To address the constraints of narrow surgical corridors and limited workspace in pituitary tumor procedures, this paper presents a compact and modular minimally invasive surgical robot. The system adopts a master-slave architecture in which all degrees of freedom are controlled via a remote operating platform. Three 5 mm end effectors are coordinated through a 12 mm diameter channel, enabling localized collaborative manipulation while minimizing tissue interference. The robot comprises an integrated lifting-rotating platform, one endoscope module, and two surgical modules arranged vertically in a staggered configuration. Each surgical module provides five degrees of freedom-vertical translation, axial rotation, lateral oscillation, pitch, and grasping-through a cable-driven ball-joint mechanism to achieve high dexterity within confined spaces. The endoscope module offers vertical translation and axial rotation for intraoperative visualization. The lifting-rotating platform synchronizes axial and vertical motions of all modules to ensure coordinated operation and flexible instrument repositioning. Structural design and kinematic modeling are presented, followed by prototype development and experimental validation. Performance tests, including positioning accuracy and cooperative manipulation, demonstrate high precision and operational flexibility, confirming the feasibility of the proposed system for minimally invasive pituitary surgery.

This paper proposes a hierarchical control architecture that is established at both lower limb joint and platform levels based on a simplified admittance model, where compliance is regulated through virtual mass and damping. At the lower limb joint level, admittance control governs lower limb motion tracking, while at the platform level it adjusts the omnidirectional mobile platform velocity in response to human interaction forces. Within this framework, a Sarsa-based reinforcement learning agent dynamically optimizes the parameters of a Sigmoid function using dual state inputs. Based on hip joint angle error and human–robot interaction force, the controller dynamically adjusts virtual mass and damping to optimize the trade-off between tracking error and dynamic compliance. The simulation and experimental results on the prototype system demonstrate that, compared with traditional Sigmoid parameter-tuned admittance control, the proposed approach significantly enhances gait smoothness (dimensionless squared jerk reduced by 65.62% and 36.74% for hip and knee joints), and increases human–robot interaction compliance (RMS interaction force was reduced from 3.2502 N to 2.5109 N; EPUD decreased from 12.14 to 9.53). Moreover, the proposed strategy achieves smaller maximum overshoot (0.45° vs. 0.9°) and faster settling time (2.6 s vs. 4.59 s). These findings indicate that integrating reinforcement learning with Sigmoid parameter adaptation provides a systematic and effective solution for adaptive compliance regulation in mobile exoskeleton systems, enhancing adaptability, safety, and functional relevance for stroke patients undergoing lower limb rehabilitation training.

Traditional dexterous hands can readily grasp objects but face limitations in dexterous manipulation due to complex control systems and high actuation demands. This paper presents a novel dexterous hand designed to address these challenges. The hand consists of four fingers, each equipped with two mecanum wheels at the fingertips to allow for the omnidirectional manipulation of objects. Continuous rotation of the mecanum wheels enables unbounded motion of grasped objects without the need for finger gaiting. Object pose adjustment is achieved by controlling the rotation of mecanum wheels, thus significantly reducing operational complexity and enhancing manipulative agility. Furthermore, to address the control difficulty of multi-finger coordinated motion, a four-finger coupled mechanism is implemented, resulting in a dexterous hand with three degrees of freedom. Kinematic models of omnidirectional manipulation are established for typical geometric objects, including a flat plate, a cuboid, a sphere, and a cylinder. Simulations confirm the correctness of the kinematic models. Experimental results show that the hand can achieve omnidirectional manipulation of objects. Finally, the extended functionality of the dexterous hand is briefly presented, which allows it to be reconfigured into an omnidirectional mobile robot.

