Manipulation Problem Research Articles

Hybrid Sampling-Based Path Planning for Mobile Manipulators Performing Pick and Place Tasks in Narrow Spaces

A mobile manipulator is capable of traversing a vast area while performing manipulation tasks in confined spaces. However, the high degree of freedom presents a challenge for path planning. In this paper, a hybrid sampling-based path planning method is proposed for mobile manipulators performing pick and place tasks in confined spaces. This method employs a random sampling approach, yet differs from the traditional RRT method. Firstly, a sampling-based configuration generation method for mobile manipulators is proposed, with the objective of generating a valid, collision-free configuration with the end-effector at the desired pose. A path for the end-effector corresponding to the goal configuration is then planned using the RRT method. Secondly, an area-restricted approach that samples in the vicinity of the previous configuration is introduced to generate the next valid configuration. Subsequently, a cost computation rule is devised to identify the optimal subsequent configuration utilizing the trajectory of the end-effector as a guiding principle. Finally, the obtained path is smoothed. Simulations demonstrate that the proposed hybrid sample-based method is an effective solution to the path planning problem for mobile manipulators performing pick and place tasks in narrow spaces.

Barrier-critic-disturbance approximate optimal control of nonzero-sum differential games for modular robot manipulators

In this paper, for addressing the safe control problem of modular robot manipulators (MRMs) system with uncertain disturbances, an approximate optimal control scheme of nonzero-sum (NZS) differential games is proposed based on the control barrier function (CBF). The dynamic model of the manipulator system integrates joint subsystems through the utilization of joint torque feedback (JTF) technique, incorporating interconnected dynamic coupling (IDC) effects. By integrating the cost functions relevant to each player with the CBF, the evolution of system states is ensured to remain within the safe region. Subsequently, the optimal tracking control problem for the MRM system is reformulated as an NZS game involving multiple joint subsystems. Based on the adaptive dynamic programming (ADP) algorithm, a cost function approximator for solving Hamilton–Jacobi (HJ) equation using only critic neural networks (NN) is established, which promotes the feasible derivation of the approximate optimal control strategy. The Lyapunov theory is utilized to demonstrate that the tracking error is uniformly ultimately bounded (UUB). Utilizing the CBF’s state constraint mechanism prevents the robot from deviating from the safe region, and the application of the NZS game approach ensures that the subsystems of the MRM reach a Nash equilibrium. The proposed control method effectively addresses the problem of safe and approximate optimal control of MRM system under uncertain disturbances. Finally, the effectiveness and superiority of the proposed method are verified through simulations and experiments.

Improved Bald Eagle Search Optimization Algorithm for the Inverse Kinematics of Robotic Manipulators.

The inverse kinematics of robotic manipulators involves determining an appropriate joint configuration to achieve a specified end-effector position. This problem is challenging because the inverse kinematics of manipulators are highly nonlinear and complexly coupled. To address this challenge, the bald eagle search optimization algorithm is introduced. This algorithm combines the advantages of evolutionary and swarm techniques, making it more effective at solving nonlinear problems and improving search efficiency. Due to the tendency of the algorithm to fall into local optima, the Lévy flight strategy is introduced to enhance its performance. This strategy adopts a heavy-tailed distribution to generate long-distance jumps, thereby preventing the algorithm from becoming trapped in local optima and enhancing its global search efficiency. The experiments first evaluated the accuracy and robustness of the proposed algorithm based on the inverse kinematics problem of manipulators, achieving a solution accuracy of up to 10-18 m. Subsequently, the proposed algorithm was compared with other algorithms using the CEC2017 test functions. The results showed that the improved algorithm significantly outperformed the original in accuracy, convergence speed, and stability. Specifically, it achieved over 70% improvement in both standard deviation and mean for several test functions, demonstrating the effectiveness of the Lévy flight strategy in enhancing global search capabilities. Furthermore, the practicality of the proposed algorithm was verified through two real engineering optimization problems.

