This paper presents a radiation-resilient spine-inspired robotic arm actuated by shape-memory alloys (SMAs) to address environmental adaptability challenges in high-radiation nuclear environments such as power plants and decommissioning facilities. Forward- and inverse kinematics models are systematically established, followed by the development of a radiation-hardened control board utilizing field-programmable gate arrays (FPGAs) with multi-layer shielding to ensure operational reliability. To precisely regulate bending deformation, a predictive control algorithm integrating proportional-integral-derivative (PID) and radial basis function (RBF) neural networks calculates the optimal output voltage for heating SMA springs, achieving enhanced shape control accuracy. Furthermore, the robotic arm incorporates programmable variable-stiffness joints enabled by low-melting-point alloys, endowing it with shape-reconfiguration capabilities to adaptively perform tasks in unstructured spaces. Experimental validation confirmed the dexterous and compliant manipulation performance of the system, demonstrating its potential for critical applications, including maintenance, emergency response, and precision inspection within confined radioactive environments.

Recent research has reported the impressive adaptability of bio-inspired soft robots in executing complex movements and traversing diverse terrains. However, achieving navigation for these robots remains challenging when operating in unknown environments with solely preprogrammed locomotion strategies. Inspired by the tactile appendages of the star-nosed mole, we present a soft overturning robot with tactile-driven navigation in unknown confined spaces. Leveraging Cosserat rod theory, we develop a bending configuration estimation model that accounts for the robot's weight distribution and axial deformation. The system enables real-time perception of self-orientation and obstacle localization during motion. Experimental results validate the robot's ability to generate adaptive locomotion gaits in unstructured environments using tactile feedback, independent of external navigation systems.

Abstract With the rapid increase of space debris such as defunct satellites and rocket upper stages, active debris removal has become an urgent task for the aerospace community. Traditional rigid manipulators suffer from poor adaptability and may cause unintended collisions when interacting with irregularly shaped or fragile objects, while purely flexible manipulators often lack sufficient gripping force and operational accuracy. To address these challenges, this paper proposes a novel net-coordinated variable stiffness manipulator. The manipulator consists of three fingers, a variable stiffness mechanism, and a flexible capture net. Two types of variable stiffness mechanisms are developed: one based on shape memory alloy (SMA) actuation inspired by human finger anatomy, and another based on a variable-fulcrum cantilever beam model. Experimental results show that the SMA-driven finger achieves a stiffness variation range of 2.29 times (from 22.3 N·mm rad −1 to 50.86 N·mm rad −1 ), while the cantilever-based mechanism achieves a much wider range of up to 107 times (from 0.71 N·mm rad −1 to 76.33 N·mm rad −1 ). A magnetically controlled self-locking mechanism and a flexible capture net are also integrated to enable high-stiffness locking for heavy loads and conformal wrapping for enhanced stability. The proposed manipulator can reliably grasp objects of various shapes, sizes, materials, and weights (from <300 g to ∼1 kg), offering a promising solution for compliant capture of non-cooperative targets in future space debris removal missions.

The widespread use of spot color inks in packaging printing has led to the accumulation of substantial remaining spot color inks, resulting in resource waste and environmental concerns. This study proposes a novel utilization method for remaining spot color inks that integrates Delaunay triangulation with the single-constant Kubelka-Munk (K-M) theory to achieve precise and efficient reuse. A color matching database was first established based on the spectral reflectance and colorimetric properties of base and remaining spot color inks. The Delaunay triangulation algorithm was applied enabling the identification of feasible ink combinations through tetramodel based on the single-constant K-M theory was developed to optimize ink formulations for given target colors. Experimental validation using multiple remaining spot color targets demonstrated that resource-efficient color management in industrial printing.

A bionic eye system driven by shape memory alloy (SMA) springs is designed for humanoid facial expression.• An antagonistic SMA spherical joint model enables precise multi-directional eyeball rotation with ±20° range and <1° error.• An SMA-PVC bias actuation mechanism with closed-loop PID control achieves eyelid motion with step tracking error <3.7%.

