The intricate water-land intermingled nature of wild environments necessitates robots to exhibit multimodal cross-domain mobility capabilities. This paper introduces a novel wheel-spoke-paddle hybrid amphibious robot (WSP-bot) that can operate on flat and rough terrains, water surfaces, and water-land transitional zones. The proposed robot relies on a propulsion mechanism called transformable wheel-spoke-paddle (WSP), which combines the stability of wheeled robots with the obstacle-climbing capability of legged robots, while also providing additional aquatic mobility. The utilization of a crank-slider-based transformation mechanism enables seamless switching between multiple motion modes. An analysis of mode transition and ground motion in spoke mode was conducted, along with an investigation of its obstacle-crossing capability. Simulations were performed for mode transition, ground locomotion, and obstacle-crossing, as well as propulsion of a single WSP module on water. Based on the above work, a prototype robot was manufactured. Prototype tests, including mode transition and mobility tests on land and water surfaces under multimodal states, confirmed the effectiveness of the proposed WSP-bot.

An online pattern recognition method of lower limb movements is proposed based on the personalized surface electromyography (sEMG) signals, and the corresponding experimental researches are performed in the rehabilitation training. Further, a wireless wearable acquisition instrument is used. Based on this instrument, a host computer for the personal online recognition and real-time control of rehabilitation training is developed. Three time-domain features and two features in the nonlinear dynamics are selected as the joint set of the characteristic values for the sEMG signals. Then a particle swarm optimization (PSO) algorithm is used to optimize the feature channels, and a k-nearest neighbor (KNN) algorithm and the extreme learning machine (ELM) algorithm are combined to classify and recognize individual sample data. Based on the multi-pose lower limb rehabilitation robot, the real-time motion recognition and the corresponding rehabilitation training are carried out by using the online personalized classifier. The experimental results of eight subjects indicate that it takes only 6 min to build an online personalized classifier for the four types of the lower limb movements. The recognition between switches of different rehabilitation training movements is timely and accurate, with an average recognition accuracy of more than 95%. These results demonstrate that this system has a strong practicability.

Heavy-duty hexapod robots are well-suited for physical transportation, disaster relief, and resource exploration. The immense locomotion capabilities conferred by the six appendages of these systems enable traversal over unstructured and challenging terrain. However, tipping can be a serious concern when moving with a tripod gait in these challenging environments, which may cause irreversible consequences such as compromised movement control and potential damage. In this paper, we focus on heavy-duty hexapod robot sideline tipping judgment and recovery during tripod gait motion, and a novel sideline tipping judgment and recovery method is proposed by adjusting an optimal swinging leg to the stance state. Considering the locomotion environments, motion mode, and tipping analysis, the robot’s stability margin is quantified, and the tipping event is evaluated by the Force Angle Stability Measure (FASM). The recovery method is initiated upon detecting that the robot is tipping, which involves the selection of an adjustment leg and the determination of an optimal foothold. Since the FASM is based on the foot force and robot center of gravity (CoG), the stability margin quantification expression is reformulated to the constraint form of quadratic programming (QP). Furthermore, a foot force distribution method, integrating stability margin considerations into the QP model, has been devised to ensure post-adjustment stability of the landing leg. Experiments on tipping judgment and recovery demonstrate the effectiveness of the proposed approaches on tipping judgment and recovery.

This article proposes a control method for underactuated cartpole systems using semi-implicit cascaded proportional-derivative (PD) controller. The proposed controller is composed of two conventional PD controllers, which stabilizes the pole and the cart second-order dynamics respectively. The first PD controller is realized by transforming the pole dynamics into a virtual PD controller, with the coupling term exploited as the internal tracking target for the cart dynamics. Then, the second PD controller manipulates the cart dynamics to track that internal target. The solution to the internal tracking target relies on an equation set and features a semi-implicit process, which exploits the internal dynamics of the system. Besides, the design of second PD controller relies on the parameters of the first PD controller in a cascaded manner. A stability analysis approach based on Jacobian matrix is proposed and implemented for this fourth-order system. The proposed method is simple in design and intuitive to comprehend. The simulation results illustrate the superiority of proposed method compared with conventional double-loop PD controller in terms of convergence, with the theoretical conclusion of at least locally asymptotic stability.

