Soft origami continuum robots have attracted considerable attention because of their flexibility and adaptability, but they face challenges in control accuracy. This study presents a novel design and control scheme for large-deformation hybrid-actuated soft origami continuum robots to improve their motion performance in real tasks. An origami-based pneumatic chamber is used as the robot backbone to achieve a high extension ratio, and the hybrid variable stiffness principle combining antagonistic actuation and layer jamming further enhances the robot's overall structural stiffness. The robot performs precise movements by controlling tendons distributed in an external origami. An iterative training strategy based on long short-term memory is employed to model the inverse kinematics of the robot in consideration of the inherent hysteresis of origami robots. Step size features are introduced to improve model accuracy with limited data. The capability for variable stiffness enables the migration of the training model on the basis of a single segment, the accuracy of which is close to the submillimeter level. Decoupling control of each segment for a rear-driven multi-segment origami continuum robot is also achieved. Experiment results reveal that the trajectory tracking errors for single-segment and multi-segment of the robot are 1.58, and 2.81 mm, respectively, with relative errors of 0.71% and 0.63% over the robot length, demonstrating the good performance of the proposed design and control method. Orientation data can also be added to the dataset to achieve orientation control of the robot, with an angle error of 0.75°. The robot shows its stability and safety in a series of real tasks, including writing, LED tracking, board cleaning, and pick and place. This study demonstrates a potential solution for continuum robots performing precise tasks without any sensory feedback in confined spaces and specialized environments where electronic sensors fail.

High-precision tiny object alignment remains a common and critical challenge for humanoid robots in real world. To address this problem, this paper proposes a vision-based framework for precisely estimating and controlling the relative position between a handheld tool and a target object for humanoid robots, e.g., a screwdriver tip and a screw head slot. By fusing images from the head and torso cameras on a robot with its head joint angles, the proposed Transformer-based visual servoing method can correct the handheld tool’s positional errors effectively, especially at a close distance. Experiments on M4-M8 screws demonstrate an average convergence error of 0.8-1.3 mm and a success rate of 93%-100%. Through comparative analysis, the results validate that this capability of high-precision tiny object alignment is enabled by the Distance Estimation Transformer architecture and the Multi-Perception-Head mechanism proposed in this paper.

Electrical impedance tomography (EIT)-based tactile sensor has shown great potential in human–machine interaction due to its low manufacturing cost, large-area scalability. However, challenges, such as limited spatial resolution, and artifacts in reconstructed images, hinder their effectiveness. In response, this study proposes a model-driven deep learning reconstruction network for EIT-based tactile sensing, named PDCISTA-Net. The framework integrates a preprocessing filtering module and a dual-channel iterative shrinkage-thresholding algorithm (ISTA). Unlike traditional ISTA, PDCISTA-Net employs a dual-channel structural network tailored to capture and represent block correlations and sparsity within impedance change distributions. This approach enables end-to-end training, where parameters, such as step size, nonlinear transforms, and shrinkage thresholds, are learned from generated training data. In addition, a novel filtering module based on the sensitivity matrix is introduced to enhance reconstruction quality by mitigating measurement noise. Numerical metrics and visual results show that PDCISTA-Net outperforms traditional Newton's one-step error reconstructor, total variation, ISTA-Net, and FISTA-Net methods with higher structural similarity index measure and peak signal-to-noise ratio. Ablation experiments verified the effectiveness of the dual-channel structure in improving reconstruction quality. Finally, we developed an EIT-based tactile system to validate the practical application of our approach. The results from real-contact detection demonstrate enhanced image quality and greater noise robustness compared to traditional reconstruction methods.

Aiming at the robust force tracking challenge for robots in continuous contact with uncertain environments, a novel adaptive variable impedance control policy based on deep reinforcement learning (DRL) is proposed in this article. The policy includes a neural network feedforward controller and a variable impedance feedback controller. Based on the DRL algorithm, the iterative network feedforward controller explores and prelearns the optimal policy for impedance tuning in simulation scenarios with randomly generated terrain. The converged results are then used as feedforward inputs in the variable impedance feedback controller to improve the force-tracking performance of the robot during contact. A simplified dynamic contact model between the robot and the uncertain environment called the “couch model,” which satisfies the Lipschiz continuity condition, is developed to provide boundary conditions for the safe transfer of capabilities learned in simulation to real robots. Unlike the exhaustive example that relies on the completeness of the learning samples, this article gives theoretical proofs of the stability and convergence of the proposed control policy via Lyapunov’s theorem and contraction mapping principle. The control method proposed in this article is more interpretable and shows higher sample utilization efficiency and generalization ability in simulations and experiments.

