Incipient slip detection provides critical feedback for robotic grasping and manipulation tasks. However, maintaining its adaptability under diverse object properties and complex working conditions remains challenging. This article highlights the importance of representing spatio-temporal features of incipient slip, and proposes a novel approach for incipient slip modeling and detection. Based on the analysis of localized displacement phenomenon, we establish the relationship between the characteristic strain rate extreme events and the local slip state. This approach enables the detection of both the spatial distribution and temporal evolution of stick-slip regions. Also, the proposed method can be applied to strain distribution sensing devices, such as vision-based tactile sensors. Simulations and prototype experiments validated the effectiveness of this approach under varying contact conditions, including different contact geometries, friction coefficients, and combined loads. Experiments demonstrate that this method not only accurately and reliably delineates incipient slip, but also facilitates friction parameter estimation and adaptive grasping control.

Object reorientation is a critical task for robotic grippers, especially when manipulating objects within constrained environments. The task poses significant challenges for motion planning due to the high-dimensional output actions with the complex input information, including unknown object properties and nonlinear contact forces. Traditional approaches simplify the problem by reducing degrees of freedom, limiting contact forms, or acquiring environment/object information in advance—significantly compromising adaptability. To address these challenges, we deconstruct the complex output actions into three fundamental types based on tactile sensing: task-oriented actions, constraint-oriented actions, and coordinating actions. These actions are then optimized online using gradient optimization to enhance adaptability. Key contributions include simplifying contact state perception, decomposing complex gripper actions, and enabling online action optimization for handling unknown objects or environmental constraints. Experimental results demonstrate that the proposed method is effective across a range of everyday objects, regardless of environmental contact. Additionally, the method exhibits robust performance even in the presence of unknown contacts and nonlinear external disturbances.

Accurate grasp force control is one of the key skills for ensuring successful and damage-free robotic grasping of objects. Although existing methods have conducted in-depth research on slip detection and grasping force planning, they often overlook the issue of adaptive tracking of the actual force to the target force when handling objects with different material properties. The optimal parameters of a force tracking controller are significantly influenced by the object’s stiffness, and many adaptive force tracking algorithms rely on stiffness estimation. However, real-world objects often exhibit viscous, plastic, or other more complex nonlinear time-varying behaviors, and existing studies provide insufficient support for these materials in terms of stiffness definition and estimation. To address this, this paper introduces the concept of generalized stiffness, extending the definition of stiffness to nonlinear time-varying grasp system models, and proposes an online generalized stiffness estimator based on Long Short-Term Memory (LSTM) networks. Based on generalized stiffness, this paper proposes an adaptive parameter adjustment strategy using a PI controller as an example, enabling dynamic force tracking for objects with varying characteristics. Experimental results demonstrate that the proposed method achieves high precision and short probing time, while showing better adaptability to non-ideal objects compared to existing methods. The method effectively solves the problem of grasp force tracking in unknown, nonlinear, and time-varying grasp systems, demonstrating the generalization capability of our neural network and enhancing the robotic grasping ability in unstructured environments.

In typical in-hand manipulation tasks represented by object pivoting, the real-time perception of rotational slippage has been proven beneficial for improving the dexterity and stability of robotic hands. An effective strategy is to obtain the contact properties for measuring rotation angle through visuotactile sensing. However, existing methods for rotation estimation did not consider the impact of the incipient slip during the pivoting process, which introduces measurement errors and makes it hard to determine the boundary between stable contact and macro slip. This paper describes a generalized 2-d contact model under pivoting, and proposes a rotation measurement method based on the line-features in the stick region. The proposed method was applied to the Tac3D vision-based tactile sensors using continuous marker patterns. Experiments show that the rotation measurement system could achieve an average static measurement error of 0.17°±0.15° and an average dynamic measurement error of 1.34°±0.48°. Besides, the proposed method requires no training data and can achieve real-time sensing during the in-hand object pivoting.

Grasping is the most basic and important function of robots. However, the grasping performance of existing manipulators is far inferior to that of human hands. Human hands are able to evaluate the degree of incipient slip on contact surface based on tactile information during grasping process and thus regulate grasping force. As a result, humans can use the smallest possible grasping force while ensuring successful grasping, which maximizes the performance of their hands and avoids damaging the grasped object as much as possible. The existing incipient slip degree evaluation methods have certain shortcomings. So this paper proposes a novel method which can be applied to complex contact conditions where the incipient slip degree is not unidirectional and torque exists. Also, there are no restrictions on the material parameters and surface topography of the grasped object, and no need to obtain any information about it in advance. We construct a grasping force control strategy for parallel grippers based on this evaluation method, with the goal of enabling the gripper to achieve the similar grasping performance of human hands. The grasping strategy is verified in simulations and actual experiments, and the difference between the controlled force and minimum grasping force is demonstrated.Note to Practitioners—In unstructured grasping tasks, the information of the grasped object cannot be obtained in advance, which makes it difficult for robotic grasping. Conventional grasping strategies often use the maximum force of grippers, but this can cause damage to fragile objects. In contrast, human hands can flexibly control the size of the grasping force, which will not far exceed the minimum force required to ensure successful grasping. This capability greatly expands the grasping range of human hands. Neuroscience studies have shown that such excellent grasping performance owes to the perception of incipient slip by human’s tactile system. However, existing incipient slip detection methods for manipulators have certain drawbacks. To fill this gap, we propose a novel method that can be applied to contact conditions where tangential forces are reversed and torques exist. Meanwhile the method has no restrictions on the surface morphology and material parameters of the contact object, which has a good range of applications. This paper applies this method to a parallel gripper commonly used in industrial scenarios, and proposes a grasping force control strategy that can automatically adjust the magnitude of the grasping force to achieve a similar grasping performance to human hands -keeping the grasping force within a certain range beyond the minimum value. In the future, we also intend to apply the proposed method to more dexterous manipulators and perform more complex manipulation tasks. Possible application scenarios include: smart prosthesis, virtual reality, remote control, etc. Currently, we assume that the Coulomb’s law of friction is satisfied on the contact surface, and more complex friction models will be introduced in the future to further improve the applicability of this method.

