Flexible Robot Research Articles

The Identification, Separation, and Clamp Function of an Intelligent Flexible Blueberry Picking Robot

Identifying fruit maturity accurately and achieving damage-free harvesting are challenges in designing blueberry-picking robots. This paper presents an intelligent flexible picking system. First, we trained a deep learning-based YOLOv8n network to locate the position of the fruit and determine fruit ripeness. We used a neural network to establish the relationship between fruit hardness and shape parameters, achieving an adaptive gripping force for different fruits. To address the issue of dense clusters in some blueberry varieties, we designed a fruit separation subsystem using a combination of flow field analysis and pressure-sensitive experiments. The results show that the mean average precision can reach 84.62%, the precision is 94.49%, the recall is 83.85%, the F1 score is 88.85%, and the test time is 0.12 s, which can meet the requirements for blueberry fruit recognition accuracy and speed. The spacing between closely packed fruits can increase by 4 mm, and the damage-free picking rate exceeds 92%, achieving stable, damage-free harvesting.

An Optimal Weighted Spectral Difference Method for Faulty Vibration Separation in Flexible Joint Robot

An Optimal Weighted Spectral Difference Method for Faulty Vibration Separation in Flexible Joint Robot

A data density-based measure of dexterity for continuum robots and its comparative study

Abstract Continuum robot-based surgical systems are becoming an effective tool for minimally invasive surgery. A flexible, dexterous, and compact robot structure is suitable for carrying out complex surgical operations. In this paper, we propose performance metrics for dexterity based on data density. Data density at a point in the workspace is higher if the number of reachable points is higher, with a unique configuration lying in a small square box around a point. The computation of these metrics is performed with forward kinematics using the Monte Carlo method and, hence, is computationally efficient. The data density at a particular point is a measure of dexterity at that point. In contrast, the dexterity distribution property index is a measure of how well dexterity is distributed across the workspace according to desired criteria. We compare the dexterity distribution property index across the workspace with the dexterity index based on the dexterous solid angle and manipulability-based approach. A comparative study reveals that the proposed method is simple and straightforward because it uses only the position of the reachable point as the input parameter. The method can quantify and compare the performance of different geometric designs of hyper-redundant and multisegment continuum robots based on dexterity.

Flexible Continuum Robot with Variable Stiffness, Shape‐Aware, and Self‐Heating Capabilities

Conventional continuum robots have outstanding flexibility and dexterity. However, when the robot needs to interact with the environment, the softness may affect the performance of the robot. Especially in transport tasks, the softness of continuum robots can lead to handling failures and drastic drops in precision. The variable stiffness continuum robot combines the advantages of flexibility and rigidity, which is conducive to expanding the application scenarios of flexible continuum robots. This article proposes a flexible continuum robot that simultaneously realizes variable stiffness, shape‐aware, and self‐heating functions using liquid metal. The low‐temperature phase transition property of liquid metal is utilized to realize the variable stiffness function; the overall stiffness of the robot can reach the range of 18.5–183 N m−1, which can realize a tenfold stiffness gain. The conductivity of liquid metal is utilized to develop the shape‐aware function, and the monitoring accuracy is within 5%. At the same time, this article utilizes the liquid metal's resistive thermal effect to realize heating function, so that the robot no longer needs heating systems such as heating wires and can realize the phase transition by energizing itself. Based on this design, the robot arm can realize the transition between maximum and minimum stiffness within 240 s.

Instantaneous Tiltmeter Triggered by Dynamic Wetting Behavior.

A novel instantaneous tiltmeter with dynamic and static monitoring functions is reported that is based on liquid metal dynamic wetting behavior in a bio-fabricated anisotropic microchannel. The proposed system achieves instantaneous tiltmeter functionality, offering a broad detection range (-90°-90°) with high precision (0.05°), a rapid reaction time (0.11 s), and enhanced durability. Moreover, a seamless integration has enabled water wave detection, language programming, and human limb monitoring. Especially, the integration of tiltmeter and a t3D motion platform results in a surface structure scanning system capable of effectively performing large area (>200 cm2) and height difference scanning functions. This innovative approach holds great potential for transformative changes in the fields of advanced manufacturing, flexible robotics, and the flexible sensing, further facilitating widespread adoption.