Robotic hand exoskeletons hold immense potential for enhancing human hand functionality, addressing the hand's strength limitations and fatigue during physically-demanding tasks. However, most existing hand exoskeletons are motorized, being weak in generating high supporting force for gripping augmentation. We present a non-motorized hand exoskeleton based on magnetorheological (MR) actuators to provide high gripping support and elevate grip endurance. Meanwhile, it ingeniously harnesses human energy for actuation and energy storage, enhancing grip strength without external power. The MR actuator demonstrates a peak holding force of 1046 N with merely 5 W power input, boasting a force-to-power ratio one-order-of-magnitude higher than conventional approaches, and 97.7% energy reduction for same holding force compared to other approaches. Participants wearing the hand exoskeletons experience a 41.8% enhancement in grip strength without external power and reduced hand muscle fatigue during prolonged physical labor. In rescuing scenarios such as post-earthquake rescue, debris clearance, and casualty evacuation, our exoskeleton effectively supports gripping and improves working efficiency.

Animals possessing spinal columns often exhibit exceptional agility for highly dynamic locomotion. The spine grants the trunk with increased degrees of freedom, thereby endowing diverse postures. This paper presents the development of a robot STRAY for quadrupedal locomotion, featuring a four-degree-of-freedom spine design. Using trajectory based reinforcement learning techniques, STRAY is able to trot and bound dynamically using its spine. Simulation results reveal the positive roles of spinal movement, such as twisting, extension, retraction and rotation, in helping STRAY realize efficient locomotion. Preliminary results from experiments demonstrate that STRAY can achieve a trotting gait of approximately 0.6 m/s and a bounding gait of 0.7 m/s, with desired velocities of 0.8 m/s and 1.0 m/s, respectively. The results also indicate that reinforcement learning is a feasible way to investigate how the spine should be used in dynamic quadrupedal locomotion and achieve more possibilities in the future.

Robot skateboarding is a novel and challenging task for legged robots. Accurately modeling the dynamics of dual floating bases and developing effective planning and control methods present significant complexities in accomplishing skateboarding behavior. This paper focuses on enabling the quadrupedal platform CyberDog2 to achieve dynamic balancing and acceleration on a skateboard. An optimization-based control pipeline is developed through careful derivation of the system’s equations of motion, considering both the robot and skateboard dynamics. By accounting for system physical constraints, an advanced offline trajectory optimization method is employed to generate various acceleration trajectories, creating a motion library for the system. An online linear model predictive control with whole body control framework is used to track the generated trajectories and stablize the system in real-time. To validate its effectiveness, we conducted experiments in various scenarios. The quadrupedal robot successfully performed acceleration from a static state to various velocities and demonstrated the ability to balance and steer the skateboard.

Torque ripple reduction in quasi-direct drive (QDD) motors is crucial in their robotic applications for dynamic locomotion and dexterous manipulation. In this paper, we present a novel approach for reducing torque ripples of QDD motors, which integrates an angle-based repetitive learning observer (ARLO) and a model predictive control-based field-oriented controller (MPC-FOC). The proposed method successfully improves the torque loop control bandwidth and surpasses conventional proportional-integral (PI) controllers owing to the integrated physical constraints inside MPC. Additionally, the ARLO portion is able to mitigate ripple caused by the inherent cogging torque in brushless motors and also the periodic friction torque from the planetary gearboxes in QDD systems. The effectiveness of the proposed method is demonstrated through both simulation of a single QDD motor and experiments on a two-degree-of-freedom robotic leg, where the performance improvement can be 72.7% in speed tracking and 58.5% in trajectory tracking. The proposed method shows great potential in facilitating smooth motion and precise force control in future robotic applications.

Road extraction from light detection and ranging (LiDAR) point cloud data is crucial for modern urban management, transportation planning, autonomous driving, and so on. However, accurate road extraction is faced with challenges, such as obscuration from foreground elements and interference from similar looking elements. To overcome these challenges, this letter builds upon the classic DeepLabV3+ semantic segmentation model and proposes the CM-DeepLabV3+ model to extract deep context and fuse multistage features for superior road extraction performance. The proposed CM-DeepLabV3+ incorporates a cascade atrous spatial pyramid pooling (C-ASPP) module to enhance the contextual awareness of road features by cascading atrous convolution and incorporating attention mechanisms, an multistage feature fusion (MSFF) module to optimize the feature fusion process and ensure the effective integration of high-level semantic and low-level spatial information, and transfer learning technique to initialize the model weights using an auxiliary dataset and enhance the model’s adaptability and robustness to new scenarios. The experimental results on our customized dataset, which includes diverse urban park scenes across cities in China, demonstrate improved performance of CM-DeepLabV3+ in terms of accuracy, precision, recall, intersection over union (IoU), and$F1$score, validating the effectiveness of this approach.