Accurate spot color matching is critical to printing applications, yet constructing an efficient ink base database remains a challenge due to the labor-intensive preparation of ink ladder samples. This study proposes a two-step optimization method to enhance the efficiency and accuracy of spot color prediction using the singlesimilarity screening via the Goodness-of-Fit Coefficient to select samples with consistent spectral behavior. The second step optimizes for K/S linearity, identifying concentrations (35% and 40%) that best align with the KM model's linearity assumption. Five target spot colors, created by mixing yellow, red, and blue base inks, were used to evaluate the method. The K/S values derived from three sample sets-all ladder samples, one-step optimized samples, and two-step optimized samples-were used to predict spectral reflectance and for one-step optimized samples, demonstrating superior accuracy. By reducing the required samples from 19 to 2 per ink, the method for industrial applications such as packaging and branding.

In this paper, a load-adaptive continuum robot with accurate shape sensing and control capabilities through tendon tension modulation is presented. First, three shape memory alloy (SMA) springs actuate the bioinspired continuum robot to achieve 3D deformation. Second, the bending shape can be accurately estimated in real time using the tensions of the SMA springs based on the forward kinematics of a modified Cosserat model that considers friction between the tendons and disks. For a desired position, the required tensions of the SMA springs can be obtained using the inverse kinematics of the proposed model. Finally, a closed-loop control method is implemented to test the continuum robot's shape control performance. Experiments demonstrate that the robot exhibits accurate tracking results for different complex trajectories, both with and without an external load at the end effector, based on the proposed model's forward and inverse kinematics. In conclusion, SMA actuation combined with tension feedback control enables accurate load-bearing capacity, shape sensing, and position tracking, representing a promising approach for developing future design guidelines for continuum robots.

The realization of natural and authentic facial expressions in humanoid robots poses a challenging and prominent research domain, encompassing interdisciplinary facets including mechanical design, sensing and actuation control, psychology, cognitive science, flexible electronics, artificial intelligence (AI), etc. We have traced the recent developments of humanoid robot heads for facial expressions, discussed major challenges in embodied AI and flexible electronics for facial expression recognition and generation, and highlighted future trends in this field. Developing humanoid robot heads with natural and authentic facial expressions demands collaboration in interdisciplinary fields such as multi-modal sensing, emotional computing, and human-robot interactions (HRIs) to advance the emotional anthropomorphism of humanoid robots, bridging the gap between humanoid robots and human beings and enabling seamless HRIs.

Achieving robust environmental interaction in small-scale soft robotics remains challenging due to limitations in terrain adaptability, real-time perception, and autonomous decision-making. Here, we introduce Flexible Electronic Robots constructed from programmable flexible electronic components and setae modules. The integrated platform combines multimodal sensing/actuation with embedded computing, enabling adaptive operation in diverse environments. Applying modular design principles to configure structural topologies, actuation sequences, and circuit layouts, these robots achieve multimodal locomotion, including vertical surface traversal, directional control, and obstacle navigation. The system implements proprioception (shape and attitude) and exteroception (vision, temperature, humidity, proximity and pathway shape recognition) under dynamic conditions. Onboard computational units enable autonomous behaviors like hazard evasion and thermal gradient tracking through adaptive decision-making, supported by embodied artificial intelligence. In this work, we establish a framework for creating small-scale soft robots with enhanced environmental intelligence through tightly integrated sensing, actuation, and decision-making architectures.

Space debris, such as abandoned rocket bodies, defunct satellites, and space rocks, poses an ever-increasing risk to satellites and spacecraft, threatening the long-term security and stability of the space environment. Active debris removal has become a critical task for the spaceflight community. Traditional rigid manipulators for capturing debris are heavy and lack adaptability, often causing unwanted collisions during interactions with fragile objects. This paper presents a novel lantern-like manipulator actuated by smart materials, capable of shape deformation via shape memory alloys and variable stiffness modulation using polycaprolactone (PCL). Experimental results demonstrate the manipulator's ability to capture non-cooperative targets with irregular shapes, offering a promising solution for compliant active debris removal in future space missions.