Correction to: Design and architecture of a slender and flexible underwater robot

Correction to: Design and architecture of a slender and flexible underwater robot

Utilizing Photovoltaic Solar Panels for Real‐Time Localization and Speed Detection of Approaching Illuminated Objects in Humanoid Robots

ABSTRACTIn the field of humanoid robotics, this paper showcases a promising method of integrating the photovoltaic (PV) solar panels into the “GUCnoid 1.0” humanoid robot model. Instead of the conventional power generation, solar cells are used to supply electrical energy to various sensors to create a system capable of real‐time sensing and perception. The main focus of the study will be the use of PV panels as receptors that are capable of detecting the light, enabling an adaptive system which perceives environmental changes. The behavior of the PV cell is superbly studied when various light sources are approached or depart. They finally reveal unique patterns in the voltage output signal amplitude. Interestingly, these patterns figure out the same symmetric structure, which reflects on a vertical axis by their mirroring. Using this simplicity, the method involves using an artificial neural network that is able to distinguish the light sources coming towards the detector and the ones running away and the rate at which they approach/recede. Outdoor experiment was organized for verification of methods. GUCnoid 1.0— humanoid robot was placed in front of a moving vehicle with different speeds of approach. To be able to identify the vehicle's position and velocity, a PV sensing technique has to be applied. This innovative technology will have wide applications, with much attention paid to improving the speed of finding nearby objects or vehicles in the scenarios where quick detection is a serious life safety issue. Through the process of PV solar panels' smart sensing, we directly connect the areas of high‐tech robotics and renewable energy. This discovery creates an opportunity for companies to build more responsive and flexible humanoid robots that can effectively collaborate with humans to achieve greater outcomes through more secure and efficient interactions between humans and robots.

AI robots pioneer the Smarter Inclusive Society

This paper outlines a project aimed at realizing a “Smarter Inclusive Society” by 2050 through the integration of AI robots into various public facilities. Led by the Cabinet Office’s “Moonshot Research and Development Program,” the project focuses on developing Adaptable AI-enabled Robots that enhance self-efficacy by supporting users’ abilities while maintaining their sense of independence. Key to the project is the Robotic Nimbus, a soft and flexible robot designed to provide tailored assistance while preserving user agency. The concept of “Adaptable AI-enabled Robots” is introduced to ensure versatility in accommodating user needs and preferences. In addition to physical assistance, the project emphasizes creating engaging experiences through activities like dance and sports, fostering excitement and inclusivity. Collaborations, such as the “Yes We Dance!” performance, demonstrate the potential of AI technology in enhancing rehabilitation opportunities and promoting social participation. By 2050, the project aims to establish a society where AI robots contribute to mental, physical, and social wellbeing, empowering individuals to engage in independent activities and fostering a vibrant, inclusive community. This paper is a compilation of articles/papers/presentations previously presented on the Moonshot Hirata project.

Robust Metallized Liquid Crystal Elastomer Fiber Arrays Toward a Machine Learning‐Assisted Artificial Neuromuscular System with Perceptual Function

AbstractEndowing artificial muscles with perceptual function, as an octopus does, is highly desired but still suffering from interfacing mismatch between actuating and sensing units in a thin fiber. Herein, an artificial neuromuscular fiber capable of electrically responding to external strain/temperature and actuation path with power supply is reported by using polymer‐assisted metal deposition to firmly coat Cu nanoparticles on the surface of liquid crystal elastomer (LCE). The LCE core acts as an actuator, while the wrinkled Cu sheath provides environment interaction and actuation monitoring. Benefiting from the levodopa/polyethyleneimine interface design, this fiber exhibits large reversible contraction (47.61%), fast strain rate (370% s−1), high output power density (663.75 W kg−1) and reliable durability (1000 cycles) under electrical stimulation. Furthermore, by combining as‐made fiber arrays with electronic system and computing algorithm, this machine learning‐assisted artificial neuromuscular system can actuate the Chinese shadow puppetry and recognize its body movements with high accuracy. This work paves a revolutionary way for fabricating next‐generation flexible robots.

