A deformable, flexible sheet that is held by multiple robots can be used for manipulating and transporting an object. For such multi-robot collaborative transporting system, forward kinematics provide object's potential positions where it might remain stationary on the sheet. Some of these forward kinematics solutions are however demonstrated unstable under object's position or robot formation perturbations. This paper presents stability criteria of the kinematics solutions of the object on the deformable sheet held by multi-robot system. We capture the sheet deformation and object position using the virtual variable cable model. A constrained quadratic problem is formulated to obtain the forward kinematics solution for a given multi-robot configuration. Linear dependence of active constraints and stability multipliers are used to assess the stability of the kinematics solutions. Two stability criteria are proposed and analyzed under object position or robot formation perturbations. These criteria are related to the properties and conditions of the stability multipliers. We present an efficient computational algorithm to determine stable kinematics, which are a small fraction of feasible solutions. Experimental results and case studies are presented to validate and demonstrate the effectiveness and efficiency of the analyses and algorithms.

Robot exploration aims at the reconstruction of unknown environments, and it is important to achieve it with shorter paths. Traditional methods focus on optimizing the visiting order of frontiers based on current observations, which may lead to local-minimal results. Recently, by predicting the structure of the unseen environment, the exploration efficiency can be further improved. However, in a cluttered environment, due to the randomness of obstacles, the ability to predict is weak. Moreover, this inaccuracy will lead to limited improvement in exploration. Therefore, we propose FPUNet which can be efficient in predicting the layout of noisy indoor environments. Then, we extract the segmentation of rooms and construct their topological connectivity based on the predicted map. The visiting order of these predicted rooms is optimized which can provide high-level guidance for exploration. The FPUNet is compared with other network architectures which demonstrates it is the SOTA method for this task. Extensive experiments in simulations show that our method can shorten the path length by 2.18% to 34.60% compared to the baselines.

Sorting tasks are typical applications in the product and industrial domains. When facing new settings, such as different types of objects and their positions, the system has to be reprogrammed by experienced engineers, which decreases production efficiency and increases labour and downtime costs. In this paper, an off-line programming framework for sorting tasks based on human demonstration has been developed, taking advantage of machine vision and programming-by-demonstration concepts. Only one eye-to-hand depth camera has been used to record both the demonstrated trajectory and the corresponding relationship of the sorting task. A feature generalization algorithm has also been proposed to ensure that the system is able to reproduce the sorting task using both the key features of the trajectory and the sorting features. Finally, tracking, object and target matching, and generalization experiments have been conducted using the JAKA Zu7 robot with different types of fruit models and target boxes. The results show that the proposed framework is able to record the sorting trajectory at a frequency of 10 Hz, acquire and maintain the sorting relationship after new demonstration, and automatically reproduce the sorting task in new settings.Note to Practitioners—This paper is motivated by a goal to accomplish intuitive and user-friendly off-line programming for sorting task. The programming framework should be usable by non-experts and be able to complete programming tasks with a few demonstrations when faced with new scenarios, such as different types of objects, different correspondence relationships of the object, and different positions of the object. By observing the human demonstrated sorting tasks, the above-mentioned features can be effectively extracted, so as to complete the convenient teaching. The underlying principle of this paper is to let the robot observe human demonstrated sorting task by a depth camera, tracking the relative trajectory between the object and box as well as recording their matching information. The key features of the trajectory are extracted and further generalized combing the matching information to automatically reproduce the executable path in different settings. The proposed framework allows the users without prior programming knowledge to program the sorting task in new setting by simply conducting the pick and place process with their hands.