Currently, workers in sand casting face harsh environments and the operation safety is poor. Existing pouring robots have insufficient stability and load-bearing capacity and cannot perform intelligent pouring according to the demand of pouring process. In this paper, a hybrid pouring robot is proposed to solve these limitations, and a vision-based hardware-in-the-loop (HIL) control technology is designed to achieve the real-time control problems of simulated pouring and pouring process. Firstly, based on the pouring mechanism and the motion demand of ladle, a hybrid pouring robot with a 2UPR-2RPU parallel mechanism as the main body is designed. And the equivalent hybrid kinematic model was established by using Eulerian method and differential motion. Subsequently, a motion control strategy based on HIL simulation technique was designed and presented. The working space of the robot was obtained through simulation experiments to meet the usage requirements. And the stability of the robot was tested through the key motion parameters of the robot joints. Based on the analysis of pouring quality and trajectory, optimal dynamic parameters for the experimental prototype are obtained through water simulation experiments, the pouring liquid height area is 35–40 cm, the average flow rate of pouring liquid is 112 cm3/s, and the ladle tilting speed is 0.0182 rad/s. Experimental results validate the reasonableness of the designed pouring robot structure. Its control system realizes the coordinated movement of each branch chain to complete the pouring tasks with different variable parameters. Consequently, the designed pouring robot will significantly enhance the automation level of the casting industry.

Robots with multi-sensors always have a problem of weak pairing among different modals of the collected information produced by multi-sensors, which leads to a bad perception performance during robot interaction. To solve this problem, this paper proposes a Force Vision Sight (FVSight) sensor, which utilizes a distributed flexible tactile sensing array integrated with a vision unit. This innovative approach aims to enhance the overall perceptual capabilities for object recognition. The core idea is using one perceptual layer to trigger both tactile images and force-tactile arrays. It allows the two heterogeneous tactile modal information to be consistent in the temporal and spatial dimensions, thus solving the problem of weak pairing between visual and tactile data. Two experiments are specially designed, namely object classification and slip detection. A dataset containing 27 objects with deep presses and shallow presses is collected for classification, and then 20 slip experiments on three objects are conducted. The determination of slip and stationary state is accurately obtained by covariance operation on the tactile data. The experimental results show the reliability of generated multimodal data and the effectiveness of our proposed FVSight sensor.

Most of the currently available three-degree-of-freedom manipulators are light load and cannot achieve full continuous rotation; given this, we designed a heavy-load manipulator that achieves unrestricted and continuous rotation. Due to manufacturing and assembly errors, parameter deviations between the real manipulator and its underlying theoretical model were unavoidable. Because of the lack of high-precision, high-frequency, and real-time closed-loop detection methods, we proposed a type of kinematics calibration of parameterized ant colony optimization and feedforward control methods. This was done to achieve high-precision motion control. First, an error model combining structural parameters and joint output angles was established, and the global sensitivity of each error source was analyzed to distinguish both primary and secondary sources. Based on the measured data of a laser tracker, the ant colony optimization was then used to identify six error sources. This resulted in both link length and joint driving errors of the designed manipulator. As it is a type of systematic error, the rounding error of the theoretical trajectory was carefully analyzed, and feedforward control methods with different coefficients were designed to further improve positioning accuracy based on the kinematic calibration. Experimental results showed that the proposed kinematic calibration and feedforward control methods achieved relatively precise motion control for the designed manipulator.

Unmanned aerial vehicles (UAVs) possess fast-moving abilities and have been used in various tasks in the past decades. However, their performances are still restricted by insufficient endurance and confined environments. Intuitively, combining other locomotion modes with UAVs, such as diving and driving, would be an appropriate idea to improve the robot’s adaptability and solve the endurance problem. Recently, the terrestrial/aerial hybrid robots have drawn the researchers’ eyes for their outstanding performances, which can deploy flight mode to traverse insurmountable terrains and ground mode to increase endurance and realize detailed searches. Therefore, this paper developed the autonomous quadrotor tilting hybrid robot (AQT-HR) to achieve terrestrial/aerial dual-modal mobility and verified that the robot delivers high energy efficiency. The AQT-HR can achieve flying and driving through a quadrotor tilting mechanism, which can alter one single driving force into different directions. Furthermore, the dynamic models of the hybrid robot’s aerial and ground locomotion are derived and introduced into the model-feedforward PID control algorithm for improving the robot’s flying stability. Finally, we conducted some mobility tests and experiments about traversing obstacles to demonstrate that the proposed hybrid robot can realize autonomous mode switching and perform a low energy consumption in ground movement mode.