Programmable Hydrogel-Based Soft Robotics via Encoded Building Block Design

Hydrogels have revolutionized the field of soft robotics with their ability to provide dynamic and programmable responses to different stimuli, enabling the fabrication of highly adaptable and flexible robots. This continual development holds significant promise for applications in biomedical devices, active implants, and sensors due to the biocompatibility of hydrogels. Actuation in hydrogel-based soft robotics relies on variations in material properties, structural design, or a combination of both to generate desired movements and behaviors. While such traditional approaches enable hydrogel actuation, they often rely on complex material design, bringing challenges to hydrogel fabrication and hindering practical use. Therefore, this work seeks to present a simplified and versatile approach for fabricating programmable single-component hydrogel-based soft robotics using an encoded building block design concept and 3D printing. A series of structural building blocks have been designed to achieve various actuation characteristics, including the direction, degree, and kinetics of actuation. By assembling these building blocks into various configurations, a broader range of actuation responses can be encoded, allowing for the fabrication of versatile, programmable soft robotics using a single uniform material through vat photopolymerization 3D printing. This approach enables adaptation to a wide range of applications, providing highly customizable encoding designs.

Hopping potential wells and gait switching in a fish-like robot with a bistable tail

Fish outperform current underwater robots in speed, agility, and efficiency of locomotion, in part due to their flexible appendages that are capable of rich combinations of modes of motion. In fish-like robots, actuating many different modes of oscillation of tails or fins can become a challenge. This paper presents a highly underactuated (with a single actuator) fish-like robot with a bistable tail that features a double-well elastic potential. Oscillations of such a tail depend on the frequency and amplitude of excitation, and tuning the frequency-amplitude can produce controllable oscillations in different modes leading to different gaits of the robot. This robot design is inspired by recent work on underactuated flexible swimming robots driven by a single rotor. The oscillations of the rotor can propel and steer the robot, but saturation of the rotor makes performing long turns challenging. This paper demonstrates that by adding geometric bistability to the flexible tail, turns can be performed by controllably exciting single-well oscillations in the tail, while exciting double-well oscillations of the tail produces average straight-line motion. The findings of this paper go beyond the application to a narrow class of fish-like robots. More broadly we have demonstrated the use of periodic excitation to produce bistable response that generate different gaits including a steering gait. The mechanics demonstrated here show the feasibility of applications to other mobile soft robots.

Mathematical modelling of a flexible steering robot for vehicle trajectory control

Abstract This paper presents a mathematical modelling framework for a novel design of a steering robot to control vehicle trajectory. A systematic approach is adopted to capture the dynamics of a belt transmission mechanical system of the robot. The steering robot is powered by a Brushless Direct Current (BLDC) servo motor which employs a Field Oriented Current (FOC) control for motor position control. The system’s accuracy is demonstrated through simulations conducted using MATLAB SIMULINK, showcasing its capability to effectively track the reference steering angle.

Modeling and motion analysis of flexible legged robots using the finite particle method

Robotics with flexible legs have attracted significant attention. Engineers often design and analyze the motion of legged robots from kinematic and biomimetic perspective. However, the influence of flexibility of the feet on robot locomotion is often not given sufficient considerations, which is also very crucial to the motion posture of the flexible legged robots, especially as the soft robots design becomes increasingly popular. The mainly difficulties lies in the traditional numerical methods in handling the dynamic motion analysis with both large rigid motion and large deformation. In this paper, the finite particle method (FPM) is used to simulate the motion and deformation coupled problems of the flexible six-legged robot. A shell-based particle model of a six-leg robot and the contact model between legs and ground are built. Without iterative and modification of the FPM analytical framework, structural nonlinearity is efficiently handled after eliminating rigid body motions by a fictitious reverse motion. The motion and deformation of a single leg with varying leg thickness, locomotion speed, and leg-to-ground friction coefficients were simulated. By analyzing the stress distribution in the leg and the number of contact points with the ground, the mechanical leg was optimized in design. Furthermore, the motion and deformation of the entire six-legged robot were simulated using the FPM. The numerical results' feasibility was validated through comparison with experimental data obtained from robot walking tests. The proposed method effectively simulates the motion and deformation of flexible robots, providing significant insights for the design of soft robots.