Robust grasp pose detection is crucial for robotic manipulation. Antipodal grasps are often inadequate for spatial objects. However, insufficient demonstrations and the higher complexity compared to parallel grippers make it challenging to implement learning-based robotic grasping with multi-fingered hands. To address these deficiencies, we propose a multi-modal grasp detection method tailored for three-fingered grippers. We utilize a simplified unified grasp representation, which can represent both precision and power grasping modes. Additionally, we introduce a pipeline to generate a multi-modal grasp dataset with a three-point contact grasp metric. We also present the concept of confness to quantify the probabilities of different grasp configurations within a given graspable area, guiding the predicted grasp poses to be concentrated around on centroidal region of objects. Furthermore, we develop QcMNet, an end-to-end network that is used to predict confness and infer grasp parameters. Experiments conducted with a Robotiq 3-Finger Adaptive Gripper, both in simulation and real-world scenarios, demonstrate that multi-modal grasping can increase the percent-cleared rate and enhance anti-disturbance capabilities in cluttered scenes. Note to Practitioners—This paper was motivated by the problem of finding the proper gripper configurations to ensure the stable grasping of objects across diverse tasks in industrial automation. Most existing approaches to robotic grasping detection utilize parallel grippers. However, it is insufficient to achieve stable grasping for spatial objects only with two frictional point contacts. This paper presents a learning-based grasp pose detection method tailored for three-fingered grippers, enabling robots to achieve a stable grasp on objects. We first propose a unified grasp representation that can represent different grasp modes, including precision and power grasp modes. Subsequently, a densely annotated dataset for three-fingered grippers is obtained by employing a robust grasp metric and an auto-annotation pipeline in simulation. This approach is designed to reduce labor consumption. The proposed pipeline not only enhances the efficiency of the data collection process but also ensures its diversity. Furthermore, we introduced a neural network to quantify the probabilities of different grasp modes within a graspable area and to infer grasp poses. Extensive physical robotic experiments validate the feasibility of this approach for grasping various objects. Significantly, most grasp poses concentrate around the centroid of objects, enhancing grasp stability. In future research, we will involve further optimizing the generated grasping candidates using local geometry information of objects. Additionally, we intend to generate online collision-free trajectories for robotic grasping to adapt to more complex automation environments.

The containment of multirobot systems (MRSs) has a wide range of applications. However, time delays in communication among robots introduce difficulties to the system to accomplish containment. In addition, the specific dynamics of robot models pose new nonlinear and nonholonomic challenges. To solve these problems, a containment control law is proposed first for double-integrator MRSs subject to nonuniform time-varying delays. In contrast to impractical uniform delays, nonuniform time-varying delays are considered more deeply from the perspective of the Laplacian matrix in this article. The stability is proved by the Lyapunov–Krasovskii function and linear matrix inequalities. The proposed control law is further refined into a dual-loop structure for multi-nonholonomic-mobile-robot systems, addressing the problem of nonholonomic constraints. Specifically, the first loop decouples the control inputs in a finite time, and then the nonholonomic robot models are regarded as linear models, which facilitates the proof of system stability. The effectiveness of the aforementioned two control laws is validated through simulations and experiments. Under these containment control laws, followers in the system reach the convex hull formed by leaders and meet the convergence objective despite the constraint of nonuniform time-varying delays.

Multi-mobile robot systems (MMRSs) show great advantages over one single robot in many applications. However, the robots are required to form desired task-specified formations, making feasible motions decrease significantly. Thus, it is challenging to determine whether the robots can pass through an obstructed environment under formation constraints, especially in an obstacle-rich environment. Furthermore, is there an optimal path for the robots? To deal with the two problems, a novel graph-based motion planner is proposed in this article. Valid configurations of the system are defined to satisfy both formation and obstacle constraints. Then, the whole valid configuration space is identified and mapped to an undirected graph. The breadth-first search (BFS) method is employed on the graph to answer the question of whether there is a feasible path on the graph. Finally, an optimal path will be planned on the updated graph, considering the cost of path length and formation preference. Simulation results show that the planner can be applied to get optimal motions of robots under formation constraints in obstacle-rich environments. In addition, different types of constraints are considered to verify the generality.

