A kind of high-precision singularity prediction method for manipulator based on adaptive damped least-squares

It is a challenging problem to derive closed-form solution of inverse kinematics of the humanoid robot fingers with nonlinearly coupled joints. This paper presents a novel quasi-closed-form solution of inverse kinematics for such fingers. On the assumption that the angles of the two coupled joints are equal, an approximate closed-form solution of fingertips inverse kinematics is derived firstly. Utilizing the approximate solution as the ancillary variables, the problem of inverse kinematics is converted to determination of the joint angles from the approximate solution instead of the fingertip position. Based on the properties of the approximate solution, it is found that the approximate solution of the coupled joint plays the most important role in the joint angle derivation. In practical implementation, a ID look-up table and the linear interpolation to the approximate solution of the coupled joint are used to compute the accurate joint angles named the quasi-closed-form solution. Simulation results show that the proposed method exhibits good accuracy, though its computational cost is slightly higher than that of the approximate solution. Furthermore, a trajectory tracking controller is developed, formed with a combination of feedforward, feedback and a saturation control. The controller does not require the explicit use of dynamic modeling parameters. Lyapunov based stability analysis indicates that the finger system with the proposed controller can be asymptotically stable. Experiments are finally performed to demonstrate the correctness of the proposed solution of inverse kinematics and the trajectory tracking control algorithm.

Blade is the key component in the energy power equipment of turbine, aircraft engines and so on. Research on the process and equipment for blade finishing becomes one of important and difficult points. The motion control of hybrid grinding and polishing machine tool is different from that of traditional machine tool, and there exists motion coupling phenomenon when it moves as a whole. In order to control precisely motion of machine tool, the inverse and forward kinematics solution considering motion coupling of parallel mechanism was solved based on designed hybrid grinding and polishing machine tool for blade finishing in this paper. Firstly, the coupling factor of parallel mechanism was taken into account in the new model, and the inverse kinematics solution was modified to improve the solution accuracy. Then, the forward kinematics solution of machine tool based on inverse kinematics solution was solved. Finally, the motion simulation was made to verify the inverse and forward kinematics algorithm based on kinematic coupling factor of parallel mechanism by simulation software ADAMS. The simulation result shows that the inverse and forward kinematics solution deduced is correct, which provides theoretical basis for the motion control scheme of machine tool calibration and inspection control system.

Multiarm systems can perform complex and difficult tasks, such as manipulating a heavy or large object, that cannot be accomplished by a single manipulator owing to workspace and payload limitations. However, the motion planning problem for performing such a task is challenging because of the need to consider the closed-chain constraint. This article proposes an efficient motion planner that considers the closed-chain constraint based on a probabilistic roadmap. The proposed planner utilizes the following strategies. First, the planner obtains feasible nodes by randomly sampling the object pose, followed by computing the inverse kinematics (IK) solution of the multiarm. This can directly find a collision-free node satisfying the closed-chain constraint. Second, the planner repeatedly updates the new IK solution of the multiarm for the start and goal object pose. The IK solution is computed as close as possible to the joint configuration of the neighbor node. Consequently, the planner is more efficient than the existing methods that generate a node by sampling the joint configuration with projection method and have one pair of the start and goal node. Therefore, the planner can efficiently compute the path for object manipulation using a multiarm under a closed-chain constraint. The effectiveness of the proposed planner is validated by comparison with the existing planners in several scenarios. A video clip of the experiments in various scenarios can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://youtu.be/PR9aFf3juu4</uri> .

In this paper, the singularity of a planar mechanism with kinematic redundancy is studied. First, the architecture of the mechanism and the concept schematic diagram for singularity avoidance are stated. Next, inverse kinematics model of the planar parallel mechanism with kinematic redundancy is established. For determining the unique inverse solution of the mechanism under certain initial installation configuration, a comparison analysis based on the strategy tree and the virtual prototype is performed. Then, based on the obtained Jacobian matrices and the singular condition, the workspace-singularity map and two singular configurations of the mechanism are drawn. Finally, with the obtained workspace-singularity map, a singularity-free transition layer and an aisle can be found to perform to singularity avoidance, even if the initial designed trajectory passing through the second kind of singularity. Three tasks are carried out to illustrate that the workspace boundary and singular configuration can be changed by adjusting the kinematic redundant actuated parameter.

In the paper, we theoretically discuss the use of dynamic cavity environment to realize controlling of the evolution of spontaneous emission from an excited two-level atom. It is found that cyclical changes in cavity environment leads to the interaction betwcen the electromagnetic modes, resulting in the redistribution of the electromagnetic modes density; both the frequency of energy exchange and the energy dissipation rate between atom and environment are affected. When the frequency of environment change is relatively accordant with the process of energy exchange between the atom and environment, the decay rate is obviously inhibited and a stable coherence evolution can be obtained. Thus the evolution of coherent states can be modulated by using dynamic environment changes.