Many soft hands usually preprogram desired motions using oblique actuators, which limits their motion diversity and adaptability. To achieve high compliance and adaptability, this paper proposes a novel soft hand with a hybrid actuator system that can demonstrate both pure bending and helical motions. First, the structure and actuation system of the soft hand are presented. A direct-current motor is used to rotate the actuators. A pump is used to output sufficient torque to bend the soft hand. Second, a theoretical model and Finite Element Analysis (FEA) method are used to analyze the bending performance at various pressures. Then, a control method is proposed to regulate the helical radius of the soft hand in real time by adjusting the rotation angle and pressure simultaneously. Furthermore, experimental results verify the feasibility of programmable and dexterous grasping according to the shape of objects based on the independent control of bending and helical motions.

In order to apply the single-constant Kubelka–Munk (KM) model to color prediction of fiber blends, a novel correction method is proposed in the paper. The single-constant KM model is based on the assumption that the ratio of absorption coefficients to scattering coefficients (K/S) of a mixture is linear to mass proportion of its components. However, when it comes to the media of pre-colored fiber blends, the linear assumption always fails, resulting in inaccurate color prediction with large color difference. To solve this problem, a novel correction method was proposed, which improved the linearity of K/S in the way of decreasing the linear deviation. Pre-colored cotton fibers were used to prepare samples to examine the proposed correction method. The average color difference values ΔEcmc (2:1) and ΔE00 of the single-constant KM model with proposed correction method are 1.37 and 1.17 respectively, which are remarkably better than those of the Kubelka–Munk model without correction ( 8.41 and 6.35) and the Kubelka–Munk model with Saunderson correction ( 8.63 and 6.55). The results indicate that, for the media of pre-colored fiber blends, the proposed correction method greatly improves the color prediction accuracy.

Space debris removal is important for the environment of outer space to avoid the endangering of space systems in low earth orbit. Space debris capture is the base of the on-orbit servicing and the activity of human beings in outer space. In this paper, a novel gripper with variable stiffness is presented for space debris capture. The joint of the finger can switch between high stiffness state and low stiffness state by heating and cooling the embedded low melting point alloys (LMPA), which demonstrates a great stiffness change up to 102 times. Due to the finger in low stiffness state affected by its body weight, Cosserat rod theory is used to model the bending shape. The experimental results demonstrate that multi-finger gripper can hold different objects and achieve self-locking performance under load based on variable stiffness mechanism, which can potentially be an alternative for space debris capture in practical applications.

In this article, a new soft finger is presented to achieve a fast variable stiffness mechanism using polycaprolactone based on an integrated actuating–cooling system. First, the variable stiffness finger demonstrates a dramatic 15-fold stiffness variation from 10.2 × 10 4 to 0.67 × 10 4 N·mm 2 when the temperature is changed in a range from 20 °C to 60 °C. The cycle time of the variable stiffness based on the integrated actuating–cooling hydraulic system is approximately 505 s, which is much shorter than that of 3870 s based on the pneumatic method. Second, due to the varied weight of water in the chamber, Cosserat rod theory is used to model the bending shape of the soft finger instead of the constant curvature model. A feasible method to control the position and stiffness simultaneously based on the Cosserat model is presented. Finally, a three-fingered soft hand is fabricated based on the variable stiffness and self-locking method, which can hold objects up to 3560 g successfully, which is more than 5.2 times its own weight of 680 g.

In this paper, a new soft finger is presented to achieve self-locking performance based on variable stiffness mechanism using polycaprolactone (PCL). The variable stiffness structure demonstrates the capability of 167 times stiffness variation. A pneumatic soft finger with PCL structure is fabricated, which achieves different bending shape under the same air pressure when stiffness changed. The soft finger also demonstrates self-locking performance even under load. Two different multi-finger soft hands are fabricated to test the corresponding self-locking capacity. The experimental results demonstrate that the multi-finger soft hands can hold different objects with weight up to 184 g. In conclusion, the soft finger based on PCL structure shows an alternative for the variable stiffness mechanism. It can potentially be developed into manipulators in practical applications to prevent failed grasping incurred by actuator fault, and also be used to save energy consumption for the objects needed to be holden for a long time based on self-locking mechanism.

