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This paper addresses the dynamics and trajectory tracking control of cooperative multiple mobile cranes. Compared with a single mobile crane, cooperative cable parallel manipulators for multiple mobile cranes (CPMMC) are more complex in configuration, which have the characters of both series and parallel manipulators. Therefore, for the CPMMC, the forward as well as the inverse kinematics and dynamics include the difficulties of both series and parallel manipulators. However, the closed kinematic chain brings about potential benefits, including sufficient accuracy, higher cost performance, better lifting capacity and security. Firstly, the forward and inverse kinematics of the CPMMC with point mass are derived with elimination method, and the complete dynamic model of the CPMMC is established based on Lagrange equation and the complete kinematics. Secondly, considering the repetitive tasks and high security and precision requirement, a robust iterative learning controller is designed for trajectory tracking on the basis of the linearization of the dynamics. Thirdly, taking the engineering practice into consideration, two case studies are simulated with the same expected trajectory but with different weights of the loads. Finally, the designed controller is compared with traditional PD control algorithm via numerical simulation. The results demonstrate the feasibility and superiority of the CPMMC and designed controller, and provide a theoretical basis for the cooperation of multiple mobile cranes.

Cable-driven parallel robots (CDPRs) are categorized as a type of parallel manipulators. In CDPRs, flexible cables are used to take the place of rigid links. The particular property of cables provides CDPRs several advantages, including larger workspaces, higher payload-to-weight ratio and lower manufacturing costs rather than rigid-link robots. In this paper, the history of the development of CDPRs is introduced and several successful latest application cases of CDPRs are presented. The theory development of CDPRs is introduced focusing on design, performance analysis and control theory. Research on CDPRs gains wide attention and is highly motivated by the modern engineering demand for large load capacity and workspace. A number of exciting advances in CDPRs are summarized in this paper since it is proposed in the 1980s, which points to a fruitful future both in theory and application. In order to meet the increasing requirements of robot in different areas, future steps foresee more in-depth research and extension applications of CDPRs including intelligent control, composite materials, integrated and reconfigurable design.

Three-dimensional (3D) printing technology has been greatly developed in the last decade and gradually applied in the construction, medical, and manufacturing industries. However, limited workspace and accuracy restrict the development of 3D printing technology. Due to the extension range and flexibility of cables, cable-driven parallel robots can be applied in challenging tasks that require motion with large reachable workspace and better flexibility. In this paper, a cable-driven parallel robot for 3D Printing is developed to obtain larger workspace rather than traditional 3D printing devices. A kinematic calibration method is proposed based on cable length residuals. On the basis of the kinematic model of the cable-driven parallel robot for 3D Printing, the mapping model is established among geometric structure errors, zero errors of the cable length, and end-effector position errors. In order to improve the efficiency of calibration measurement, an optimal scheme for measurement positions is proposed. The accuracy and efficiency of the kinematics calibration method are verified through numerical simulation. The calibration experiment based on the motion capture system indicates that the position error of end-effector is decreased to 0.6157 mm after calibration. In addition, the proposed calibration method is effective and verified for measurement positions outside optimal positions set through experiments.

Achieving the high-precision control of cable-driven parallel robots (CDPRs) is complex because of their structural properties. In this paper, a quintessential redundant CDPR is designed as the research subject, and a continuous switching sliding mode controller based on workspace vision is implemented to enhance the accuracy and stability of trajectory tracking. In addition, a virtual prototype of the CDPR with uncertainties is created in the simulation analysis software ADAMS, and co-simulation is performed with the control system designed in Simulink to validate the effectiveness of the proposed control strategy. Furthermore, a CDPR platform is established for trajectory tracking experiments using the visual-based position feedback method. The trajectory tracking performance with the three control schemes is then evaluated. The experimental results show that the continuous switching sliding mode control algorithm can significantly decrease trajectory tracking errors and exhibit superior trajectory tracking performance compared to the other control strategies.