Quadrupedal robots are performing increasingly more real-world capabilities, but are primarily limited to locomotion tasks. To expand their task-level abilities of object acquisition, i.e., run-to-catch as frisbee catching for dogs, this paper developed a control pipeline using stereo vision for legged robots which allows for dynamic catching balls while the robot is in motion. To achieve high-frame-rate tracking, we designed a ball that can actively emit homogeneous infrared (IR) light and then located the flying ball based on binocular vision positioning using the onboard RealSense D450 camera with an additional IR bandpass filter. The camera was mounted on top of a 2-DoF head to gain a full view of the target ball. A state estimation module was developed to fuse the vision positioning, camera motor readings, localization result of RealSense T265 equipped on the back, and the legged odometry output altogether. With the use of a ballistic model, we achieved a robust estimation of both the ball and robot positions in an inertial coordinate. Additionally, we developed a close-loop catching strategy and employed trajectory prediction so that tracking and run-to-catch were performed simultaneously, which is critical for such drastically dynamic and precise tasks. The proposed approach was validated through both static testing and dynamic catch experiments conducted on the CyberDog robot with a high success rate.

A multisensor data fusion method is investigated for bearing fault diagnosis of a switched reluctance motor (SRM) of an electric vehicle (EV) powertrain under varying speed conditions. The accumulative rotation angle of the SRM rotor is estimated by fusing the synchronous sampled current and vibration signals. The time-domain vibration signal is then resampled on an angular domain, and the bearing fault type is identified on the envelope spectrum of the resampled signal. In this article, an experimental setup is designed to validate the performance of the proposed method compared with the traditional ones. The practical EV working conditions including driving, coasting, and braking are considered in the experiments. Results indicated that the proposed method successfully diagnoses the SRM bearing faults under random and complex conditions. The method is promising for online SRM fault diagnosis under varying speed conditions as it requires no extra tachometer, specifically when the sensorless control strategy is adopted.

Liquid metal is an interesting metallic material with many unique properties that can be applied in many applications. Liquid metal droplets can be activated by an external electrical field in aqueous environments, which has led to the development of novel actuators. However, a study on the development and control of liquid metal actuating robots is still absent, which hinders their further applications. In this paper, we report the development of a novel controllable untethered vehicle driven by electrically actuated liquid metal droplets in a sodium hydroxide solution. The simplified dynamic model of the vehicle in sodium hydroxide solution was developed. The vehicle's performance, including translational and rotating locomotion with various speeds, was experimentally evaluated. The vehicle driven by liquid metal droplets possesses many advantages such as working silently, almost wear-free motion, and low power consumed, which has great potential to be applied in liquid metal enabled robotics and automation process such as laboratory automation.

As the concept of variable stiffness (VS) and variable damping (VD) has increasingly drawn attention because of its superiority on reducing unwanted vibrations, dampers with property of varying stiffness and damping have been an attractive method to further improve vehicle performance and driver comfort. This paper presents the design, prototyping, modeling, and experimental evaluation of a VS and VD magnetorheological (MR) vehicle suspension system. It was first characterized by an INSTRON machine. Then, a phenomenological model was proposed to capture the characteristics of the damper and TS fuzzy approach was used to model the quarter car system where the proposed damper was installed. Different controllers, including skyhook, short-time Fourier transform and state observer based controller were designed to control the damper. Experimental results demonstrate that the quarter car system with the VS and VD suspension performs best in terms of reducing the sprung mass accelerations comparing with other suspensions.

This paper presents the development of a fish-like magnetically propelled microswimmer fabricated by 3D laser lithography. The microswimmer consists of a head and a caudal fin, just like a natural fish. There is a joint between the head and the fin so that the caudal fin can oscillate around the head to generate thrust, and the oscillation of the fin hardly transfers to the head, which benefits the stable motion of the microswimmer. The caudal fin of the microswimmer is deposited with a layer of 50 nm nickel (Ni) for magnetic actuation. Through applying an oscillating uniform magnetic field, the microswimmer can move along with the direction guided by the external magnetic field. A magnetic control system with permanent magnets is designed to provide such an oscillating uniform magnetic field, where the oscillating frequency and amplitude are controllable. A micro probe operation platform is used to detach the fabricated microswimmers from glass substrate in manufacturing. The proposed magnetically propelled microswimmer can be potentially used as powerful detoxification and biosensing tools for medical diagnosis and treatment in precision medicine.

