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This paper addresses the problems of valve-turning operation in rescue environments where a wheeled mobile manipulator (WMM) is employed, including the possible occurrence of large internal forces. Rather than attempting to obtain the exact position of the valve, this paper presents a solution to two main problems in robotic valve-turning operations: the radial position deviation between the rotation axes of the tool and the valve handle, which may cause large radial forces, and the possible axial displacement of the valve handle as the valve turns, which may lead to large axial forces. For the former problem, we designed a compliant end-effector with a tolerance of approximately 3.5° (angle) and 9.7 mm (position), and provided a hybrid passive/active compliance method. For the latter problem, a passivity-based force tracking algorithm was employed. Combining the custom-built compliant end-effector and the passivity-based control method can significantly reduce both the radial and the axial forces. Additionally, for valves with different installation types and WMMs with different configurations, we analyzed the minimum required number of actuators for valve turning. Simulation and experimental results are presented to show the effectiveness of the proposed approach.

Flexible and safe object handling in modern industrial environments increasingly relies on mobile robotic systems capable of both dexterous manipulation and adaptive motion. However, when wheeled mobile manipulators (WMMs) operate under heavy or dynamically varying loads, challenges arise in maintaining sufficient force exertion capability and achieving stable coordination, particularly during cooperative transportation. In this paper, we present a unified framework to address these challenges with three main contributions. A quadratic-programming-based redundancy resolution scheme incorporating a load-capacity maximization metric is developed to explicitly enhance the force exertion capability of the system under heavy loads. A variable-admittance cooperative control strategy for dual-WMM transport is proposed to ensure synchronized motion and adaptive force regulation during collaborative manipulation. In addition, a unified framework that integrates configuration optimization with compliant cooperative control is established, enabling strict constraint enforcement, improved load capacity, and robust coordination between the two WMMs. Extensive simulations demonstrate the effectiveness of the proposed methods in improving load-handling performance and ensuring coordinated, compliant cooperative manipulation.

Accurate kinematic parameter identification is essential for space manipulators to attain millimeter-level positioning accuracy and robust motion control. This study develops a universal strategy for comprehensive parameter identification by establishing a generalized geometric error model using Denavit–Hartenberg (DH) parameterization. For robotic calibration, the Fibonacci spiral sampling technique optimizes pose selection, ensuring end-effector poses fully cover the manipulator’s workspace to enhance identification convergence. By combining the local convergence capability of the Levenberg–Marquardt (LM) algorithm with the global search characteristics of Particle Swarm Optimization (PSO), we propose a novel hybrid PSO-LM optimization algorithm, achieving synergistic enhancement of global exploration and local refinement. An experimental platform using a laser tracker as the metrology reference was constructed, with a 6-degree-of-freedom (6-DOF) space manipulator selected as a validation case. Experimental results demonstrate that the proposed method significantly reduces the average positioning error from 10.87 mm to 0.47 mm, achieving a 95.7% improvement in relative accuracy. These findings validate that the parameter identification approach can precisely determine the actual geometric parameters of space manipulators, providing critical technical support for high-precision on-orbit operations.

Healthcare systems are burdened by mobility impairments resulting from aging and neurological conditions. One of the recent advances in robotics is lower limb assistive/rehabilitative devices that can make independent living possible. Nonetheless, some limitations need to be addressed before robotics can be used in home‐based applications. This paper describes the current state of the art in intelligent motion planning and control of lower limb assistive devices, which have addressed some of these challenges. Adaptable central pattern generators and the divergent component of motion are introduced as methods for personalized motion planning based on physical human–robot interaction (pHRI). Uncertainty analysis for neural networks is introduced to increase safety in motion planning based on pHRI. For the case that a user cannot apply physical interaction, a reinforcement‐learning‐based approach is introduced to switch between different modes of walking based on the user's input via a push button embedded in a walker. Moreover, a smart walker is introduced as a device that can be synchronized with the lower limb exoskeleton to assist users with their daily activities. Also, a roadmap for future steps that can make lower limb assistive/rehabilitative devices a good fit for home use is introduced. Lower‐limb assistive devices can make independent life possible for people with neurological impairments if their associated issues can be addressed. This paper views the studies meant to resolve some of the challenges of intelligent motion planning and control of lower‐limb assistive devices. Also, it provides a roadmap for future studies that can help facilitate using these systems in home‐based assistance.