This article reports the design and validation of a novel predefined-time barrier function adaptive sliding-mode control (PTBFASMC) strategy for robust control of disturbed systems. The PTBFASMC strategy is established by integrating the time base generator along with the barrier function. Unlike existing similar works, the proposed method enables global predefined-time convergence, i.e., the system trajectory returns to the ultimate bound even if an escape occurs at a certain time instant. Besides, the convergence time can be predefined by the user, which is independent of the initial conditions and disturbance. Moreover, the reaching phase is eliminated and the magnitude of initial control output is zero. Another attractive feature of the proposed method lies in that the ultimate bound can be predefined, i.e., the ultimate bound is independent of the upper bound of disturbance. To avoid large control magnitude, a modified control strategy is provided, which extends the proposed scheme to different scenarios. The stability of the control system is demonstrated, and its superiority is verified through numerical simulations and experimental investigations on a piezoelectric actuator.

The accuracy of the force–torque sensors generally decreases with time under standard operating conditions. Therefore, periodic calibration for force–torque sensors is necessary. Specialized devices or subsidiary sensors are required during the traditional calibration process which bring in external measurement errors. In this article, anin situcalibration method is proposed to calibrate the force–torque sensor with a single standard mass to generate expected wrenches of the sensor during some arbitrary robot poses. Since the tilt of the mounting base of the robot can affect the direction of the gravity expressed with respect to the sensor frame, we first derive a model to express the relationship between the raw measurements and the expected wrenches under the influence of the tilted base, and then introduce subspaces and take advantage of projection and Rayleigh quotient to obtain the calibration matrix and angles of tilting simultaneously. The proposed algorithm is validated on simulation and the industrial automation (ATI) force–torque sensor. The results show that the proposed method is more cost-effective and reliable than existing technologies.

Machining chatter is an unstable vibration that often results in poor surface quality, low productivity, and degraded machine tool life. Chatter detection is an important approach to timely avoid detrimental effects on the workpiece and machine tools. However, most chatter detection methods will lose effectiveness and even yield false alarms, when chatter is accompanied by the beat effect. The beat effect is a common phenomenon in the machining process, which results in severely modulated amplitude of chatter. In this article, the variable-scale wavelet packet entropy (VSWPE) is proposed to detect chatter with the beat effect based on beat frequency estimation. First, an optimal demodulation technique is presented to extract the chatter vibration component and its modulated instantaneous amplitude (IA) from measured signals contaminated with noise and periodic interference. Afterward, the beat frequency is estimated from the harmonically distorted IA by an interpolated discrete Fourier transform (DFT). Finally, the VSWPE is calculated for chatter detection at a variable scale that is adaptively computed by the beat frequency. Numerical simulations show that the beat frequency can be accurately estimated with computational efficiency. Moreover, machining tests under different cutting conditions demonstrate that the proposed chatter detection method can effectively detect chatter with the beat effect.Note to Practitioners—Automatic online chatter detection is crucial in order to allow operators to timely interfere with the process and avoid detrimental effects caused by chatter. However, the practical machining process is considerably complex especially for unstable scenarios, where the beat effect is likely to occur simultaneously. Traditional chatter detection methods neglect the presence of the beat effect, and they will give misleading results and even false alarms when the beat effect is present. This article seeks to accurately detect chatter with and without the beat effect in a unified framework. The proposed solution can be integrated into the machine tool system, hence, to enhance the productivity, reliability, and precision of the machining process.

This paper presents an active control method, consisting of an adaptive sliding-mode controller (ASMC) and a displacement field reconstruction (DFR) method, for chatter suppression in turning of thin-walled workpieces (such as compressor disks and casings in aircraft engines) where low workpiece stiffness renders machining with potential regenerative chatter. Due to the presence of multi-modal dynamics, variant modal parameters, and measurement difficulties, active chatter control of thin plate turning has been challenging. Unlike existing controls based on a lumped-parameter single degree-of-freedom cutting model, a distributed-parameter dynamic model of a rotating thin plate with multiple vibration modes is used to analyze the machining stability with the designed controller. Moreover, model parameters of the plate are not needed to construct the controller. The DFR is employed to capture the plate dynamic behavior for feedback to the ASMC during turning, overcoming the long existing difficulties to measure plate vibration at the cutting point. A fast tool servo is utilized in the control implementation. Theoretical analyses, numerical simulations, and experimental evaluation on a lathe demonstrate that chatter in thin plate turning can be effectively attenuated with the proposed active control method.

