 3D printed more complex actuators mimicking a human hand and a worm to demonstrate more complex motion.

By bonding micron-thin polymer layers with an electrically conductive layer, we can utilize the bi-metal effect to create a soft, thin, electrically-activated thermal actuator (ETA), which can be used in printed soft robots. Using ETAs have shown quite remarkable potential in previous works. However, to reveal their true capabilities, we propose to utilize a multi-layered ETA. In this research, we analytically model the actuator for both the bi-, and more importantly, tri-layer ETAs and analyze their behavior. We develop a simple intuitive model to better explain the benefits of the tri-layer actuation and to estimate the optimal design parameters. We verify the models by conducting an experiment to measure the curvature change for different third layer thicknesses. The experiments show a significant improvement in performance of the tri-layer actuators and display a good fit between the simplified analytic model and the full model.