Due to the dyeing process, learning samples used for color prediction of pre-colored fiber blends should be re-prepared once the batches of the fiber change. The preparation of the sample is time-consuming and leads to manpower and material waste. The two-constant Kubelka-Munk theory is selected in this article to investigate the feasibility to minimize and optimize the learning samples for the theory since it has the highest prediction accuracy and moderate learning sample size requirement among all the color prediction models. Results show that two samples, namely, a masstone obtained by 100% pre-colored fiber and a tint mixed by 40% pre-colored fiber and 60% white fiber, are enough to determine the absorption and scattering coefficients of a pre-colored fiber. In addition, the optimal sample for the single-constant Kubelka-Munk theory is also explored.

随着互联网时代的到来,社交电商得到蓬勃发展.面对日趋激烈的竞争环境,如何开展有效的营销活动成为平台企业成功的关键.基于"认知—情感—行为意愿"理论,构建社交电商用户购买意愿的影响因素模型,实证分析用户的认知对购买意愿的影响,重点探索用户认知对购买意愿的作用机制.通过问卷调查法收回293份有效数据,并利用SPSS和AMOS软件对假设进行实证检验.研究发现:感知价值对沉浸体验和购买意愿有显著正向影响,感知过载对用户倦怠有显著正向影响,但对购买意愿的影响不显著;沉浸对购买意愿有正向影响,倦怠对购买意愿的影响不显著.

针对现有软体机械手存在形变模型不准确、操作空间小和位置控制精度低等问题，设计出一种液-气相变驱动三关节软体机械手并进行了相应的形变改进模型分析与运动控制试验。建立一种考虑温度与材料自重因素的硅胶形变非线性力学模型且基于该模型完成了机械手的弯曲性能分析，建立三关节机械手末端操作空间的数学模型。而后基于传统刚性机械手的运动学逆解算法，提出一种基于最小驱动能量函数的运动学逆解方法来确定机械手各关节的弯曲角度。通过最小驱动能量函数并利用神经网络算法策略实现了三关节软体机械手末端轨迹的稳定控制，其相对位置距离偏差小于2.8%。通过充分的试验和理论分析，为液-气相变转化驱动软体机械手的后期实际应用提供了理论支撑。

Inspired by turtle locomotion, a novel type of robot with flippers actuated by antagonistic shape memory alloy (SMA) springs is presented that can crawl based on two different variable friction mechanisms. First, unlike robots incorporating a variable friction mechanism based on weight, the turtle-inspired robot bends its flippers through " on-off" control of the SMA springs in an open-loop system and achieves high friction based on the electromagnet. It can crawl on a vertical surface under a load with a load ratio of 78.5% without accurate locomotion control. To achieve fast and accurate displacement for each locomotion sequence, a radial basis function (RBF) compensator based on the minimum parameter algorithm and a mechanical model of the flippers is used to control the antagonistic SMA springs. The robot can regulate the bending shape of its flippers using an RBF compensator and climb a nonmagnetic surface with an inclination angle of $72^\circ $ when it utilizes suction units in the foot for its variable friction mechanism. The robot can crawl forward along both the X - and Y -axis directions. In conclusion, antagonistic SMA actuation with an RBF compensator provides fast and accurate performance and is a feasible alternative to the disturbance of the control of the flippers. This article can be used to develop design guidelines for crawling robots.

在后疫情时代和内循环经济的背景下,企业经营的压力和开拓市场的难度都越来越大.如何化解这种困局?开辟式创新为企业解决这类问题提供了一个新的思路.文章以五菱宏光MINI EV新能源车为例,分析了其销售成功和成为开辟式创新典范的原因,希望更多企业能从中得到启示.