A Review on Recent Trends of Bioinspired Soft Robotics: Actuators, Control Methods, Materials Selection, Sensors, Challenges, and Future Prospects

Bioinspired soft robotics is an emerging field that aims to develop flexible and adaptive robots inspired by the movement and capabilities of biological organisms. This review article examines recent advances in materials, actuation mechanisms, sensors, and control strategies and discusses the challenges and future prospects of bioinspired soft robotics. Key innovations highlighted include pneumatic, elastomer actuators, variable‐length shape memory alloy tendons, closed‐loop control with soft sensors, and the incorporation of soft materials including shape memory polymers and conductive composites. Challenges in soft robotics such as achieving complex motion control, incorporating feedback systems, modeling soft material dynamics, and replicating biological muscle efficiency with artificial muscles are also discussed. Promising future directions are explored including the integration of biodegradable materials, machine learning‐based control algorithms, and leveraging data‐driven techniques for modeling and control. Building on progress in multi‐functional materials, manufacturing techniques, and bioinspired design principles, soft robots hold considerable promise for expanding robot capabilities, enhancing versatility and adaptability, enabling applications from wearable assistive devices to search and rescue operations. This review provides a holistic perspective encompassing key drivers propelling innovations in the vibrant field of bioinspired soft robotics.

Evaluation of digital innovation in smart homes – based on bibliometrics and rooted theory

PurposeTo evaluate the level of digital innovation in the smart home industry.Design/methodology/approachBy employing bibliometric analysis and grounded theory, this study aims to assess the innovation capabilities of leading manufacturing enterprises and establish a digital innovation evaluation system for smart homes.FindingsScientific arguments show that the cultivation of innovation capability should focus on three aspects of knowledge innovation, product innovation and technological innovation. Utilizing the four first-level indicators and 15 second-level indicators derived from the previous sections, we conducted TF-IDF (Term Frequency-Inverse Document Frequency) words frequency matching on the annual reports of A-share smart home listed companies. The resulting score for the company across these four primary indicators was then considered as the score for the digital innovation level indicator.Originality/valueThe evaluation system includes first-level indicators such as technological innovation, production innovation, management innovation and sales innovation and second-level indicators such as digital technology, intelligent manufacturing, industrial Internet, green intelligent manufacturing, flexible robot production and cross-border integration, which provides smart home enterprises with a reference to understand the level of digital innovation, improve the deficiencies and promote the development of the smart home industry.

Reliable and Energy-Efficient Communications in Mobile Robotic Networks by Collaborative Beamforming

For mobile robotic networks in industrial scenarios, reliable and energy-efficient communications are crucial yet challenging. Fortunately, collaborative beamforming (CB) emerges as a promising solution, which can increase the transmission gain and reduce the transmit power of robots by constructing a mobile robot-enabled virtual antenna array (MRVAA). The performance of CB is tightly related to robot positions, necessitating proper robot selection. However, robot selection may expose the network to the risk of unbalanced energy distribution, reducing network lifetime. Additionally, the mobility and variable numbers of robots require flexible and scalable robot selection algorithms. To tackle these challenges, we first formulate a multi-objective optimization problem to reduce the maximum sidelobe level (MSLL) of MRVAA while minimizing the standard deviation of the network energy distribution (SDNED) by selecting robots for CB. Then, based on distributed multi-agent learning (MARL), we propose an effective and scalable robot selection algorithm with energy considered (RoSE) to solve the problem, where difference-rewards function (DRF) and policy sharing are designed for enhancing convergence rate and policy stability. Simulation results show that the RoSE has the scalability to positions and numbers of robots. Furthermore, RoSE surpasses existing selection algorithms in network lifetime and time efficiency, while still maintaining comparable MSLL.