Purpose The need for compliant control of dexterous hands as end-effectors in environmental interactions is increasingly emphasized. Traditional compliant control methods are often characterized by significant grasping force errors and poor adaptability. To address the constant force control requirements for object grasping and posture adjustment for the omnidirectional dexterous hand (ODH), this paper aims to propose a nonlinear disturbance observer-based adaptive admittance control (NDOAAC) method. Design/methodology/approach Based on the established kinematic model, an admittance control framework is constructed. A gradient descent-based adaptation law is designed to continuously estimate environmental stiffness and dynamically modify the reference trajectory. A nonlinear disturbance observer is incorporated to aggregate unmodeled dynamics and external disturbances as composite disturbances. The parameters of the admittance model are then regulated according to the observed values. The stability of the proposed control system is theoretically demonstrated. Findings The superiority of the NDOAAC method is validated through comparative simulations with Classical Admittance Control (CAC) and Adaptive Admittance Control (AAC), while its effectiveness is confirmed via physical experiments. Results demonstrate that the NDOAAC strategy achieves precise tracking of desired grasping forces, improves system response speed and maintains excellent adaptability to objects with varying stiffness levels. Originality/value The NDOAAC strategy is proposed in this paper, which effectively fulfills the requirements of grasping tasks and provides a viable solution for compliant manipulation with the ODH.

Cable-driven minimally invasive surgical robots suffer from significant motion inaccuracies due to nonlinear transmission effects such as friction, elasticity, and hysteresis. These factors lead to strong nonlinear and direction-dependent behaviors, making accurate modeling and compensation challenging. To address this issue, this study investigates the error characteristics of a cable-driven surgical robot prototype based on its structural features. A kinematic model is first established, and geometric errors are corrected through Denavit–Hartenberg (DH) parameter identification using a least-squares method. To further characterize nonlinear effects, the LuGre friction model and equivalent stiffness theory are introduced to analyze friction and cable deformation behaviors. Since physics-based models alone cannot accurately capture the coupled nonlinear errors, a radial basis function (RBF) neural network is employed to approximate the residual errors. To enable real-time implementation, the predicted errors are further simplified using equivalent polynomial functions for efficient compensation. Experimental results demonstrate that the proposed method significantly improves the motion accuracy of the cable-driven system, effectively reducing both tracking error and hysteresis effects. By integrating mechanism-based modeling with data-driven compensation, this approach provides a practical and effective solution for precision enhancement in cable-driven surgical robotic systems.

This article presents an adaptive patient-cooperative control strategy and implements it on an originally designed lower limb rehabilitation robot. The robot structure comprises a lower limb exoskeleton and an omnidirectional mobile platform, and this design enables it to provide omnidirectional overground gait training for hemiplegic patients. The kinematic models of the lower limb exoskeleton, the omnidirectional mobile platform, and their movement coordination that can be used for robot control are established. To enhance patients' active participation during rehabilitation training, a patient-cooperative control strategy consisting of an outer-loop with an admittance controller and an inner-loop with a velocity controller is proposed, and an adaptive law is designed to adjust admittance parameters based on the exoskeleton joint angle and human-robot interaction force. Experiments involving five healthy subjects are conducted, and the results demonstrate that the proposed control strategy exhibits excellent trajectory tracking performance and can adaptively adjust the robot's gait speed according to the subject's motion state and intention, which indicates significant potential in improving rehabilitation effect.

This paper presents a robotic structure that combines modular design with low energy consumption and a large workspace, suitable for both mobile and fixed platforms. It primarily consists of pitch joint modules and yaw joint modules. The yaw joint module employs a novel single-motor dual-axis configuration design, which effectively increases the yaw range and expands the robot’s workspace compared to traditional yaw joint structures. The pitch joint module is designed based on a parallelogram structure and is diagonally driven by an electric cylinder. By incorporating a spring installed in parallel with the electric cylinder, a Parallel Elastic Actuator is formed, achieving the goal of low energy consumption. Comparative analysis demonstrates that the proposed robot has certain advantages in terms of workspace and energy consumption for the pitch joint module. A robot prototype was constructed and tested, and the results indicate that the robot can successfully complete target grasping tasks, the yaw joint can achieve large-angle yaw movements, and the energy consumption of the new pitch joint module is reduced by 47.02%.