Inverse Kinematics of Robotic Manipulators Based on Hybrid Differential Evolution and Jacobian Pseudoinverse Approach

Robot manipulators play a critical role in several industrial applications by providing high precision and accuracy. To perform these tasks, manipulator robots require the effective computation of inverse kinematics. Conventional methods to solve IK often encounter significant challenges, such as singularities, non-linear equations, and poor generalization across different robotic configurations. In this work, we propose a novel approach to solve the inverse kinematics (IK) problem in robotic manipulators using a metaheuristic algorithm enhanced with a Jacobian step. Our method overcomes those limitations by selectively applying the Jacobian step to the differential evolution (DE) algorithm. The effectiveness and versatility of the proposed approach are demonstrated through simulations and real-world experimentation on a 5 DOF KUKA robotic arm.

All-Optical Trapping and Programmable Transport of Gold Nanorods with Simultaneous Orientation and Spinning Control.

Gold nanorods (GNRs) are of special interest in nanotechnology and biomedical applications due to their biocompatibility, anisotropic shape, enhanced surface area, and tunable optical properties. The use of GNRs, for example, as sensors and mechanical actuators, relies on the ability to remotely control their orientation as well as their translational and rotational motion, whether individually or in groups. Achieving such particle control by using optical tools is challenging and exceeds the capabilities of conventional laser tweezers. We present a tool that addresses this complex manipulation problem by using a curve-shaped laser trap, enabling the optical capture and programmable transport of single and multiple GNRs along any trajectory. This type of laser trap combines confinement and propulsion optical forces with optical torque to transport the GNRs while simultaneously controlling their rotation (spinning) and orientation. The proposed system facilitates the light-driven control of GNRs and the quantitative characterization of their motion dynamics including transport speed, spinning frequency, orientation, and confinement strength. We experimentally demonstrate that remote control of the GNRs can be achieved both near a substrate surface (2D trapping) and deep within the sample (3D all-optical trapping). The motion dynamics of two sets of off-resonant GNRs, possessing similar aspect ratios but different resonance wavelengths, are analyzed to highlight the role played by their optical and mechanical properties in the optical manipulation process. The experimental results are supported by a theoretical model describing the observed motion dynamics of the GNRs. This optical manipulation tool can significantly facilitate applications of light-driven nanorods.

An application of natural matrices to the dynamic balance problem of planar parallel manipulators

Abstract This paper introduces a simplified matrix method for balancing forces and moments in planar parallel manipulators. The method resorts to Newton’s second law and the concept of angular momentum vector, yet it is not necessary to perform the velocity and acceleration analyses, tasks that were normally unavoidable in seminal contributions. With the introduction of natural matrices, the proposed balancing method is independent of the time and the trajectory generated by the moving links of parallel manipulators. The effectiveness of the method is exemplified by balancing two planar parallel manipulators.

Inverse kinematic solution and singularity avoidance using a deep deterministic policy gradient approach

<p>The robotic arm emerges as a subject of paramount significance within the industrial landscape, particularly in addressing the complexities of its kinematics. A significant research challenge lies in resolving the inverse kinematics of multiple degree of freedom (M-DOF) robotic arms. The inverse kinematics of M-DOF robotic arms pose a challenging problem to resolve, thus it involves consideration of singularities which affect the arm robot movement. This study aims a novel approach utilizing deep reinforcement learning (DRL) to tackle the inverse kinematic problem of the 6-DOF PUMA manipulator as a representative case within the M-DOF manipulator. The research employs Jacobian matrix for the kinematics system that can solve the singularity, and deep deterministic policy gradient (DDPG) as the kinematics solver. This chosen technique offers enhancing speed and ensuring stability. The results of inverse kinematic solution using DDPG were experimentally validated on a 6-DOF PUMA arm robot. The DDPG successfully solves inverse kinematic solution and avoids the singularity with 1,000 episodes and yielding a commendable total reward of 1,018.</p>

Combinatorial lower bounds for the Generalized Traveling Salesman Problem with Neighborhoods

