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The redundancy feature of manipulators provides the possibility for the fault tolerant trajectory planning. Aiming at the completion of the specific task, an algorithm of global fault tolerant trajectory optimization for redundant manipulator based on the self-motion is proposed in this paper. Firstly, inverse kinematics equation of single redundancy manipulator based on self-motion variable and null-space velocity array of Jacobian are analyzed. Secondly, the mathematical description of fault tolerance criteria of the configuration of manipulator is established and the fault tolerance configuration group of manipulator is obtained by using iteration traversal under the fault tolerance criteria. Then, considering the joint limits and minimum the energy consumption as the optimization target, the global fault tolerant joint trajectory is achieved. Finally, simulation for 7 degree of freedom (DOF) manipulator is performed, by which the effectiveness of the algorithm is validated.

Low-speed crawling phenomenon may occur when space manipulators run at a low speed, which may bring big problem for manipulation work. A kind of low-speed optimization algorithm based on gradient projection method is pro-posed in this paper. The designing of continuous balanced proportional factor can effectively reduce the quantitative differences between the homogeneous solution and the special solution, avoiding the joint velocities oscillations. The low-speed crawling problem can be effectively improved by appropriate increase of joint velocities. And the effectiveness and correctness of the optimization algorithm are confirmed by the simulation.

In order to solve the problem of operating precision analysis considering multiple error sources of serial robots, an operating precision analysis method combined Monte Carlo algorithm with pose error model of robot end-effector is proposed. Firstly, the pose error model of robot end-effector is built based on the MCPC method. Then, the error sources which have a main influence on the operating precision of robot end-effector are analysed in detail. At last, the Monte Carlo algorithm is used to analyse the error probabilistic characteristics of robot end-effector considering multiple error sources, which can validate the precision design of mechanical arms and provide theory basis to distribute the error limits of error sources reasonably.

Accurately grasping objects with varying levels of hardness and softness is a challenging task. Current robotic grasping methods rely solely on visual to determine the grasping posture, without considering the grasping force of different objects, which might increase the risk of slippage or damage. For the purpose of realize the improvement and supplementation of visual information from the dimension of force, several robots include tactile sensors installed at fingertips. In this paper, we propose a method that integrates visual and tactile information to accurately determine the 6D posture and force for robot grasping. The main challenges of this research include (1) grasping representation of visual and tactile data from heterogeneous sources, (2) Construction of the implicit link between grasping posture and clutching force, (3) a visual–tactile training dataset. To address these challenges, we first use a grasping quality assessment module to filter the grasping posture and a transformer-depth separation convolution module to predict the grasping force; second, we propose a diffusion model with multi-stage convolution module to generate the optimal grasping. In addition, we created a visual–tactile grasping data representation form that included the grasping quality of grasping postures, and matching tactile data for objects of 8 distinct hardness. Experiments show that our method achieves a 98% success rate and decreases grasping force redundancy by 78% compared to purely visual methods,which significantly improves the accuracy and stability of the robot grasping. [ABSTRACT FROM AUTHOR]

For the system containing multiple adjustable degree-of-freedom modular units, a mathematical description of the modular robot topology including a standardized process and a kinematics parallel modeling method are proposed. Firstly, a library of the modular unit motion transfer is established, and a mathematical description of the modular robot topology based on the quadruple matrix with the standardized process is proposed. Then, based on the kinematics model of one modular unit, considering the coupling effect between the branches of the modular robot with multiple motion branches, a position level and speed-level kinematics model is established, which is suitable for chain modular robots. Subsequently, a kinematics parallel modeling method of modular robots is proposed. Both theoretical analytics and stimulation results show that this method can be adapted to the rapid modeling of modular robots with various types of topologies. The introduction of topology mathematical description and parallel computing reduces the difficulty of modular robot modeling and improves modeling efficiency. [ABSTRACT FROM AUTHOR]

Visual servoing is widely used in the peg-in-hole assembly due to the uncertainty of pose. Humans can easily align the peg with the hole according to key visual points/edges. By imitating human behavior, we propose P2HNet, a learning-based neural network that can directly extract desired landmarks for visual servoing. To avoid collecting and annotating a large number of real images for training, we built a virtual assembly scene to generate many synthetic data for transfer learning. A multi-modal peg-in-hole strategy is then introduced to combine image-based search-and-force-based insertion. P2HNet-based visual servoing and spiral search are used to align the peg with the hole from coarse to fine. Force control is then used to complete the insertion. The strategy exploits the flexibility of neural networks and the stability of traditional methods. The effectiveness of the method was experimentally verified in the D-sub connector assembly with sub-millimeter clearance. The results show that the proposed method can achieve a higher success rate and efficiency than the baseline method in the high-precision peg-in-hole assembly. [ABSTRACT FROM AUTHOR]

Electric field reconstruction at interfaces plays a crucial role in device performances controlling, for example, Schottky potential barrier and interfacial Rashba effect. Here, scanning transmission electron microscopy (STEM) and ab‐initio calculation are used to estimate the atomic‐scale and large‐scale potential reconstruction at the interface between a metallic oxide SrRuO3 (SRO) thin film and an insulating DyScO3 (DSO) substrate. The intensity and the symmetry of the large‐scale electrostatic reconstruction at the interface is probed by 4D‐STEM discussing the center‐of‐mass shift for different angular ranges detection. Numerical simulations indicate that thermal diffuse scattered (TDS) electrons can be sensitive to large‐scale electric field and experiments based on these diffused electrons near the interface confirm that the electric field extends more in the insulating DyScO3 (DSO) side. The magnitude of the electrostatic drop at the interface estimated by the 4D‐STEM experiment is in accordance with the ab‐initio values for a p‐type reconstruction of the interface plane. Furthermore, an atomically resolved TDS potential asymmetry is observed in real‐space at the SRO/DSO interface by 4D‐STEM. This asymmetry is associated with the formation of a local ferroelectric type dipole at the interfacial unit‐cell revealing unambiguously the balance evolution between antiferrodistortive and ferroelectric instabilities at the interface between a metallic SRO and an insulating DSO. [ABSTRACT FROM AUTHOR]