In order to improve the development effect of private education, this paper analyzes the current situation of private education combined with the data mining algorithm and explores the problems existing in the development of private education. Moreover, this paper combines the semi-parametric product estimation method with parameter estimation and applies the estimation method to model-assisted sampling estimation. This work enhances the estimate accuracy of the sample estimation and increases the field of application of the model while enhancing the classic generalized regression estimation. It also modifies the estimation accuracy on the basis of the linear assumption. The experimental study reveals that the data mining algorithm-based analysis approach for private education development provided in this work has a certain impact, and the development strategy of private education is assessed on this premise.

An accurate dynamic model of a robot is fundamentally important for a control system, while uncertainties residing in the model are inevitable in a physical robot system. The uncertainties can be categorized as internal disturbances and external disturbances in general. The former may include dynamic model errors and joint frictions, while the latter may include external payloads or human-exerted force to the robot. Disturbance observer is an important technique to estimate and compensate for the uncertainties of the dynamic model. Different types of disturbance observers have been developed to estimate the lumped uncertainties so far. In this paper, we conducted a brief survey on five typical types of observers from a perspective of practical implementation in a robot control system, including generalized momentum observer (GMO), joint velocity observer (JVOB), nonlinear disturbance observer (NDOB), disturbance Kalman filter (DKF), and extended state observer (ESO). First, we introduced the basics of each observer including equations and derivations. Two common types of disturbances are considered as two scenarios, that is, constant external disturbance and time-varying external disturbance. Then, the observers are separately implemented in each of the two simulated scenarios, and the disturbance tracking performance of each observer is presented while their performance in the same scenario has also been compared in the same figure. Finally, the main features and possible behaviors of each type of observer are summarized and discussed. This survey is devoted to helping readers learn the basic expressions of five typical observers and implement them in a robot control system.

PurposeThe purpose of this paper is to enable autonomous door-opening with unknown geometrical constraints. Door-opening is a common action needed for mobile manipulators to perform rescue operation. However, it remains difficult for them to handle it in real rescue environments. The major difficulties of rescue manipulation involve contradiction between unknown geometrical constraints and limited sensors because of extreme physical constraints.Design/methodology/approachA method for estimating the unknown door geometrical parameters using coordinate transformation of the end-effector with visual teleoperation assists is proposed. A trajectory planning algorithm is developed using geometrical parameters from the proposed method.FindingsThe relevant experiments are also conducted using a manipulator suited to extreme physical constraints to open a real door with a locked latch and unknown geometrical parameters, which demonstrates the validity and efficiency of the proposed approach.Originality/valueThis is a novel method for estimating the unknown door geometrical parameters with coordinate transformation of the end-effector through visual teleoperation assists.

Problem solving is considered one of the most critical competencies for life. However, many students may perform well in school yet cannot transfer the skills they learned in school to solve real-world problems, especially in science education. This study discusses the characteristics of the problem from the physics education perspective, based on Problem Representation Theory, and proposes a new type of problem: The Primitive Physics Problem. Moreover, the significance and contribution in promoting students' physics problem-solving ability, both theoretical and empirical, are described. Then the study develops a set of instruments that was tested on 811 students in 12th grade from 10 upper-secondary schools in China. The results showed that the new instrument had good reliability and validity. The promising application in the instruction of the Primitive Physics Problem and in measuring upper-secondary school students' problem-solving ability of the instrument is discussed.

The efficient use of natural gas will require catalysts that can activate the first C–H bond of methane while suppressing complete dehydrogenation and avoiding overoxidation. We report that single iron sites embedded in a silica matrix enable direct, nonoxidative conversion of methane, exclusively to ethylene and aromatics. The reaction is initiated by catalytic generation of methyl radicals, followed by a series of gas-phase reactions. The absence of adjacent iron sites prevents catalytic C-C coupling, further oligomerization, and hence, coke deposition. At 136B kelvin, methane conversion reached a maximum at 48.1% and ethylene selectivity peaked at 48.4%, whereas the total hydrocarbon selectivity exceeded 99%, representing an atom-economical transformation process of methane. The lattice-confined single iron sites delivered stable performance, with no deactivation observed during a 60-hour test.

An active robust adaptive fault-tolerant control protocol is studied for reducing vibration of crane bridge system and handling actuator faults and output constraints simultaneously based on a partial differential equation model. The closed-loop system subject to environmental perturbations and actuator failures can be stabilized with proposed control laws. Furthermore, output constraints of trolley can always be ensured via employing barrier Lyapunov function (BLF), and uncertain actuator faults can also be compensated availably using developed adaptive control laws without any knowledge of actuator fault information. Finally, numerical simulation is provided for illustrating performance of the proposed control method.