A kinematically redundant robot manipulator has a lot of useful properties, such as avoiding singularities, obstacles and optimized motions, because the redundant manipulator has an infinite number of solutions of inverse kinematics. But, unfortunately it is very hard to get the solution of inverse kinematics when the manipulator is redundant. We propose two schemes which find the globally optimized solution of the redundant manipulator's inverse kinematics using evolutionary programming (EP). Evolutionary programming is a stochastic search method based on principles of evolution and heredity. The two schemes are mainly different in the chromosome representation method. The chromosome of the first scheme is composed of all joint angles while the chromosome of the second is composed of only redundant joint angles. To make a complete trajectory, interpolation is needed and the bound of interpolation error is calculated. We find the globally optimized trajectory of a 3-DOF RRR-type planar manipulator.

A kinematically redundant robot manipulator has a lot of useful properties, such as avoiding singularities, obstacles and optimized motions. The redundant manipulator can have those useful properties because it has an infinite number of solutions of inverse kinematics. Unfortunately, many solutions also make it hard to get the solution of inverse kinematics. In this paper we propose two schemes which can find the globally near optimal solution of redundant manipulator's inverse kinematics using evolutionary programming (EP). Evolutionary programming is a stochastic search method based on principles of evolution and hereditary. The two schemes are mainly different on chromosome representation method. The chromosome of the first scheme is composed of all joint angles while the chromosome of the second scheme is composed of only redundant joint angles. To make a complete trajectory, interpolation is needed between two knots and the bound of interpolation error is calculated. We make the globally optimized trajectory of a 3-DOF RRR-type planer manipulator.

Redundant manipulators (RMs) are widely used in various fields due to their flexibility and versatility, but challenges remain in adjusting their inverse kinematics (IK) solutions. Adjustable IK solutions are crucial as they not only avoid joint limits but also enable the manipulability of the manipulator to be regulated. To address this issue, this paper proposes an IK optimization method. First, a performance metric for adjustable IK solutions is developed by introducing the motion-level factor. By setting the desired joint motion level, the IK solutions can be adjusted accordingly. Furthermore, a two-stage optimization algorithm is proposed to obtain the adjustable IK solutions. In the first stage, a modified gradient projection method is used to optimize the performance metric, generating a set of initial optimal solutions. However, cumulative errors may arise during this stage. To counteract this, the forward and backward reaching inverse kinematics algorithm is employed in the second stage to enhance the accuracy of the initial solutions. Finally, the effectiveness of the proposed method is validated through simulations and experiments using a planar cable-driven redundant manipulator. The results demonstrate that the IK solutions can be adjusted by modifying the motion-level factors. The proposed two-stage optimization algorithm integrates the advantages of the gradient projection method and the forward and backward reaching inverse kinematics algorithm, yielding a set of accurate and optimal IK solutions. Furthermore, the adjustable IK solutions facilitate the regulation of the RM’s manipulability, enhancing its adaptability and flexibility.

An autonomous humanoid robot (HR) with learning and control algorithms is able to balance itself during sitting down, standing up, walking and running operations, as humans do. In this study, reinforcement learning (RL) with a complete symbolic inverse kinematic (IK) solution is developed to balance the full lower body of a three-dimensional (3D) NAO HR which has 12 degrees of freedom. The IK solution converts the lower body trajectories, which are learned by RL, into reference positions for the joints of the NAO robot. This reduces the dimensionality of the learning and control problems since the IK integrated with the RL eliminates the need to use whole HR states. The IK solution in 3D space takes into account not only the legs but also the full lower body; hence, it is possible to incorporate the effect of the foot and hip lengths on the IK solution. The accuracy and capability of following real joint states are evaluated in the simulation environment. MapleSim is used to model the full lower body, and the developed RL is combined with this model by utilizing Modelica and Maple software properties. The results of the simulation show that the value function is maximized, temporal difference error is reduced to zero, the lower body is stabilized at the upright, and the convergence speed of the RL is improved with use of the symbolic IK solution.

We propose a bio-inspired model for making handover decision in heterogeneous wireless networks. It is based on an extended attractor selection model, which is biologically inspired by the self-adaptability and robustness of cellular response to the changes in dynamic environments. The goal of the proposed model is to guarantee multiple terminals’ satisfaction by meeting the QoS requirements of those terminals’ applications, and this model also attempts to ensure the fairness of network resources allocation, in the meanwhile, to enable the QoS-oriented handover decision adaptive to dynamic wireless environments. Some numerical simulations are preformed to validate our proposed bio-inspired model in terms of adaptive attractor selection in different noisy environments. And the results of some other simulations prove that the proposed handover scheme can adapt terminals’ network selection to the varying wireless environment and benefits the QoS of multiple terminal applications simultaneously and automatically. Furthermore, the comparative analysis also shows that the bio-inspired model outperforms the utility function based handover decision scheme in terms of ensuring a better QoS satisfaction and a better fairness of network resources allocation in dynamic heterogeneous wireless networks.