The language-guided visual robotic grasping task focuses on enabling robots to grasp objects based on human language instructions. However, real-world human-robot collaboration tasks often involve situations with ambiguous language instructions and complex scenarios. These challenges arise in the understanding of linguistic queries, discrimination of key concepts in visual and language information, and generation of executable grasping configurations for the robot’s end-effector. To overcome these challenges, we propose a novel multi-modal transformer-based framework in this study, which assists robots in localizing spatial interactions of objects using text queries and visual sensing. This framework facilitates object grasping in accordance with human instructions. Our developed framework consists of two main components. First, a visual-linguistic transformer encoder is employed to model multi-modal interactions for objects referred to in the text. Second, the framework performs joint spatial localization and grasping. Extensive ablation studies have been conducted on multiple datasets to evaluate the advantages of each component in our model. Additionally, physical experiments have been performed with natural language-driven human-robot interactions on a physical robot to validate the practicality of our approach.

SLAM Benchmark plays a pivotal role in the field by providing a common ground for performance evaluation. In this paper, a novel methodology of simultaneous localization and mapping benchmark and map accuracy improvement (SLAMB&MAI) is introduced. It can objectively evaluate errors of localization and mapping, and further improve map accuracy by utilizing evaluation results as feedback. The proposed benchmark transforms all elements into a global frame and measures the errors between them. The comprehensiveness consists in the benchmark of both localization and mapping, and the objectivity consists in the consideration of the correlation between localization and mapping by the preservation of the original pose relations between all reference frames. The map accuracy improvement is realized by first obtaining the optimization that minimizes the errors between the estimated trajectory and ground truth trajectory and then applying it to the estimated map. The experimental results showed that the map accuracy can be improved by an average of 15%. The optimization that yields minimal localization errors is obtained by the proposed Centre Point Registration-Iterative Closest Point (CPR-ICP). This proposed Iterative Closest Point (ICP) variant pre-aligns two point clouds by their centroids and least square planes and then uses traditional ICP to minimize the error between them. The experimental results showed that CPR-ICP outperformed traditional ICP, especially in cases involving large-scale environments. To the extent of our knowledge, this is the first work that can not only objectively benchmark both localization and mapping but also revise the estimated map and increase its accuracy, which provides insights into the acquisition of ground truth map and robot navigation.

In the current investigation, a two-stage hybridization model has been used for the motion planning of humanoids in complex environmental conditions using regression analysis and the firefly algorithm. In the first step, sensory outputs are fed to the regression model, and an initial turning angle (ITA) is obtained. In the second step, the ITA is again fed as input to the firefly model along with other required inputs, and the final turning angle (FTA) is obtained. The FTA serves as the guiding parameter for the humanoids to reach their desired destinations. The developed motion planning scheme has been implemented on NAO humanoid robots in simulation and experimental platforms. A Petri-Net control strategy has been integrated along with the hybrid scheme while negotiating multiple humanoids in a common platform. The results obtained from the motion planning analysis in simulation and experimental arenas are compared against each other in terms of selected navigational parameters and observed satisfactory agreements. Finally, the proposed hybrid controller is also tested against another standard navigational model and substantial enhancement in the performance has been noticed.

This paper systematically investigates a soft-rigid hybrid pneumatic actuator (SRHPA), which consists of a rigid-foldable twisting skeleton capable of a large range of helical motion and a soft bellows muscle with high linear driving force. Considering the unique varying-pitch helical motion of the foldable skeleton, the analytical model mapping the input force generated by the bellows muscle and output forces of the actuator is revealed and verified with a simulation of the force analysis. Prototypes of the actuator are developed by fabricating the twisting skeleton with multilayered aluminum composite panels and 3D-printing the bellows muscle with thermoplastic polyurethane (TPU) 95A filament. The static and dynamic performances of the prototypes are tested to validate the analytical modeling of output forces. Using the actuator as a module, a novel bipedal inchworm robot with four modules is developed and tested to demonstrate its adaptability in confined space by switching between the going-straight, the turning-around, and the rotating gaits. The hybrid actuator and the inchworm robot with zero onboard electronics have the potential to be deployed in extreme environments where pneumatically actuated systems are preferred over electrical machines and drives, such as in nuclear and explosive environments.

