Advanced Design of Soft Robots with Artificial Intelligence

Abstract

1. A comprehensive review focused on the whole systems of the soft robotics with artificial intelligence, which can feel, think, react and interact with humans, is presented.

2. The design strategies concerning about various aspects of the soft robotics, like component materials, device structures, prepared technologies, integrated method, and potential applications, are summarized.

3. A broad outlook on the future considerations for the soft robots is proposed.

Keywords: Artificial intelligence; Design tactics; Review and perspective; Soft robotic systems.

Fig. 1

Overview of the soft robots equipped with artificial intelligence (AI)

Fig. 2

a Publication focused on the soft robotics with AI. b The citation frequency of the papers concerning about the intelligent soft robots in each year

Fig. 3

Design tactics related to the soft robots with AI

Fig. 4

Schematic illustration for the working systems of the intelligent soft robots

Fig. 5

Schematic illustration for the working mechanism of sensors

Fig. 6

Schematic illustration for the basic model of machine learning in the intelligent soft robotic system

Fig. 7

Schematic illustration for the mechanism of actuation in the intelligent soft robotic system

Fig. 8

a Schematic illustration of meta-learning and rapid adaptation to new tasks for human. b Illustration for the formation of the artificial sensory intelligence system. c Graphical illustrations a variety of human hand motions. d Schematic illustration of the flow of information. e Schematic diagram of how to measure the epicentral motions of fingers. f Magnified image of the sensor on skin. g Illustration of the sensory system. h Structure and i the application of the e-skins with a piezoelectric layer and a strain layer. j Performance about the manipulator with single mold. k Preparation process of the piezoresistive tactile sensor composed of CB and PU. l Mechanism and m response time of the sensor. n Schematic diagram of how to measure the object at a distance. o Schematic of the pMUT array, and p the cross-sectional view of a pMUT unit. q Signals for the object with the distance of 98 mm. r Schematic diagram of the M-Bot. s Inkjet printing and automatic cutting used for the fabrication of e-skin-R. t Schematic signal flow diagram in the system of M-Bot

Fig. 9

Illustration of a one step of data self-augmentation, b data self-augmentation, with a sixfold increase in initially collected data and c DNN architecture. d Schematics of how to construct the GRU-CNN. e Flow diagram of the machine learning process with the node-oriented decision tree algorithm selected. f Architecture of the network for the (i) feature-level and (ii) score-level fusion. g Process and parameters of the GRU-CNN

Fig. 10

a Schematic diagram of the T-TENG sensor, and its working mechanism for (i) contact position detection, (ii) sliding detection, and (iii) contact mode recognition. b Schematic diagram of the L-TENG sensor, and its working mechanism. c Enhanced object recognition via machine learning, and d the confusion map for ML. e The soft robot equipped with the multimodal perception system

Fig. 11

a Photographs of the process illustrating how the fresh orange was sorted by a manipulator, and the resistance and voltage output obtained from the sensors. b Photographs of the process illustrating how the rotten orange was sorted by a manipulator, and the corresponding signals from the sensors. c Flow diagram illustrating how the soft robot worked in digital twin system. d Object prediction effect provided by this system, and e the system interface for object recognition and digital twin warehouse application

Fig. 12

a Illustration for the multifunctionality of the wetting ferrofluid droplets. b Illustration for the partitioned plantar. c and d Different perspectives for demonstrating the therapeutic insole. e Schematic diagram for illustrating the working process of this active insole. f Closed-loop regulation, and g signal changes of patient walking at different frequencies. h Schematic diagram and i picture of the controlling system. j Brief scheme of electrochemical platform with the assistant of ML for bacteria detection. k Combination of tactile sensors with a large area on a medical assistive robot

Fig. 13

a Demonstration of grasping tofu and tomato using a manipulator. b Ultrasonic sensor integrated on a soft robot for object profile detection, and c its performance for different topographic profiles. d Photographs of the robot prototype, and e illustration of the control components. f Photographs to verify the robot to conduct transportation tasks. g Schematic diagram for the multimode haptic sensing and feedback

Fig. 14

a Output comparison between the piezoelectric mode and strain mode. b Schematics illustration of the fast-response soft actuator. c Temperature–time relationship with water at different temperatures and natural condition. d Comparison of durations required to reduce the temperature. e Demonstration of the high load and shape adaptive ability

Fig. 15

a Schematic diagram of the magnetic soft gripper, and b the change in the lateral dimension in the whole procedure. c Schematic diagram of knitted soft robotics with build-in textile-integrated multimodal sensors

Fig. 16

a Blooming and color-shifting of flower made from CASA. b Fluttering and color-shifting of butterfly wings fabricated from CASA with IR thermal images. c Twining and color-shifting of an artificial tendril. d The key features of octopus vulgaris arm. e Schematic illustration of an AOS during actuation. f Photograph of the AOS before and after actuation. g Schematic of the AOS with soft and rigid bodies. h Schematic of the AOS integrated with MWCNT strain sensors (E-AOS). i Schematic of the E-AOS assisted with machine learning

Fig. 17

a Schematic illustration of the soft robot with a bimodal self-powered flexible sensor. b Structure of the sensor and the soft robotic hand integrated with the sensor to achieve material and surface roughness recognition. c Operating mechanism of BHSS composed of both piezoelectric and triboelectric units, and d the equivalent circuit diagram of the BHSS. e Gesture recognition assisted by the Long Short-Term Memory (LSTM). f Schematic illustration of the self-powered flexible neural tactile sensor. g Schematic illustration of the multifunctional e-skins working as human–machine interaction interfaces without external power source. h Schematic diagram of the soft gripper, and i the bending and the unfolding state of the gripper. j Illustration of how to lift a load of 500 g with zero power consumption. k Photograph of sensing the temperatures of the eggs for selecting the hot egg to be grasped, and l the progress of gripping and lifting the selected egg

Fig. 18

Construction of the soft gripper, including a TENG sensors with (i) L-TENG sensor and (ii) T-TENG sensor, b the soft gripper, and c the intelligent sensory data processing strategies. d Digital twin applications of this system enabled by the AI. e (i) The structure of the sensor-integrated smart soft robot. The structures and functionalities of (ii) L-TENG sensor, (iii) T-TENG sensor and iv) PVDF temperature sensor. (v) Various applications realized by this smart system. f Illustrations of how these corresponding functions are achieved by humans. g Framework of digital twin-based industrial cloud robotics. h Schematics of the intelligent soft glove for immersive communications. i Schematics illustration of the STH device, and j schematic illustration of the bi-functionality provided by this device. k Results of the numerical simulation and experimental validation for determining the optimum Cu serpentine electrode specification. l Photograph of the optimized serpentine Cu electrode on Ecoflex