Deep reinforcement learning has exhibited exceptional capabilities in a variety of sequential decision-making problems, providing a standardized learning paradigm for the development of intelligent multi-robot systems. Nevertheless, when confronted with dynamic and unstructured environments, the security of decision-making strategies encounters serious challenges. The absence of security will leave multi-robot susceptible to unknown risks and potential physical damage. To tackle the safety challenges in autonomous decision-making of multi-robot systems, this manuscripts concentrates on a uniformly ultimately bounded constrained hierarchical safety reinforcement learning strategy (UBSRL). Initially, the approach innovatively proposes an event-triggered hierarchical safety reinforcement learning framework based on the constrained Markov decision process. The integrated framework achieves a harmonious advancement in both decision-making security and efficiency, facilitated by the seamless collaboration between the upper-tier evolutionary network and the lower-tier restoration network. Subsequently, by incorporating supplementary Lyapunov safety cost networks, a comprehensive strategy optimization mechanism that includes multiple safety cost constraints is devised, and the Lagrange multiplier principle is employed to address the challenge of identifying the optimal strategy. Finally, leveraging the principles of uniformly ultimate boundedness, the stability of the autonomous decision-making system is scrutinized. This analysis reveals that the action trajectories of multiple robots can be reverted to a safe space within a finite time frame from any perilous state, thereby theoretically substantiating the efficacy of the safety constraints embedded within the proposed strategy. Subsequent to exhaustive training and meticulous evaluation within a multitude of standardized scenarios, the outcomes indicate that the UBSRL strategy can effectively restricts the safety indicators to remain below the threshold, markedly enhancing the stability and task completion rate of the motion strategy.

In this paper, a non-intrusive closed-loop current sensor based on high-sensitivity tunneling magnetoresistance (TMR) were demonstrated. Using the finite element modeling based on Maxwell’s electromagnetic theory, the distribution of magnetic field on the closed-loop magnetic flux concentrator (MFC) was calculated with a copper busbar passing through the center. Two different types with “slot” and “hole” were designed to place TMR sensors for the detection of the magnetic field generated by the working copper busbar. A secondary winding around MFC generated a reverse magnetic field to completely offset the magnetic field of the primary current, which realizes the real-time adjustment and monitoring of the current by the TMR sensor with magnetic flux balance. Moreover, the simulated results show that the performance of slot-gap MFC is better than that with hole-type design. Utilizing the experimental TMR sensor with sensitivity of 10 mV/V/Oe and linear field range of ±40 Oe, the monitoring current can reach up to 200 A. The described application in the current monitoring demonstrates the functionality and feasibility of TMR sensors.

This paper develops a dynamics-based nonsingular interval model and proposes a first-order composite function interval perturbation method (FCFIPM) for luffing angular response field analysis of the dual automobile cranes system (DACS) with narrowly bounded uncertainty. By using the nonsingular interval model to describe a structure parameter with bounded uncertainty, the reasonable lower and upper bounds can be obtained, which is quite different from the traditional interval model with approximate bounds only from a large number of samples. Firstly, for the DACS with deterministic information, the inverse kinematics is analyzed, and the dynamic model of the DACS is established based on the virtual work principle and the inverse kinematics. Secondly, considering the nonsingularity of the dynamic response curves, a dynamics-based nonsingular interval model is introduced. Based on the nonsingular interval model, the interval luffing angular response vector equilibrium equation of the DACS is established. Thirdly, a first-order composite function interval perturbation method is proposed. In the FCFIPM, the composite function vectors are expanded by using the first-order Taylor series expansion, based on the differential property of composite function and monotonic analysis technique, the lower and upper bounds of the interval luffing angular response vector of the crane 1 and crane 2 of the DACS are determined. The first case is to investigate the deterministic kinematics and dynamics of the DACS with a given trajectory. The second case is provided to illustrate the detailed implementation process of constructing a dynamics-based nonsingular interval model. Finally, some numerical examples are given to verify the feasibility and efficiency of the FCFIPM for solving the luffing angular response field problem with narrowly interval parameters.