In this paper, we study the Generalized Traveling Salesman Problem with Neighborhoods (GTSPN), a variant of the Traveling Salesman Problem (TSP), where the goal is to find the shortest path visiting each of the given neighborhood sets represented as a set of convex regions. The GTSPN is motivated by the sequencing problem of robotic manipulators, where an operation can be achieved from multiple locations, such as the visual inspection that can be performed from several possible views. The GTSPN formulation allows for exploiting continuous optimization to find the most suitable locations for the inspection, yielding possible solution cost reduction. Moreover, instances with overlapping high-dimensional convex regions further allow modeling neighborhood sets with complex shapes. We propose a novel approach to determine the first lower bounds to the studied GTSPN by employing the Branch-and-Bound (BB) method and the Mixed-Integer Second-Order Cone Programming (MISOCP) model for particular BB subproblems. In addition, the proposed method allows for solving the GTSPN to optima. The developed lower bound determination is further exploited in the empirical evaluation of existing heuristic approaches to the GTSPN and assesses the solution quality using the relative optimality gap. Regarding the presented results, the proposed BB-based approach provides tight lower bounds and solutions with up to 20% optimality gap for the GTSPN instances with less than 15 neighborhood sets for the given limited computational time. Furthermore, the presented results support that the proposed approach is suitable for solving high-dimensional instances of the GTSPN that can be found in inspection tasks with robotic manipulators.

The ethical motivation for post-normal science

This paper focuses on the concept of post-normal science, originally proposed by Silvio Funtowicz and Jerome Ravetz, as an advanced method of knowledge generation for policy, and reflects on the ethical motivations for both its theoretical meaning and its practical realisation. In order to put the reflection on the why and how of post-normal science in a broader contemporary and future-oriented context, I will first elaborate on what I call ‘the politics of hypothesis’ and argue that the fundamental challenge for science that aims to advice policy today is not the problem of strategic manipulation of scientific advice by politics, civil society or the market, but rather the problem of dealing with the lack of evidence in situations where politics, civil society or the market ‘need’ that evidence to (urgently) inform, criticise or justify specific actions or practices. Confronted with the need to deal with incomplete and speculative knowledge, in many cases, scientific hypotheses have become the ‘end products’ of science themselves, and society has no other choice than to deal with them in a responsible way. The challenge of science in these cases is therefore not any longer the production of convincing proofs, it is the construction of credible hypotheses. Against this backdrop, a second part will recall how the normative motivation for post-normal science was originally worked out by Funtowicz and Ravetz. I will re-emphasize why and how the argumentation of Funtowicz and Ravetz in favour of the democratisation of science and the opening up of the dialogue to include opinions, beliefs and lay knowledge of ‘non-experts’ is ethical. Consequently, the third part proposes some paths for further ethical reflection with regard to the value and meaning of post-normal science in the ‘post-normal age’. I will briefly elaborate on the concept of transdisciplinarity, the idea of ‘co-creation’ of complexity, the concept of holism and the idea of the unavoidable moral authority of the present generation in intergenerational ethics. The reason is that each of these explorations is at the same time and in its own way an invitation to reflect on who we are as humanity, and on how we can possibly make sense of things for the better. I hope to make clear not only how these concepts and ideas may inspire the ethics of post-normal science, but also that they should become topic of concern in post-normal science dialogues themselves. Finally, in conclusion, I will situate the original ethical motivation for post-normal science in a broader perspective on responsible knowledge generation ‘in face of’ the complexity of complex problems and argue that the overall ethical motivation for postnormal science is to enable an emancipatory and (respectfully) confrontational dialogue and not to come to a full understanding of the complexity of a complex problem or to ‘proof’ specific hypotheses. I will consequently suggest that, responding to the ‘ethical appeal’ of complexity, ‘reflexivity’ with respect to the own position, interests, strategies, hopes, hypotheses, believes and concerns would be the fundamental ethical attitude for anyone concerned. Reflexivity as a ‘quality’ of the science-policy interface does not only require science to reform in order to enable it to generate more reflexive policy advice, but also politics to reform into inclusive and deliberative forms of decision making prepared to become inspired and instructed by that reflexive policy advice. Post-normal science can in that sense be understood as a ‘method’ that can generate reflexivity at the science-policy interface and even as a deliberate political ‘act’ of knowledge generation involving science, political decision makers and civil society.

