Reprogrammable, intelligent soft origami LEGO coupling actuation, computation, and sensing

Abstract

Tightly integrating actuation, computation, and sensing in soft materials allows soft robots to respond autonomously to their environments. However, fusing these capabilities within a single soft module in an efficient, programmable, and compatible way is still a significant challenge. Here, we introduce a strategy for integrating actuation, computation, and sensing capabilities in soft origami. Unified and plug-and-play soft origami modules can be reconfigured into diverse morphologies with specific functions or reprogrammed into a variety of soft logic circuits, similar to LEGO bricks. We built an untethered autonomous soft turtle that is able to sense stimuli, store data, process information, and perform swimming movements. The function multiplexing and signal compatibility of the origami minimize the number of soft devices, thereby reducing the complexity and redundancy of soft robots. Moreover, this origami also exhibits strong damage resistance and high durability. We envision that this work will offer an effective way to readily create on-demand soft robots that can operate in unknown environments.

Graphical abstract

Figure 1

Reprogrammable intelligent soft origami (ReISO) with embedded physical intelligence (A and B) The clockwise Kresling origami is in unfolded (A) and folded (B) states. The red dashed lines represent the creases. The red arrows indicate the folding direction of the Kresling origami. (C and D) Schematic illustration of the clockwise ReISO in unfolded (C) and folded (D) states. The grooves in the sides denote the creases of the soft origami. (E and F) The pressure response of the ReISO when twisting stimuli (E) and pressing stimuli (F) are applied to it. (G) The ReISO is equivalent to a NOT logic gate. The bottom figure is the truth table of the soft NOT logic gate. A, S, and Q are the input port, source port, and output port, respectively. The green block represents the vacuum pressure. (H) The pressure response of the ReISO. The S port is connected to a constant vacuum pressure of −80 kPa. The atmospheric pressure is defined as fluidic signal 0, and the vacuum pressure is defined as fluidic signal 1. (I and J) The intelligent tube is in straight (I) and kinked (J) states. (K) An autonomous soft turtle built solely with ReISOs is able to sense, think, and move. The signal transmission between soft sensors, logic circuits, and actuators is enabled through fluidic signals. (L) The soft control system of the turtle. The modules in the pinkish-purple boxes are multiplexed as 2 functional components.

Figure 2

The logic performances of the ReISOs (A) The kinking characterization of elastomer tubes with different diameters. The numbers in the legend represent the internal and external diameters of the elastomer tubes. The unkinked length of these tubes is 20 mm. (B) The kinking characterization of elastomer tubes with different lengths. (C) The influence of the pretwisted angle of the elastomer tubes on the kinking characterization. (D) The relationship between the pressure at the output port Q and the pressure at the input port A. (E) The relationship between the kinking pressure and the pressure of the source port S. (F and G) The equivalent fluidic circuit of the ReISO in the unfolded (F) and folded (G) states. (H) The influence of the capillary tube length on the pressure response of the ReISOs. (I) The relationship between the capillary tube length and the response time of the ReISOs. (J) The pressure response of the ReISO after it is pricked with a needle. The numbers in the legend represent the number of needle pricks. (K–M) The damage-resistance principle of the ReISOs. The chamber of the ReISO is connected to the atmospheric pressure (K), positive pressure (L), and vacuum pressure (M), respectively. The red line represents the hole pricked by a needle. The orange and blue lines represent tension forces and compression forces, respectively. The error bars in this figure are calculated based on three tests.

Figure 3

Reprogrammable morphologies of the ReISOs (A) Two ReISOs in the same actuation state form a contraction combination. (B) Two ReISOs in the opposite actuation states form a twisting combination. (C) An outward radial movement is realized by fixing 3 ReISOs in the same actuation state at the center of a circle. (D) An inward radial movement is achieved by fixing 6 ReISOs to a polygon that encircles them and actuating them simultaneously. (E) A bidirectional bending combination is enabled by assembling 2 contraction combinations in the opposite actuation states. (F) A soft rod-climbing robot constructed with the ReISOs. (G) A soft manipulator constructed with the ReISOs.

Figure 4

Reprogrammable soft logic circuits of ReISOs (A–C) The soft combinatorial logic circuits. (A-i) The logic symbol and Boolean expression of the Buffer gate. (A-ii) The schematic of the soft Buffer gate circuit. (A-iii) The pressure traces of the soft Buffer gate. (A-iv) The truth table of the Buffer gate. (A-v) The experimental image of the soft Buffer gate. (B) The NAND gate. (C) The NOR gate. (D and E) The soft sequential logic circuits. (D) The soft ring oscillator circuit. (E) The soft SR latch circuit. (F and G) The soft functional circuits constructed with the above fundamental logic circuits. (F) The soft full adder circuit. (G) The soft frequency divider circuit. The green block represents the vacuum pressure.

Figure 5

Reconfigurable soft turtle with built-in intelligence (A) Schematic illustration of the soft turtle. (B) The actuation system of the soft turtle. (C) The soft control circuit of the robotic turtle is a ring oscillator with 3 NOT gates. The ReISOs in the red area function as actuators and logic gates simultaneously. (D) The locomotion principle of the soft turtle. The curves are the pressure and twisting angle variations of the soft leg. (E) A sequence of images of the untethered soft turtle swimming forward in a tank. (F–I) The schematic actuation system of the soft turtle when it swims forward (F), backward (G), CW (H), and ACW (I). These motion modes can be readily achieved by reconfiguring ReISOs.

Figure 6

An untethered and autonomous soft turtle that is able to sense stimuli, store data, process signals, and perform swimming movements (A) Schematic illustration of the soft turtle. (B) The soft control system of the robotic turtle is composed of 2 ring oscillator circuits and an SR latch circuit. The ReISOs in the green area function as 2 components simultaneously. (C) The soft turtle was controlled and actuated with 12 ReISOs. The ReISOs with the same color belong to the same subcircuit. (D) The soft turtle switched to forward movement gaits after sensing a CW twisting stimulus. The SR latch circuit detected this stimulus and stored the current state in the circuit even if the stimulus was removed. The ring oscillators then converted the constant pressure from the output ports of the SR latch to oscillatory pressures, which were used to actuate the legs to swing in the water. The bottom figures are detailed information on the soft control system and are clearly depicted in Figure S17. (E) The pressure traces of the soft turtle. Q and $\overline{\mathrm{Q}}$ represent the output pressure of the SR latch circuit. Ring-1 and Ring-2 denote the output pressures of ring oscillator 1 and ring oscillator 2. (F) The soft turtle switched to backward movement gaits after sensing an ACW twisting stimulus.