Aimed at the technical challenge of low water vapor collection efficiency in the in-situ thermal extraction of lunar polar water ice, this study, based on the heat and mass transfer characteristics of frost layer porous media, proposes a solution to enhance the gas-solid phase transition rate by optimizing the internal surface area of the water vapor collection unit. Through the establishment of a water vapor condensation dynamics model under vacuum and low-temperature conditions, the coupling effects of frost layer porosity and temperature field on the mass transfer process are revealed. A verification platform simulating the extreme lunar surface environment was constructed, and comparative experiments on different aluminum bead filling structures were conducted. The results indicate that the larger the internal surface area of the device, the higher the water vapor collection efficiency; the use of small-diameter aluminum beads significantly increases the internal surface area, but excessively small diameters may lead to pore blockage. The initial stage of water vapor collection is one of the key stages of mass transfer, where water vapor first comes into contact with the inner surface of the collection structure and undergoes a gas-solid phase change that affects the continued performance of the collection device. This research provides theoretical and experimental foundations for the design of water vapor collection units in in-situ lunar water ice extraction devices.

Under the extremely low-temperature environment of permanent shadow crater in the lunar polar region, the hardness, mechanical strength, and bonding degree of lunar soil water ice are high, and it is difficult to realize low-consumption and high-efficiency drill by traditional digging shovels and drilling. To solve the problem, an ultrasonic longitudinal-torsional composite drill is proposed. The longitudinal-torsional vibration coupling mechanism of the slant groove is analyzed, and the modal analysis, harmonic response analysis and transient analysis of the ultrasonic drill are carried out by the method of finite element. The experiment results show that the designed ultrasonic longitudinal-torsional composite drill can effectively generate longitudinal-torsional composite vibration, which provides a new technical means for the drill of lunar soil water ice.

Omnidirectional mobile robots have gained extensive application across diverse fields due to their exceptional maneuverability and adaptability in confined spaces. However, structural and systemic uncertainties significantly compromise motion accuracy. To enhance motion control precision, this paper proposes a sliding mode control (SMC) method integrated with a radial basis function (RBF) neural network. The approach aggregates model uncertainties, nonlinear dynamics, and unknown disturbances into a composite disturbance term. An RBF neural network is employed to approximate this disturbance, with compensation embedded within the SMC framework. An online adaptive law for neural network optimization is derived using the Lyapunov stability theorem, thereby improving the disturbance rejection capability. Comparative simulations and experiments validate the proposed method against modern control strategies. Results demonstrate superior tracking performance and robustness, significantly enhancing trajectory tracking accuracy for the MY3 wheeled omnidirectional mobile robot.

PurposeUpper limb rehabilitation robots can substitute traditional practitioners in training programs, effectively alleviating the shortage of rehabilitation professionals in the market. However, human-robot interactions during rehabilitation tasks often introduce disturbances, degrading the performance of these robots. This paper proposes a linear active disturbance rejection control (LADRC-SMC) strategy, based on sliding mode control, to improve trajectory tracking performance of a desktop upper limb rehabilitation robot (DULRR).Design/methodology/approachBased on the basic mechanical structure and drive characteristics of DULRR, the dynamic model of the system is established. A linear expanded state observer (LESO) estimates the total perturbation within the LADRC framework, and a sliding mode control (SMC) based control law is designed to eliminate observer bandwidth limitations, thereby enhancing dynamic system performance.FindingsThree controlled experiments are conducted to demonstrate the algorithm's performance. Experimental results align with simulations, showing that the proposed LADRC-SMC overcomes bandwidth limitations, offering better disturbance rejection and tracking performance than PID and LADRC.Originality/valueThe LADRC-SMC method is proposed to enhance the performance of LADRC by integrating SMC. This method improves the disturbance rejection capability of DULRR in rehabilitation tasks, laying the foundation for its future applications.