Industrial robots are widely used in the painting industry, such as automobile manufacturing and solid wood furniture industry. An important problem is how to improve the efficiency of robot programming, especially in the current furniture industry with multiple products, small batches and increasingly high demand for customization. In this work, we propose an outer loop adaptive control scheme, which allow users to realize the practical application of the zero-moment lead-through teaching method based on dynamic model without opening the inner torque control interface of robots. In order to accurately estimate the influence of joint friction, a friction model is established based on static, Coulomb and viscous friction characteristics, and the Sigmoid function is used to represent the transition between motion states. An identification method is used to quickly identify the dynamic parameters of the robot. The joint position/speed command of the robot’s inner joint servo loop is dynamically generated based on the user-designed adaptive control law. In addition, the zero-moment lead-through teaching scheme based on the dynamic model is applied to a spray-painting robot with closed control system. In order to verify our method, CMA GR630ST is used to conduct experiments. We identified the parameters of the dynamic model and carried out the zero-moment lead-through teaching experiment to track the target trajectory. The results show that the proposed method can realize the application of modern control methods in industrial robot with closed control systems, and achieve a preliminary exploration to improve the application scenarios of spray-painting robots.

Background Menaquinone (MK-7) is a highly valuable vitamin K 2 produced by Bacillus subtilis . Common static metabolic engineering approaches for promoting the production of MK-7 have been studied previously. However, these approaches caused an accumulation of toxic substances and reduced product yield. Hence, dynamic regulation by the quorum sensing (QS) system is a promising method for achieving a balance between product synthesis and cell growth. Results In this study, the QS transcriptional regulator SinR, which plays a significant role in biofilm formation and MK production simultaneously, was selected, and its site-directed mutants were constructed. Among these mutants, sinR knock out strain (KO-SinR) increased the biofilm biomass by 2.8-fold compared to the wild-type. SinR quad maximized the yield of MK-7 (102.56 ± 2.84 mg/L). To decipher the mechanism of how this mutant regulates MK-7 synthesis and to find additional potential regulators that enhance MK-7 synthesis, RNA-seq was used to analyze expression changes in the QS system, biofilm formation, and MK-7 synthesis pathway. The results showed that the expressions of tapA , tasA and epsE were up-regulated 9.79-, 0.95-, and 4.42-fold, respectively. Therefore, SinR quad formed more wrinkly and smoother biofilms than BS168. The upregulated expressions of glpF , glpk , and glpD in this biofilm morphology facilitated the flow of glycerol through the biofilm. In addition, NADH dehydrogenases especially sdhA , sdhB , sdhC and glpD , increased 1.01-, 3.93-, 1.87-, and 1.11-fold, respectively. The increased expression levels of NADH dehydrogenases indicated that more electrons were produced for the electron transport system. Electrical hyperpolarization stimulated the synthesis of the electron transport chain components, such as cytochrome c and MK, to ensure the efficiency of electron transfer. Wrinkly and smooth biofilms formed a network of interconnected channels with a low resistance to liquid flow, which was beneficial for the uptake of glycerol, and facilitated the metabolic flux of four modules of the MK-7 synthesis pathway. Conclusions In this study, we report for the first time that SinR quad has significant effects on MK-7 synthesis by forming wrinkly and smooth biofilms, upregulating the expression level of most NADH dehydrogenases, and providing higher membrane potential to stimulate the accumulation of the components in the electron transport system.

To avoid impacts and vibrations during the processes of acceleration and deceleration while possessing flexible working ways for cable-suspended parallel robots (CSPRs), point-to-point trajectory planning demands an under-constrained cable-suspended parallel robot (UCPR) with variable angle and height cable mast as described in this paper. The end-effector of the UCPR with three cables can achieve three translational degrees of freedom (DOFs). The inverse kinematic and dynamic modeling of the UCPR considering the angle and height of cable mast are completed. The motion trajectory of the end-effector comprising six segments is given. The connection points of the trajectory segments (except for point P3 in the X direction) are devised to have zero instantaneous velocities, which ensure that the acceleration has continuity and the planned acceleration curve achieves smooth transition. The trajectory is respectively planned using three algebraic methods, including fifth degree polynomial, cycloid trajectory, and double-S velocity curve. The results indicate that the trajectory planned by fifth degree polynomial method is much closer to the given trajectory of the end-effector. Numerical simulation and experiments are accomplished for the given trajectory based on fifth degree polynomial planning. At the points where the velocity suddenly changes, the length and tension variation curves of the planned and unplanned three cables are compared and analyzed. The OptiTrack motion capture system is adopted to track the end-effector of the UCPR during the experiment. The effectiveness and feasibility of fifth degree polynomial planning are validated.