Flexible Artificial Tactility with Excellent Robustness and Temperature Tolerance Based on Organohydrogel Sensor Array for Robot Motion Detection and Object Shape Recognition.

Hydrogel-based flexible artificial tactility is equipped to intelligent robots to mimic human mechanosensory perception. However, it remains a great challenge for hydrogel sensors to maintain flexibility and sensory performances during cyclic loadings at high or low temperatures due to water loss or freezing. Here, a flexible robot tactility is developed with high robustness based on organohydrogel sensor arrays with negligent hysteresis and temperature tolerance. Conductive polyaniline chains are interpenetrated through a poly(acrylamide-co-acrylic acid) network with glycerin/water mixture with interchain electrostatic interactions and hydrogen bonds, yielding a high dissipated energy of 1.58MJ m-3, and ultralow hysteresis during 1000 cyclic loadings. Moreover, the binary solvent provides the gels with outstanding tolerance from -100 to 60°C and the organohydrogel sensors remain flexible, fatigue resistant, conductive (0.27 S m-1), highly strain sensitive (GF of 3.88) and pressure sensitive (35.8 MPa-1). The organohydrogel sensor arrays are equipped on manipulator finger dorsa and pads to simultaneously monitor the finger motions and detect the pressure distribution exerted by grasped objects. A machine learning model is used to train the system to recognize the shape of grasped objects with 100% accuracy. The flexible robot tactility based on organohydrogels is promising for novel intelligent robots.

Fuzzy Observer-Based Command Filtered Adaptive Control of Flexible Joint Robots With Time-Varying Output Constraints

Fuzzy Observer-Based Command Filtered Adaptive Control of Flexible Joint Robots With Time-Varying Output Constraints

Photothermal-driven soft actuator capable of alternative control based on coupled-plasmonic effect

Recent advancements in small-scale soft actuators have showcased their ability to respond to various stimuli, making them ideal for biomedical engineering and robotics. Light-responsive actuators constructed from photothermal materials such as carbon-based materials have received great attention for their remote wireless control ability. However, conventional carbon-based actuators typically exhibit uniform responses to different light wavelengths, limiting the complexity of their control. To address this limitation, this study develops light-driven soft actuators utilizing gold nanorods (Au NRs) to enhance selective photo-responsiveness. Au NRs are incorporated given their superior ability to convert light energy into localized heat through localized surface plasmon resonances, with efficiency varying based on their size and the wavelength of incident light. By adjusting the specifications of the Au NRs and the wavelength of the optical field, tunable actuation efficiency can be achieved. Experimental calibration confirms that the actuator exhibits differential performance across various wavelengths, enabling more precise control over its behavior. Demonstrations of application devices highlight the actuator’s versatility in flexible robotics, showcasing its potential for advanced applications. This innovation represents a considerable step towards achieving more adaptable and functional soft robotic systems, paving the way for future developments in this area.

Design and analysis of a flexible struts V-expander tensegrity robot for navigating pipes

Tensegrity robots have increasingly attracted attention in recent years. Traditionally, these robots rely on rigid struts and cables to maintain equilibrium configurations. However, the inflexibility inherent in these rigid struts curtails the robot’s capacity for deformation, thereby amplifying structural intricacy and imposing limitations on potential applications, particularly in the realm of pipe inspection. Drawing inspiration from the V-expander tensegrity structure, this paper presents a design, analysis, and validation of a flexible struts tensegrity robot. The integration of flexible struts enables the robot to exhibit a compact structure, passive compliance, and excellent adaptability. Through the actuation of three active cables, the robot exhibits inchworm-like motion capabilities for pipes ranging from 50 mm to 110 mm in diameters. A kinetostatics modeling approach is presented to predict the shapes of flexible struts and control the motion behaviors of the robot. To validate the capabilities of the proposed robot and assess the effectiveness of the kinetostatics model, a prototype was constructed and subjected to a series of experiments. The results demonstrate that the prototype exhibits remarkable shape changeability, mobility, and adaptability, while precisely controlling the contact force.