Multilateral tele-robotic system have potential applications in the industry environments with the advantages of cooperative manipulation for the remote and hazardous tasks, and its control design is quite challenging due to several coupling issues such as stability, position tracking, force feedback and cooperative manipulation under time delays, various uncertainties and external disturbance. In this paper, a novel RBFNN ( $Radial\ Basis\ Function\ Neural\ Network$ )-based adaptive sliding mode control design is proposed for nonlinear multilateral tele-robotic system with $n$ -master- $n$ -slave manipulators. The environment force is modeled with a general form via the RBFNN-based environment parameters estimation in the slave side. The estimated environment parameters (non-power signals) are transmitted to rebuild the environment dynamics in the master side and provide the good force feedback for the human operators. The RBFNN-based adaptive sliding mode controllers are designed separately for master and slave manipulators to achieve good position tracking under parameter variations and external disturbance. The coordinated force distribution algorithm is designed to achieve cooperative manipulation with the balance of force acting on the target object. The theoretical analysis is given and the comparative experiment for a nonlinear multilateral tele-robotic system with 2-master-2-slave manipulators is implemented. The results show the good performance of our design.

Robotic object packing has broad practical applications in the logistics and automation industry, often formulated by researchers as the online 3D Bin Packing Problem (3D-BPP). However, existing DRL-based methods primarily focus on enhancing performance in limited packing environments while neglecting the ability to generalize across multiple environments characterized by different bin dimensions. To this end, we propose GOPT, a g eneralizable o nline 3D Bin P acking approach via T ransformer-based deep reinforcement learning (DRL). First, we design a Placement Generator module to yield finite sub-spaces as placement candidates and the representation of the bin. Second, we propose a Packing Transformer, which fuses the features of the items and bin, to identify the spatial correlation between the item to be packed and available sub-spaces within the bin. Coupling these two components enables GOPT's ability to perform inference on bins of varying dimensions. We conduct extensive experiments and demonstrate that GOPT not only achieves superior performance against the baselines, but also exhibits excellent generalization capabilities. Furthermore, the deployment with a robot showcases the practical applicability of our method in the real world.

This paper focuses on the electromagnetic-thermal coupled design optimization of a yokeless and segmented armature (YASA) Halbach-array axial flux permanent magnet (AFPM) machine for direct-drive automated guided vehicles (AGVs). According to the application condition, extra high torque density is required with the constraints of natural-cooling and compact size. The machine topology with Halbach-array permanent magnets (PMs) and segmented stator is firstly introduced, and different slot/pole combinations are compared. Then, comprehensive sensitivity analysis is carried out, and the electromagnetic-thermal coupled design optimization consisting of rough and refined processes is carried out based on a newly established thermal network model. Subsequently, the electromagnetic performance of the optimized machine is evaluated and compared with that of an optimized surface-mounted PM YASA (SPM-YASA) benchmark with the same envelop dimension by three-dimensional (3D) finite-element (FE) method. The mechanical structure of the investigated machine with specially-designed heat-transmission mechanism is introduced, and the mechanical strength analysis is carried out. Finally, a machine prototype is manufactured and tested to validate the FE analyses.

The mechanical and tribological behavior of eccentric bearings is crucial for the performance of a RV reducer. By combining the finite element model (FEM) and the elastohydrodynamic lubrication (EHL) method, a comprehensive model for the cylindrical roller bearings applied in the RV reducer is developed in this study. During the modeling phase, FEM is utilized to determine the bearing load, taking into account the structural flexibility. The FEM result demonstrates that a 15% increase in the maximum bearing’s load is detected by the FEM compared to the analytical model. After the simulations, the effects of the roller profile modification, the bearing load and the rolling speed on the bearing performance are revealed. The numerical results indicate that the combined generatrix shape roller results in weaker edge effects and stress concentration compared with that of the straight generatrix shape roller and the arc generatrix shape roller. The optimal values of modification length and modification quantity under various load and rolling speed conditions are provided. Furthermore, durability tests on RV reducers equipped with the three types of rollers were conducted. Experimental outcomes demonstrate that the combined generatrix shape roller significantly improves the reliability and fatigue life of the RV reducer, corroborating the numerical analyses.

Integrating an exoskeleton as the external apparatus for a brain–machine interface (BMI) has the advantage of providing multiple contact points to determine body segment postures and allowing control to and feedback from each joint. When using macaques as subjects to study the neural control of movement, an upper limb exoskeleton design with unlikely singularity is required to guarantee safe and accurate tracking of joint angles over all possible range of motion (ROM). Additionally, the compactness of the design is of more importance considering macaques have significantly smaller body dimensions than humans. This paper proposes a six degree-of-freedom (DOF) passive upper limb exoskeleton with 4DOFs at the shoulder complex. System kinematic analysis is investigated in terms of its singularity and manipulability. A real-time data acquisition system is set up, and system kinematic calibration is conducted. The effectiveness of the proposed exoskeleton system is finally demonstrated by a pilot animal test in the scenario of a reach and grasp task.