PurposeThe purpose of this paper is to analyze the roles played by organizational learning (OL) and innovation in organizations immersed in the processes of adaptation and strategic fit in dynamic and turbulent environments. The authors analyze whether OL and innovation act as sources of strategic fit, and whether strategic fit positively affects performance.Design/methodology/approachThe authors use data from a survey of a representative sample of 204 respondents from European firms active in high-technology sectors (response rate: 10.42 percent) and structural equation modeling (using the EQS 6.1 program) to undertake a transversal study.FindingsThe model confirms that OL and the capacity to innovate positively influence managers’ decisions to adapt their organizations to changes in dynamic environments. The achievement of strategic fit, in turn, improves organizational performance. The authors propose considering the innovation climate as a facilitator of new product and process development, although the innovation climate is not a direct antecedent of fit.Research limitations/implicationsThis study is limited by the fact that the analysis is cross-sectional and by the fact that all measures used are based on managers’ perceptions.Practical implicationsManagers should create and support an entrepreneurial culture that stresses continuous learning. They should also foster programs aimed at developing abilities, and promote the development of capabilities that facilitate acceptance of organizational change. Investments in building certain capabilities, such as OL and the capacity to innovate, are strategically justified, especially in turbulent environments.Originality/valueThis study is one of the first to investigate the complex interactions among OL, innovation, strategic fit, and performance. The results improve our understanding of the links between strategic fit and performance.

With the complex needs of the companies that cannot be met by a single service, Data-intensive Web Service Composition (DWSC) is required to compose multiple services in a distributed service environment. Compositions must satisfy functional specifications and non-functional requirements, i.e. Quality of Service (QoS). Existing approaches on DWSC make the underlying assumption that the participating Web services and communication networks are static so that their QoS and bandwidth seldom change. However, those approaches are impractical since network failures or dynamic bandwidth changes cause violations of user agreements. Additionally, they ignore the distribution of services in general, and therefore, variations in network attributes are not taken into account. In this paper, we address the problem of dynamic distributed DWSC (D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -DWSC), design a simulation model for bandwidth patterns, and propose an algorithm to generate robust solutions for D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -DWSC which can cope with the changes in dynamic environments. Experimental results verify the effectiveness of our method.

At present, several feature meta-models have been come up with. However, they can’t meet the requirements of dynamic Internet environment or software reuse. This paper proposes a feature meta-model based on ontology as well as its formal description. Meanwhile, FTM (Flexible Transaction Model) mechanism is considered. In particular, it is adaptable to the changes in dynamic environment and can meet the requirement of software reuse. Finally, an example is given to verify this model.KeywordsFormal DescriptionFormal SemanticPropositional FormulaSoftware ReuseBusiness ActionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This study extends the enterprise diversification strategy study in a dynamic way and the main question is how degree of diversification changes in dynamic environment. Based on the existing theories, the study puts forward propositions that high dynamic environment leads to low degree of diversification and low dynamic environment leads to high degree of diversification. To test the propositions, the study combines with the diversified strategy experience of Jianfeng, a China listed company, reaching the following conclusions. First, main environment factors, including macroeconomic, institution and industry factors, can affect dynamic environment. Second, degree of diversification changes to low with the increase of dynamic environment. And degree of diversification changes to high with the decrease of dynamic environment. These conclusions have practical implications for the Chinese diversification enterprises.

The rapid advancements in robotics have necessitated the development of innovative techniques for efficient path optimization and singularity avoidance in robotic manipulators. This study explores the integration of ant colony optimization (ACO) and fuzzy logic control (FLC) to address these challenges in manipulators with six degrees of freedom (6-DOF). ACO is utilized for global path planning, ensuring optimal trajectories while avoiding obstacles and singularity zones. Simultaneously, FLC provides local adaptability, refining the path for smoothness and stability in dynamic environments. The hybrid ACO-FLC approach demonstrates significant improvements in trajectory efficiency, singularity avoidance, and computational performance. The ACO methodology demonstrates an average improvement of 18% in path efficiency compared to conventional methods. Furthermore, the integration of FLC enhances trajectory smoothness by 25%, ensuring accurate and stable motion. This hybrid ACO-FLC framework achieves an exceptional 98% singularity avoidance rate and reduces computational overhead by 15%, facilitating faster and more efficient performance in dynamic and complex robotic environments. Comparative analysis with other techniques highlights the superiority of the proposed method in achieving robust and adaptive control.