A learning-based nearly optimal control framework for trajectory tracking of a flexible-link manipulator system with actuator fault

In this paper, a learning-based nearly optimal control framework with fault-tolerant capability is designed to tackle the tracking control problem of a flexible-link manipulator in the presence of actuator fault and model uncertainties. Initially, the optimal control law is obtained by adopting the dynamic programming and a critic structure as the solution of Hamilton–Jacobi–Bellman equation for the nominal model. Then, by implementing an integral sliding mode control, the robustness against actuator fault and model uncertainty is guaranteed. The adaptive laws are constructed based on radial basis functions neural networks to estimate the upper bound of uncertainty and the actuator bias fault, satisfying both optimal performance and chattering reduction of the sliding surface. Furthermore, the actuator effectiveness loss is handled. The stability of the closed-loop system is analytically proven, and the performance of the proposed framework is investigated against several practical operating conditions. This incorporates the fidelity assessment of tracking precision and trackability of control signal using performance indices such as the integral absolute error and root-mean-square error. The results of extensive simulation studies confirm the effectiveness and robustness of the proposed control framework.

From constraints fusion to manifold optimization: A new directional transport manifold metaheuristic algorithm

The ascent of geometry-based models and methodologies, exemplified by geometric deep learning and manifold numerical optimization algorithms, has inaugurated a novel domain across various applications that grapple with geometric data complexities, such as electroencephalogram signals represented by symmetric positive definite matrix manifold, hierarchical data represented by hyperbolic manifold. The imperative fuels this inevitable paradigm shift to encapsulate the intricacies and richness inherent in data, areas where traditional methods prove inadequate. While metaheuristic algorithms are renowned for their versatile adaptability across applications, offering practical solutions within reasonable timeframes. However, the conventional metaheuristic algorithms fail on manifold applications with meaningless solutions. From an extrinsic optimization perspective, we treat manifold optimization problems as general optimization problems with multiple fused constraints that limit the optimization path to the manifold. This study pioneered the proposal and implementation of a metaheuristic manifold optimization, introducing a novel directional transport operator to rectify previously identified issues. Through experimentation across five sets of 25 problems, comparing against five algorithms, including both gradient-free and gradient-dominant counterparts, our proposed algorithm emerges as the optimal performer within the gradient-free category, demonstrating competitiveness even against gradient-dominant algorithms. Furthermore, we applied the proposed algorithm to the robot dynamic manipulation problem, achieving a close-optimal solution that eludes gradient-dominant approaches. This paper delves into the inherent capabilities and establishes the generalization of a metaheuristic algorithm within non-Euclidean functional landscapes. The source code will be available at https://github.com/lingping-fuzzy/metaheuristic-manifold-optimization.

Why Do Companies Cook the Books? Empirical Study of the Motives of Creative Accounting of Slovak Companies

Studies on creative accounting date back to the latter part of the 20th century. Creative accounting is still a big challenge in financial accounting. The problem of financial statement manipulation might be investigated, for instance, from an accounting, legal, ethical, or psychological perspective. This research aims to identify the main motives for the use of creative accounting and to find out whether corporate culture has an impact on the motives leading to the use of creative accounting. Data collection took place from 18 November 2023 to 18 December 2022. In the research, we used Pearson’s χ2 test to determine the dependence of the studied variables in contingency tables. Subsequently, correspondence analysis was used. The type of corporate culture does not have an impact on the motives that lead to creative accounting. It was proven that the type of corporate culture has an impact on the performance of creative accounting actions based on the request of a senior employee. The uniqueness of the research lies in the investigation of creative accounting from a psychological and managerial point of view in the territory of the Slovak Republic.

Noise suppression zeroing neural network for online solving the time-varying inverse kinematics problem of four-wheel mobile manipulators with external disturbances

A novel noise suppression zeroing neural network (NSZNN) is presented for the trajectory tracking problem on a four Mecanum wheeled mobile manipulator (FMWMM) by solving its time-varying inverse kinematics (TVIK) problem. The holistic kinematic model of the FMWMM is developed, which can receive synergistic control of the mobile manipulator. Different from the situation without external interference addressed in our previous work, this paper considers a variety of common time-varying interferences by studying the basic principles of various noises, and proves the NSZNN model’s of the validity and superiority, which solves the TVIK problem of the FMWMM with external disturbances through theoretical analyses. Compared with the existing gradient neural network (GNN) and the traditional zeroing neural network (ZNN), the most representative hybrid noise is selected to conduct a large number of experiments to substantiate the high efficiency and robustness of the NSZNN model. Finally, the NSZNN model is verified on the FMWMM via a robot operating system (ROS) by a successful execution of the trajectory tracking task.