Abstract At present, the grain storage depot has introduced the warehouse closing vehicle to replace the manual grain surface leveling operation. Aiming at the problems of subsidence, vibration and slipping when the warehouse car walks on the grain surface, the multi-body dynamics simulation is carried out for the four complex working conditions and load conditions of the car body in the grain depot environment, including straight running, turning, tilting and obstacle crossing, and its driving dynamic characteristics are analyzed. It can be seen from the simulation results that in the turning condition, the required driving force is the largest, the sliding condition is the most serious, the sinking degree is the deepest, and the stability is the worst. The driving dynamics of the clearance car will change suddenly when entering and leaving the pit under the obstacle crossing condition. Through the dynamic simulation results, the change law of the driving dynamic characteristics of the clearance car is summarized, and the vibration damping performance, adhesion performance and Through performance and work efficiency, it provides a theoretical basis for vehicle body design and optimization.

As important auxiliary equipment, rehabilitation robots are widely used in rehabilitation treatment and daily life assistance. The rehabilitation robot proposed in this paper is mainly composed of an omnidirectional mobile platform module, a lower limb exoskeleton module, and a support module. According to the characteristics of the robot’s omnidirectional mobility and good stiffness, the overall kinematic model of the robot is established using the analytical method. Passive and active training control strategies for an omnidirectional mobile lower limb exoskeleton robot are proposed. The passive training mode facilitates the realization of the goal of walking guidance and assistance to the human lower limb. The active training mode can realize the cooperative movement between the robot and the human through the admittance controller and the tension sensor and enhance the active participation of the patient. In the simulation experiment, a set of optimal admittance parameters was obtained, and the parameters were substituted into the controller for the prototype experiment. The experimental results show that the admittance-controlled rehabilitation robot can perceive the patient’s motion intention and realize the two walking training modes. In summary, the passive and active training control strategies based on admittance control proposed in this paper achieve the expected purpose and effectively improve the patient’s active rehabilitation willingness and rehabilitation effect.

Snake-like robots have a slender body and strong environmental adaptability. This paper aims to propose an omni-tread snake-like robot that can adapt to the needs of specific narrow space exploration missions and provide a basis for design work through necessary motion analysis. The robot adopts the form of three modules in series, and the modules are connected through differential driving joints, which increases the joint torque of the robot. The nested omni-tread structure improves the robot's motion efficiency and environmental adaptability. This paper conducts configuration and motion analysis of the robot. The simulation is performed using the optimization solution method to obtain the joint torque and walking torque of the robot, which guides the design of the robot. The experiment is also conducted with the developed prototype to verify the robot's performance. From the simulation results, the joint torque and walking torque are obtained, and the motors are selected. The motion performance and field applicability of the robot are verified through experiment tests. The experiment results further verify the robot design and analysis work. The structural design of robots and optimization solution method in this paper has certain reference values for other researchers.

Aimed at the problem of human–machine interaction between patients and robots in the process of using rehabilitation robots for rehabilitation training, this paper proposes a human–machine interactive control method based on an independently developed upper limb rehabilitation robot. In this method, the camera is used as a sensor, the human skeleton model is used to analyse the moving image, and the key points of the human body are extracted. Then, the three-dimensional coordinates of the key points of the human arm are extracted by depth estimation and spatial geometry, and then the real-time motion data are obtained, and the control instructions of the robot are generated from it to realise the real-time interactive control of the robot. This method can not only improve the adaptability of the system to individual patient differences, but also improve the robustness of the system, which is less affected by environmental changes. The experimental results show that this method can realise real-time control of the rehabilitation robot, and that the robot assists the patient to complete the action with high accuracy. The results show that this control method is effective and can be applied to the fields of robot control and robot-assisted rehabilitation training.