High contour accuracy is vital to the multi-axis motion system. Improvement in the contour accuracy of the parallel kinematic machine (PKM) has being a challenging issue in the process of its practical application. In analogy to the intelligent structure of the organisms, this paper proposes a smart structure based compensation (SSBC) method to improve the PKM's contour accuracy. Instead of adopting the advanced control strategies or improving the performances of machine's hardware, this method deals with the contouring issue through innovative mechanical design. The smart structures, which have the ability of perception and control, are introduced into the PKM's components to actively suppress their deformations and vibrations. Meanwhile, the accurate micro motions provided by the smart structure components can be used to compensate for the PKM's contour error caused by the uncoordinated control of the driving system just by using a simple control strategy. The implementation procedure of the SSBC method is first discussed in detail. A 2-DOF PKM is taken as the application object and the smart structures are introduced into its kinematic chains to control their axial deformations and internal forces. Then this PKM's kinematic error is reduced in advance to guarantee the result of the contour accuracy improvement. Finally, several experimental tests are performed to verify the effectiveness the proposed SSBC method in improving the PKM's contour accuracy. Except for the PKM, the SSBC method also provides a novel and effective way for the advanced multi-axis machines to improve their contour accuracies.

This paper introduces the mobility analysis of a 1-DOF (Degrees of Freedom) Sarrus Linkage like 7-R single closed loop mechanism. The analysis procedure implements both group theory based criterion and reciprocal screw theory. The analysis shows that such mechanism allows rectilinear motion with full-cycle mobility. Further, unlike the Sarrus Linkage, which is overconstrained, this 7-R linkage is non-overconstrained. This property makes the Sarrus Linkage-like mechanism more robust to manufacturing errors. Simulation has shown that even with large manufacturing errors, such mechanism is still able to realize desired rectilinear motion. One possible application for this mechanism is a durable automotive suspension.

The focus position of Five-hundred-meter Aperture Spherical Telescope may be changed unexpectedly because of the environmental condition such as strong winds. So a Fine Feed Cabin Model based on Stewart platform is used to get rid of the unexpected change by controlling the position and orientation of the platform. To improve the accuracy of the platform, a calibration method by a laser tracker LTD500 is presented and an error compensation experiment is carried out. The position and orientation of the platform can be figured out by LTD500. The measurement configurations can be selected from the boundary of the joint space. After calibration the accuracy can improve to about 0.2 mm and meet the requirement. The experiment result proves that the method using a laser tracker is very useful to calibrate Stewart platform, even if there are no enough measurement configurations.

The kinematic equation of a general Stewart-Gough platform is formulated and the error mapping model is derived. The relationships between input and output errors of both a single kinematic link and the whole manipulator are presented and analyzed. The singular values of a Jacobian matrix are used to define three error sensitivity measures, namely the comprehensive error sensitivity, the absolute error sensitivity and the directional error sensitivity. According to these measures, two indicators, the comprehensive error coefficient and the comprehensive error degree, are defined to evaluate the kinematic accuracy for various configurations of this type of mechanism. Based on the structure of a real parallel kinematic machine tool, several kinematic accuracy evaluation experiments are depicted graphically. The study shows that the evaluation measures proposed in this paper are very useful to help designers and operators correctly choose the manipulator configurations for obtaining the high kinematic accuracy.

Legged robot generally moves according to the preplanned gait, which cannot coordinate the motions among the legs with environment. Petri-net is a graphic assembling net model that can be applied to describe and control the dynamic behaviors in multi-agent system. Considering the motion characters of legged robot, this paper proposed a single-leg Petri-net control model for legged robot, and defined the corresponding model parameters. As an example, five such models were assembled to construct the coordination motion control model for the five-legged robot we developed. On this base, the reachable marking graph including all moving states of the five-legged robot was calculated and expressed. Applying the Petri-net model, a new free gait was presented. At last, the motion of five-legged robot crossing grooves in discrete area was simulated. The results show that the five-legged robot can adapt to the environment automatically with the Petri-net control model in this paper.