The increase of human presence in the subsea and seabed environments necessitates the development of more capable and highly dexterous, innovative underwater manipulators. Biomimetic soft-robot arms represent a promising candidate for such manipulation systems. However, the well-known modeling techniques and control theories of traditional rigid robots do not apply to soft robots. The challenges of kinematic and dynamic modeling of soft robots with infinite degrees of freedom require the development of dedicated modeling methods. A novel procedure for representing soft-robotic arms and their motion in a rigid-body simulation environment is proposed in this paper. The proposed procedure relies on the piecewise constant curvature approach to simplify the very complex model of hyper-redundant soft-robotic arms, making it suitable for real-time applications. The proposed method is implemented and verified to be used in model-mediated teleoperation of the soft arms of a biomimetic robotic squid designed for underwater manipulation as a case study.

In this study, we present a novel approach to generate limit cycle walking using nonlinear model predictive control (NMPC). Output-zeroing control is now widely used as a control method to limit cycle walking. This control offers strong feedback to the desired trajectory and the generation of energy-efficient and robust limit cycle walking. However, we observed that this method disables the natural dynamics of the robot, leading to problems regarding energy efficiency during walking. This study demonstrates that the energy consumption of walking using the output-zeroing control increases significantly in a disturbed environment. To overcome this limitation, the proposed approach leverages the robot’s dynamics using NMPC to achieve energy-efficient walking even in a disturbed environment. We demonstrate the practicality of the proposed method using two different simulation environments.

This paper presents a comprehensive strategy to improve the locomotion performance of humanoid robots on various slippery floors. The strategy involves the implementation and adaptation of a divergent component of motion (DCM) based control architecture for the humanoid NAO, and the introduction of an embedded yaw controller (EYC), which is based on a proportional-integral-derivative (PID) control algorithm. The EYC is designed not only to address the slip behavior of the robot on low-friction floors but also to tackle the issue of non-straight walking patterns that we observed in this humanoid, even on non-slippery floors. To fine-tune the PID gains for the EYC, a systematic trial-and-error approach is employed. We iteratively adjusted the P (Proportional), I (Integral), and D (Derivative) parameters while keeping the others fixed. This process allowed us to optimize the PID controller’s response to different walking conditions and floor types. A series of locomotion experiments are conducted in a simulated environment, where the humanoid step frequency and PID gains are varied for each type of floor. The effectiveness of the strategy is evaluated using metrics such as robot stability, energy consumption, and task duration. The results of the study demonstrate that the proposed approach significantly improves humanoid locomotion on different slippery floors, by enhancing stability and reducing energy consumption. The study has practical implications for designing more versatile and effective solutions for humanoid locomotion on challenging surfaces and highlights the adaptability of the existing controller for different humanoid robots.

Reinforcement learning (RL) has been successfully applied to a wealth of robot manipulation tasks and continuous control problems. However, it is still limited to industrial applications and suffers from three major challenges: sample inefficiency, real data collection, and the gap between simulator and reality. In this paper, we focus on the practical application of RL for robot assembly in the real world. We apply enlightenment learning to improve the proximal policy optimization, an on-policy model-free actor-critic reinforcement learning algorithm, to train an agent in Cartesian space using the proprioceptive information. We introduce enlightenment learning incorporated via pretraining, which is beneficial to reduce the cost of policy training and improve the effectiveness of the policy. A human-like assembly trajectory is generated through a two-step method with segmenting objects by locations and iterative closest point for pretraining. We also design a sim-to-real controller to correct the error while transferring to reality. We set up the environment in the MuJoCo simulator and demonstrated the proposed method on the recently established The National Institute of Standards and Technology (NIST) gear assembly benchmark. The paper introduces a unique framework that enables a robot to learn assembly tasks efficiently using limited real-world samples by leveraging simulations and visual demonstrations. The comparative experiment results indicate that our approach surpasses other baseline methods in terms of training speed, success rate, and efficiency.