This paper presents the design, analysis, and development of a novel six degrees-of-freedom (6DOF) desktop upper limb rehabilitation robot. The upper limb rehabilitation robot is mainly composed of the omnidirectional mobile platform, armrest, and 3DOF wrist rehabilitation mechanism. The forward and inverse kinematics and Jacobian matrix of the upper limb rehabilitation robot are derived based on the kinematics of a rigid body, and its working space is also analyzed based on arm kinematics. The forward and inverse kinematics of the arm are derived based on the D-H method. A new control strategy and algorithm were developed based on the robot system's hardware structure and arm model. These were employed in simulated rehabilitation experiments on both a single joint and multiple joint linkages. The experimental results indicate that the maximum error for single-joint rehabilitation is 6.123 deg, while for multiple joint linkages, it is 5.323 deg. Therefore, the control strategy and control algorithm can complete the corresponding rehabilitation training. This 6DOF desktop upper limb rehabilitation robot can provide passive rehabilitation training for patients in the early stages of paralysis.

This paper proposes a new type of omnidirectional mobile robot for land and air, which has three motion modes, combines the motion characteristics of land motion and air flight, has the ability to climb walls, and can be actively deformed to adapt to the working conditions according to the current working environment. The robot incorporates an innovative “rotor blade–single row omnidirectional wheel” composite structure, which is mainly characterized by a single row of continuous switching wheels covering the outside of each rotor blade, and does not need to provide additional power when moving on the ground and walls, relying on the driving force generated by the rotor blades to drive the continuous switching wheels driven by the rotor blades. This structure can effectively combine the land movement mode, wall crawling mode, and air flight mode, which reduces the energy consumption of the robot without increasing the weight, and we design a deformation device that can realize the transformation of the three modes into each other. This paper mainly focuses on the design of the robot structure and the analysis of the movement method, and the land omnidirectional movement experiments, wall crawling experiments, and air flight experiments were, respectively, carried out, and the results show that the proposed land and air omnidirectional mobile robot has the ability to adapt to the movement of each scene, and improves the upper limit of the robot’s operation.

机械专业的毕业设计是专业培养计划中最后一个教学实践环节,也是培养学生工程实践能力、融合创新能力、应用理论能力的重要环节.文章针对机械专业毕业设计的教学改革背景进行了分析,提出了实施方案.基于产教融合,建议建立三指导教师制度,通过加强过程管理,提高指导教师的综合能力,建立可持续循环的毕业设计教学改革监控机制,形成产教融合的毕业设计培养和过程管理机制,提高毕业设计质量和学生解决工程问题的能力,达到培养"卓越工程师"的教学目的.

A novel omni-directional mobile robot for lower limb rehabilitation is proposed for the needs of stroke patients. The robot consists of an omni-directional mobile platform equipped with MY3 wheels, a lower limb joint module and a hip support module for rehabilitation training and assisted mobility. Parameters of the normal walking gait pattern are evaluated and obtained. The omni-directional mobile platform was designed based on the MY3 wheel modules. The lower limb joint module is designed in combination with cam and synchronous belt mechanism to achieve multi-degree of freedom movement with a single drive, which can assist patients in executing normal gait during general training. The overall kinematic model of the robot is developed for motion control by applying the velocity equation and the D-H method. The prototype of robot is developed and experiments are conducted on unloaded operation and manned linear trajectory walking. Experimental results show that the robot can achieve a simple mechanism to guide the movement of the lower limb in the desired gait pattern. The study demonstrates the concept and feasibility of this new lower extremity gait rehabilitation robot.

The softness and flexibility of soft robots give them strong adaptability, enabling them to move and perform tasks in narrow and irregular environments. But in flatter environments, their mobility has a large gap with wheeled robots. This paper addresses the issue of low mobility of soft robots in flat environments, and proposes a new type of leg-wheel hybrid mechanism inspired by bacterial flagella. The structure uses a tendon-driven continuum structure as the soft body part, just like the flagellar filaments of bacteria; the wheel motion is realized by driving the soft body part for continuous turn-over, just like the rotational motion of bacterial flagellar filaments driven by its base motor. In this paper, the design and kinematic analysis of the structure are presented in detail. To verify the feasibility of the structure, a prototype is implemented. The experiments are conducted on the motion and load capacity of the structure in both wheel mode and leg mode. The experimental results show the viability of the structure.