The amphibious robot is so attractive and challenging for its broad application and its complex working environment. It should walk on rough ground, maneuver underwater and pass through transitional terrain such as sand and mud, simultaneously. To tackle with such a complex task, a novel amphibious robot (AmphiHex-I) with transformable leg-flipper composite propulsion is proposed and developed. This paper presents the detailed structure design of the transformable leg-flipper propulsion mechanism and its drive module, which enables the amphibious robot passing through the terrain, water and transitional zone between them. A preliminary theoretical analysis is conducted to study the interaction between the elliptic leg and transitional environment such as granular medium. An orthogonal experiment is designed to study the leg locomotion in the sandy and muddy terrain with different water content. Finally, basic propulsion experiments of AmphiHex-I are launched, which verified the locomotion capability on land and underwater is achieved by the transformable leg-flipper mechanism.

An overwhelming phenomena across natural systems, social systems and ecosystems is discovered in recent years. The phenomena is coined by many terms, such as 1/f noise, Zipf-laws, or scale-free, while power-law distribution of various events or metrics is the fundamental fact that exists in every complex system. It is believed that there exist a mechanism to rule the dynamics of complex system and generate the distribution. In this paper, a general growth model which incorporate Lotka-Volterra dynamics is developed to explain the mechanism of the power-law distribution preliminary. In the model, the influence on power distribution of the spreading rate and the mortality rate can be easily analyzed and explained.

A social network consists of events and individuals, in which the events denote the activities happening in the system and the individuals denotes the peoples who are attracted into the activities. A memory feature exists in a dynamic social network which leads to the decay of the event attraction, and further influences the structure and the dynamics of the network. In the paper, an agent model for a social memory network is built and implemented. The simulation result reveals the dynamics of the average life span of events. The result also discovers how a social network with a small "diameter" and a large clustering coefficient evolves. The model is validated with the empirical data from USTC bulletin board system (BBS).

A social network is composed of social individuals and their relationships. In many real-world applications, such a network will evolve dynamically over time and events. A social network can be naturally viewed as a multiagent system if considering locally-interacting social individuals as autonomous agents. In this paper, we present an Autonomy-Oriented Computing (AOC) based model of a social network, and study the dynamics of the network based on this model. In the AOC model, the profile of agents, service-based interactions, and the evolution of the network are defined, and the autonomy of the agents is emphasized. The model can reveal dynamic relationships among global performance, local interaction (partner selection) strategies, and network topology. The experimental results show that the agent network forms a community with a high clustering coefficient, and the performance of the network is dynamically changing along with the formation of the network and the local interaction strategies of the agents. In this paper, the performance and topology of the agent network are analyzed, and the factors that affect the performance and evolution of the agent network are examined.

A social network can be modelled by a multi-agent system, in which the interaction among agents is represented as a service transaction process. In this paper, we present a service-based agent network to simulate and study the dynamics of social networks. In the network, the profiles of agents and service-based interactions are defined deliberately. Autonomy is emphasized as the ability of agents to manage their behaviors according to the local environment and their profiles. The experimental results reveal that network performance, network topology and the profiles of agents all evolve along with local behaviors. The over-shoot phenomenon in the evolution of network is discovered and analyzed. The discoveries are meaningful for understanding the relationship between network dynamics and local behaviors

In this paper, we describe a new evolutionary computation approach, called multiphase genetic programming (MPGP). The special features of this approach lie in its variable-granularity representations of chromosomes and their corresponding genetic operations. In the paper, we provide an overview of the MPGP approach as well as details on how the sumo maneuver evolution experiments are carried out and how the MPGP-based case study differs from others.

Researchers have recently discovered several interesting, self-organized regularities from the World Wide Web, ranging from the structure and growth of the Web to the access patterns in Web surfing. What remains to be a great challenge in Web log mining is how to explain user behavior underlying observed Web usage regularities. We address the issue of how to characterize the strong regularities in Web surfing in terms of user navigation strategies, and present an information foraging agent-based approach to describing user behavior. By experimenting with the agent-based decision models of Web surfing, we aim to explain how some Web design factors as well as user cognitive factors may affect the overall behavioral patterns in Web usage.