In view of the fact that the current research on active and passive rehabilitation training of lower limbs is mainly based on the analysis of exoskeleton prototype and the lack of analysis of the actual movement law of limbs, the human-machine coupling dynamic characteristics for active rehabilitation training of lower limbs are studied. In this paper, the forward and inverse kinematics are solved on the basis of innovatively integrating the lower limb and rehabilitation prototype into a human-machine integration system and equivalent to a five-bar mechanism. According to the constraint relationship of hip joint, knee joint and ankle joint, the Lagrange dynamic equation and simulation model of five-bar mechanism under the constraint of human physiological joint motion are constructed, and the simulation problem of closed-loop five-bar mechanism is solved. The joint angle experimental system was built to carry out rehabilitation training experiments to analyze the relationship between lower limb error and height, weight and BMI, and then, a personalized training planning method suitable for people with different lower limb sizes was proposed. The reliability of the method is proved by experiments. Therefore, we can obtain the law of limb movement on the basis of traditional rehabilitation training, appropriately reduce the training speed or reduce the man-machine position distance and reduce the training speed or increase the man-machine distance to reduce the error to obtain the range of motion angle closer to the theory of hip joint and knee joint respectively, so as to achieve better rehabilitation.

Bag manipulation through robots is complex and challenging due to the deformability of the bag. Based on the dynamic manipulation strategy, we propose a new framework, ShakingBot, for the bagging tasks. ShakingBot utilizes a perception module to identify the key region of the plastic bag from arbitrary initial configurations. According to the segmentation, ShakingBot iteratively executes a novel set of actions, including Bag Adjustment, Dual-arm Shaking, and One-arm Holding, to open the bag. The dynamic action, Dual-arm Shaking, can effectively open the bag without the need to take into account the crumpled configuration. Then, the robot inserts the items and lifts the bag for transport. We perform our method on a dual-arm robot and achieve a success rate of 21/33 for inserting at least one item across various initial bag configurations. In this work, we demonstrate the performance of dynamic shaking action compared to the quasi-static manipulation in the bagging task. We also show that our method generalizes to variations despite the bag’s size, pattern, and color. Supplementary material is available at https://github.com/zhangxiaozhier/ShakingBot.

This paper presents a kinematics modeling and hybrid motion planning framework for wheeled-legged rovers. It is a unified solution for wheeled-legged rovers to traverse multiple challenging terrains using hybrid locomotion. A kinematic model is first established to describe the rover’s motions. Then, a hybrid motion planning framework is proposed to determine the rover’s gait patterns and parameterize the legs’ and the body’s trajectories. Furthermore, an optimization algorithm based on B-spline is utilized to minimize the motors’ energy dissipation and generate smooth trajectories. The wheeled and legged hybridization allows the rover for faster locomotion while maintaining high stability. Besides, it also improves the rover’s ability to overcome obstacles. Prototype experiments are carried out in more complex environments to verify the rover’s flexibility and maneuverability to traverse irregular terrains. The proposed algorithm reduces the swing amplitude by 83.3% compared to purely legged locomotion.

Most studies on path planning of robotic arm focus on obstacle avoidance at the end position of robotic arm, while ignoring the obstacle avoidance of robotic arm joint linkage, and the obstacle avoidance method has low flexibility and adaptability. This paper proposes a path obstacle avoidance algorithm for the overall 6-DOF robotic arm that is based on the improved A* algorithm and the artificial potential field method. In the first place, an improved A* algorithm is proposed to address the deficiencies of the conventional A* algorithm, such as a large number of search nodes and low computational efficiency, in robotic arm end path planning. The enhanced A* algorithm proposes a new node search strategy and local path optimization method, which significantly reduces the number of search nodes and enhances search efficiency. To achieve the manipulator joint rod avoiding obstacles, a method of robotic arm posture adjustment based on the artificial potential field method is proposed. The efficiency and environmental adaptability of the robotic arm path planning algorithm proposed in this paper are validated through three types of simulation analysis conducted in different environments. Finally, the AUBO-i10 robotic arm is used to conduct path avoidance tests. Experimental results demonstrate that the proposed method can make the manipulator move smoothly and effectively plan an obstacle-free path, proving the method’s viability.