Objective acceptability indexes

Based on the financial efficiency and leverage effect, this study classifies the measures of reward, risk, and performance into “simple” and “compound” types. We generalize three problems of observation inconsistency, overleverage deception, and dynamic manipulation behind classical simple measures (such as variability, LPM, VaR, and regression-based measures), and two problems of observation inconsistency and leverage variance behind traditional drawdown-based compound measures. We employ an axiomatic approach to answer the question of what undesirable influences measures should avoid, and complement the axiomatic framework with objective acceptability indexes by separating useless influences that do not add value for investors. We also develop new compound measures to solve the general problems with empirical evidence.

Detection Method of Manipulator Grasp Pose Based on RGB-D Image

In order to better solve the visual detection problem of manipulator grasping non-cooperative targets, we propose a method of grasp pose detection based on pixel point and feature fusion. By using the improved U2net network as the backbone for feature extraction and feature fusion of the input image, and the grasp prediction layer detects the grasp pose on each pixel. In order to adapt the U2net to grasp pose detection and improve its detection performance, we improve detection speed and control sampling depth by simplifying its network structure, while retaining some shallow features in feature fusion to enhance its feature extraction capability. We introduce depthwise separable convolution in the grasp prediction layer, further fusing the features extracted from the backbone to obtain predictive feature maps with stronger feature expressiveness. FocalLoss is selected as the loss function to solve the problem of unbalanced positive and negative samples in network training. We use the Cornell dataset for training and testing, perform pixel-level labeling on the image, and replace the labels that are not conducive to the actual grasping. This adaptation helps the dataset better suit the network training and testing while meeting the real-world grasping requirements of the manipulator. The evaluation results on image-wise and object-wise are 95.65% and 91.20% respectively, and the detection speed is 0.007 s/frame. We also used the method for actual manipulator grasping experiments. The results show that our method has improved accuracy and speed compared with previous methods, and has strong generalization ability and portability.

Contact Force Estimation of Robot Manipulators With Imperfect Dynamic Model: On Gaussian Process Adaptive Disturbance Kalman Filter

This paper is concerned with the contact force estimation problem of robot manipulators based on imperfect dynamic models of the manipulator and the contact force. To handle the imperfect dynamic information of the manipulator, a hybrid model, consisting of the nominal model and the residual dynamics, is established for the manipulator, and the Gaussian process regression (GPR) technique is employed to learn the mean and covariance of the residual dynamics. On this basis, a virtual measurement equation is established for contact force estimation and a Gaussian process adaptive disturbance Kalman filter (GPADKF) is developed where the variational Bayes technique is employed to achieve online identification of the noise statistics in the force dynamics. The GPADKF is capable of decoupling the contact force from residual dynamics and system noises, thereby reducing the dependence on accurate dynamic models of the manipulator and the contact force. Simulation and experimental results demonstrate that the proposed scheme outperforms the state-of-art methods. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Contact force estimation for robot manipulators can be achieved by fusing the dynamic models of the manipulator and the contact force. When both models are imprecise, the traditional inverse dynamics-based and disturbance Kalman filter-based approaches can no longer provide accurate force estimates. To handle this challenge, a computationally efficient hybrid dynamic model is established for the manipulator, which consists of the nominal model and a residual dynamics compensation term learned from offline data via the GPR. On this basis, an adaptive disturbance Kalman filter is constructed by using the variational Bayes technique to deal with the inaccurate noise covariance matrix in the force dynamic model. Compared with the existing approaches, the force estimate obtained via the proposed scheme is more accurate and reliable, as refined noise covariance matrices (provided by both the GPR and the variational Bayes procedure) have been adopted in the Kalman gain calculation. The proposed GPADKF method is the extension of the composite disturbance filtering (CDF) framework. With the proposed scheme, the dependency on the perfect dynamic models in contact force estimation can be significantly reduced, and this makes our approach especially suitable for contact force estimation problems under unfamiliar and complicated environments.

