In order to transport materials flexibly and smoothly in a tight plant environment, an omni-directional mobile robot based on four Mecanum wheels was designed. The mechanical system of the mobile robot is made up of three separable layers so as to simplify its combination and reorganization. Each modularized wheel was installed on a vertical suspension mechanism, which ensures the moving stability and keeps the distances of four wheels invariable. The control system consists of two-level controllers that implement motion control and multi-sensor data processing, respectively. In order to make the mobile robot navigate in an unknown semi-structured indoor environment, the data from a Kinect visual sensor and four wheel encoders were fused to localize the mobile robot using an extended Kalman filter with specific processing. Finally, the mobile robot was integrated in an intelligent manufacturing system for material conveying. Experimental results show that the omni-directional mobile robot can move stably and autonomously in an indoor environment and in industrial fields.

Compared with traditional speed regulation (SR) approaches like variable frequency and hydraulic coupling, magnetorheological clutch (MRC) provides a more superior solution for high-efficiency energy saving SR. However, recent developments have demonstrated that severe heating is an outstanding challenge for MRC, especially in high-power applications. Among commonly used cooling methods, liquid cooling offers a viable alternative for the problem. Aiming at pre-evaluating the cooling efficiency of a liquid-cooled MRC in high-power situations, this study introduces a heat-flow coupling simulation method. In this paper, theoretical basis for the simulation is presented first, which is followed by an illustration of the heat-flow coupling simulation. This paper details the simulation model establishment, finite element meshing (FEM), boundary conditions, and simulation parameters. After the simulations, the results concerning the steady flow field of the internal coolant, along with the steady-state temperature fields of MRC, magnetorheological (MR) fluids and the coolant are presented and discussed. Finally, several heating tests of an MRC prototype under various operation conditions are performed and the results verify the correctness and rationality of the simulation.

The ability to sense external force is an important technique for force feedback, haptics and safe interaction control in minimally-invasive surgical robots (MISRs). Moreover, this ability plays a significant role in the restricting refined surgical operations. The wrist joints of surgical robot end-effectors are usually actuated by several long-distance wire cables. Its two forceps are each actuated by two cables. The scope of force sensing includes multidimensional external force and one-dimensional clamping force. This paper focuses on one-dimensional clamping force sensing method that do not require any internal force sensor integrated in the end-effector’s forceps. A new clamping force estimation method is proposed based on a joint torque disturbance observer (JTDO) for a cable-driven surgical robot end-effector. The JTDO essentially considers the variations in cable tension between the actual cable tension and the estimated cable tension using a Particle Swarm Optimization Back Propagation Neural Network (PSO-BPNN) under free motion. Furthermore, a clamping force estimator is proposed based on the forceps’ JTDO and their mechanical relations. According to comparative analyses in experimental studies, the detection resolutions of collision force and clamping force were 0.11 N. The experimental results verify the feasibility and effectiveness of the proposed clamping force sensing method.

This paper illustrates the development and experimental validation of a robotic ankle–foot orthosis (AFO) with a series elastic actuator (SEA) and a magneto-rheological (MR) brake. First, the biomechanics of a human ankle joint during walking is explained. Next, the hardware design of the robotic AFO is introduced, including its mechanical structure, actuator design and configuration, and electronic system. The SEA is primarily composed of an electric motor, a planetary gearbox, a torsion spring, and a pair of bevel gears. The MR brake can modulate the viscosity of the robotic AFO and generate a large braking torque of 21.8 Nm with a low power of 8.8 W. Additionally, the modeling of the robotic AFO is presented, followed by an introduction to its control; several gait evaluation indices are proposed as well. Finally, a pilot study is conducted to verify the effectiveness of the developed robotic AFO. The experimental results demonstrate that the robotic AFO has the potential to provide dorsiflexion assistance, thus preventing foot slap and toe drag, in addition to plantarflexion assistance for the forward propulsion of the body. During a gait cycle, an average power of 0.23 W is harvested, and an 8% improvement in the system energy efficiency is achieved.

This paper illustrates the design and testing of an upper-body exoskeleton for the assistance of individuals with load-lifting and load-carrying tasks, and the provided assistive force can well match with the force required by the human. First, the biomechanics of the human lumbar during the squat lifting of an object is described. Next, the modeling of the exoskeleton is introduced. Additionally, the hardware design of the exoskeleton is presented. The exoskeleton is mainly composed of a back-assist mechanism and an upper extremity labor-saving mechanism, which can assist the wearer’s lumbar during the squat lifting of an object and assist the wearer’s arms to carry an object during walking, respectively. Finally, experiments are conducted to evaluate the performance of the developed upper-body exoskeleton. The experimental results demonstrate that the exoskeleton has the potential to provide assistance for individuals with manual handling tasks. An average assistive force of 44.8 N can be provided for the wearer to lift a 10-kg object. During the squat lifting of the 10-kg object, reductions of 31.86% and 28.30% of the average muscle activities of the wearer’s lumbar erector spinae and thoracic erector spinae are observed, respectively. In addition, a reduction of 23.78% of the average muscle activity of the wearer’s biceps brachii is observed during walking while carrying the 10-kg object.

In unstructured multi-object scenes, unclear target regions and interference from backgrounds and adjacent objects can affect grasp point selection. To address these problems, this paper proposes a visual grasping method for collaborative robots based on improved object detection and grasp pose estimation networks. In the object detection stage, the corresponding convolution and upsampling modules in YOLOv8n are replaced with RFAConv and DySample, respectively, to improve the detection of target boundaries and local features. In the grasp pose estimation stage, a residual attention structure and a region-guidance mechanism are incorporated into GR-ConvNet to enhance the stability of grasp prediction in complex backgrounds. To further reduce interference from non-target regions, the detected bounding box is used as a bounding box prompt for MobileSAM to generate a target mask. The target mask is then used to constrain the grasp quality map output by the improved GR-ConvNet and restrict grasp point search to the target region. The proposed method is validated on a collaborative robot visual grasping experimental platform using RGB-D images as input. Experimental results show a target detection success rate of 98 % and an overall grasp success rate of 95 %. The proposed method reduces interference from non-target regions during grasp point selection and improves the stability of target grasping in unstructured multi-object scenes.