Dynamic model has broad applications in motion planning, feedforward controller design, and disturbance observer design. Particularly, with the increasing application of model-based control in industrial robots, there has been a resurgence of research interest in accurate identification of dynamic models. However, on the one hand, most existing identification methods directly rely on least squares or weighted least squares (WLS), which suffer from outliers and could lead to physical infeasible solutions. On the other hand, nonlinearity of the friction model is seldom treated in a unified way with linear regression. Moreover, recent researches have shown that proper exciting trajectories are crucial to the identification accuracy, but few of previous works take measurement noise into consideration when optimizing the exciting trajectories. In this article, we propose an iterative approach which integrates WLS, iteratively reweighted least squares with linear matrix inequality constraints, and nonlinear friction models so that the above-mentioned issues can be properly solved altogether. Our research also reveals that performance can be improved by including priori knowledge of measurement noise in the optimization of exciting trajectories. The proposed approach is supported by experimental analysis of four different combinations within the framework on a 6-DoF industrial robot.

This paper focuses on proposing an easily-implemented control method for high-acceleration point-to-point motions. The control algorithm used here to handle the disturbances consists of a model-based feedforward controller, a pole-placement PD controller and a disturbance observer. Then, a fast time-domain identification technique is implemented to give the accurate model parameters, which can be directly utilized in the control algorithm. The method avoids the complicated parameters tuning process, which would be attractive in the industrial application. Experiments are carried out on a permanent magnet linear synchronous motor (PMLSM) and the results demonstrate that the proposed method is capable of achieving high-precision and fast positioning by reducing the tracking error and overshoot.

In real-time network-based systems with long linear paths, the growth rate of time synchronization error is the major barrier to the scalability of systems even if a transparent clock mechanism of IEEE 1588 is used. This paper is devoted to designing a new time synchronization method for such systems. In the proposed algorithm, a proportional-integral (PI) clock servo is used to achieve the frequency compensation. In order to reduce the growth rate of synchronization error due to the quantization error in timestamping, a Kalman filter is designed based on a state-variable model, which is built for the PI controller-tuned slave clock. In addition, the quantization effect is analyzed and the variance of quantization error is quantitatively estimated for each slave node. Experiments are performed to validate its effectiveness and demonstrate that the peak-to-peak jitter is measured to be only 59.37 ns after four hops, and the growth rate of synchronization error can also be significantly reduced by the presented synchronization method. This indicates that the maximum number of networked nodes can be correspondingly increased.

This paper proposes a time-stamped cross-coupled control (TSCCC) algorithm to deal with the asynchronous sampling and network-induced delays in networked computer numerical control (CNC) machines. It uses time-stamps to estimate the network-induced delays from the sampling instants of different axes to the controller node. The network-induced delays are considered for accurately estimating the contour error in real-time. Furthermore, a networked CNC simulation system based on TrueTime toolbox is constructed, on which the proposed TSCCC algorithm is compared with the cross-coupled control (CCC) algorithm. Simulation results on two DC servomotors show that the TSCCC algorithm achieves better contour accuracy than the CCC algorithm.

A new control method based on a combination of robust control and internal model control has been proposed. This control system includes internal model controller for velocity loop, robust controller for position loop, and a feedforward controller. The internal model controller is designed to suppress disturbance. Stability robustness of the closed loop is provided by the robust controller. The zero phase error tracking controller is adopted to act as a feedforward controller to further improve the tracking performance. The theoretical analysis shows the validity of the proposed control scheme. Furthermore, simulations and experimental results are presented to demonstrate performance improvement of the proposed control structure.

Nonlinear friction and external disturbances affect the positioning accuracy of high-speed motion systems, especially impelled by linear motor. Thus, how to eliminate theses disturbance should be considered when designing a robust controller. This paper presents a controller, which includes three parts: a proportional-plus-derivative (PD) feedback controller, a friction compensator, and a disturbance observer. The friction compensator is based on LuGre model and it compensates for nonlinear friction. The disturbance observer is used to eliminate the friction compensation error and other external disturbances. Experimental results show that the controller gives high positioning accuracy and more robust performance in the presence of disturbances.