Optimization of robot manipulator configuration calibration by using Zhang neural network for repetitive motion

High precision and low complexity control algorithm plays an important role in the developing of the end-effector instrumentation of different robot manipulators. In order to reduce the kinetic energy and the high-speed drift phenomenon of the repetitive motion tracking task, the robot manipulator needs to calibrate its configuration. In this paper, we formulate the configuration calibration of the robot manipulator for the repetitive motion task as a future quadratic programming optimization problem constrained with equality constraints, which is also regarded as a fundamental problem in artificial intelligence and modern control engineering. Zhang neural network, which is a canonical method, can be adopted to deal with the continuous form of the future optimization problem, named as temporally dependent quadratic programming problem with equality constraints. In order to overcome the issue of temporally dependent inverse computing, a novel Zhang neural network model and its uncertain disturbance tolerant model, which are termed as filtered reciprocal-kind Zhang neural network model and uncertain disturbance tolerant filtered reciprocal-kind Zhang neural network model, respectively, are proposed by integrating the energy-type cost function and Zhang neural network design formula for solving the temporally dependent quadratic programming problem with equality constraints in this paper. Based on the Euler discrete formula and the models, the discrete filtered reciprocal-kind Zhang neural network and the discrete uncertain disturbance tolerant filtered reciprocal-kind Zhang neural network algorithms are proposed for solving the future quadratic programming problem with equality constraints and the robot manipulator configuration calibration problem of repetitive motion. The convergence properties of the reciprocal-kind Zhang neural network model and its corresponding uncertain disturbance tolerant model are obtained by Lyapunov stability theory of nonlinear system and its corresponding perturbed system, while the convergence property of the filtered reciprocal-kind Zhang neural network model is analyzed by the limit thinking. For the repetitive motion task, three experiments for solving the configuration calibration problem of PUMA560, Kinova Jaco2, and Franka Emika Panda robot manipulators are performed to illustrate the effectiveness, robustness and superiority of our proposed discrete filtered reciprocal-kind Zhang neural network algorithms.

Observer based output feedback repetitive learning control of robotic manipulators

AbstractThis work tackles the tracking control problem of robotic manipulators where the robot dynamics contains uncertain parameters and joint velocity measurements are not available. Specifically when the robotic manipulator is required to perform a periodic task repetitively, as in most industrial applications, a repetitive learning controller is proposed that does not require joint velocity measurements and can compensate the uncertainties of the robot dynamical parameters and additive disturbances caused due to the periodic joint motion. The proposed solution is achieved via the use of a novel learning component in the controller design in conjunction with a novel model‐free joint velocity observer design. The stability of the closed‐loop system and the convergence of both the joint position tracking error and the joint velocity observation error to the origin are guaranteed via Lyapunov based arguments. Experimental results performed on a 2 degree of freedom robot manipulator are presented to demonstrate the performance of the proposed observer–controller couple.

An Adaptive Discrete Integral Terminal Sliding Mode Control Method for a Two-Joint Manipulator

In response to the trajectory tracking control problem of manipulators under measurement disturbances, a novel multi-input multi-output discrete integral terminal sliding mode control scheme is proposed. Initially, this scheme establishes a dynamic model of a two-joint manipulator based on the Lagrangian dynamics analysis method. Subsequently, a discrete integral terminal sliding mode control law based on the dynamic model of the two joints is designed, incorporating delayed estimation of unknown disturbances and discretization errors in the manipulator system. To enhance the trajectory tracking accuracy of the control scheme and suppress the impact of sliding mode chattering on the manipulator system, an adaptive switching term is introduced into the discrete integral terminal sliding mode control law. The paper derives an adaptive discrete integral terminal sliding mode control scheme and provides stability proof for the proposed approach. Simulation experiments are conducted to compare the proposed adaptive discrete integral terminal sliding mode control scheme with classical discrete sliding mode control schemes and discrete integral terminal sliding mode control schemes. The simulation results demonstrate that the designed adaptive discrete integral terminal sliding mode control scheme maintains trajectory tracking errors within 0.004 radians for each joint of the manipulator, with minimal changes in control torque for each joint. The absolute integral of the control torque variations is calculated at 5.85×103, which is lower than other control schemes, thereby validating the effectiveness and superiority of the proposed approach.

Nonlinear optimal control for robotic exoskeletons with electropneumatic actuators

Nonlinear optimal control for robotic exoskeletons with electropneumatic actuators