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Copyright of International Journal of Engineering Design & Technology (IJEDT) is the property of Burdur Mehmet Akif Ersoy University, Faculty of Architecture & Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the advent of Industry 4.0, intelligent manufacturing has emerged as a prominent trend for future development. The integration of intelligent manufacturing scenarios with reinforcement learning offers significant advantages and potential. However, in real world scenarios, reinforcement learning faces challenges in terms of sampling efficiency and potential mechanical damage. Model-based reinforcement learning demonstrates advantages that make it more applicable to real scenarios. In this study, we propose the improved Dreamer algorithm and develop a system for learning picking and placing operations using multimodal information. The enhanced algorithm efficiently mitigates the challenge of low sample efficiency and demonstrates remarkable efficacy in addressing high-dimensional state space problems. Furthermore, the policy acquired through reinforcement learning can be utilised for the manipulation of object with diverse geometries. To verify the effectiveness of the algorithm, we construct a production line simulation environment for robotic arm manipulation based on the Coppeliasim platform. Additionally, the advantages of the improved algorithm are further validated through the continuous complex control tasks in DeepMind. [ABSTRACT FROM AUTHOR]

Robotic arms are now commonplace in diverse settings and are poised to play a crucial role in automating laboratory tasks. However, biological experiments remain challenging for automation due to their dependence on human factors, such as researchers' skills and experience. This article introduces robotic automation and remote control for both general and biological research tasks through a modularized platform comprising a robotic arm, auxiliary tools, and software. This platform facilitates fully automated or remote execution of key experiments in chemistry and biology, including liquid handling, mixing, cell seeding, culturing, and genetic manipulation. The robot interfaces seamlessly with standard laboratory equipment and operates remotely in real time through an online program. Integration of a vision system via robotic arm webcams ensures precise positioning and object localization, enhancing accuracy. This modularized robotic platform signifies a substantial advancement in lab automation, promising enhanced efficiency, reproducibility, and scientific progress compared to human‐led experiments. [ABSTRACT FROM AUTHOR]

Robotic arms are now commonplace in diverse settings and are poised to play a crucial role in automating laboratory tasks. However, biological experiments remain challenging for automation due to their dependence on human factors, such as researchers’ skills and experience. This article introduces robotic automation and remote control for both general and biological research tasks through a modularized platform comprising a robotic arm, auxiliary tools, and software. This platform facilitates fully automated or remote execution of key experiments in chemistry and biology, including liquid handling, mixing, cell seeding, culturing, and genetic manipulation. The robot interfaces seamlessly with standard laboratory equipment and operates remotely in real time through an online program. Integration of a vision system via robotic arm webcams ensures precise positioning and object localization, enhancing accuracy. This modularized robotic platform signifies a substantial advancement in lab automation, promising enhanced efficiency, reproducibility, and scientific progress compared to human‐led experiments.

In this article, we show the use of the recently developed enhanced extended observer design technique in tracking time‐dependent references by a PMSM‐actuated robot arm. It is proven that this robust control framework guarantees achievement of semiglobal practical stability and makes trajectories of the system arbitrarily close to those that would had been obtained by the non‐robust nominal feedback‐linearizing control if there were no plant uncertainties. [ABSTRACT FROM AUTHOR]

The principal objective of the paper is to show the importance of the Hamiltonian in control theory. Instead of using the Lagrangian formulation of electromechanical or robotic systems, our work is focused on robot dynamics by its Hamiltonian. Using the iterative Newton–Euler, we generate the local Hamiltonians and the derivative of the moments at each joint of the robot manipulator. Thus, we can apply decentralized controllers at each joint. We compare and discuss the efficiency of the controllers. We show that the performance of the sliding modes controller is more robust than that of the PD or Bang–Bang controllers. [ABSTRACT FROM AUTHOR]

Robot and programming education, as a key part of STEM education, is attracting more and more attention in the education industry. In this paper, a novel open-sourced educational robotic platform, Daran robot, is proposed with key features in terms of reconfigurable, powerful, and affordable. As an entry-level robotic platform, the Daran robot consists of three individual robots, which are a Mecanum-wheeled robot, a three-wheeled robot, and a 4-DoF robot arm. Both graphical and Python programming environments are developed for students with different entry levels. Thanks to the reconfigurability, four classic constructions of the Daran robot are presented with corresponding case studies, based on which the students can practically learn basic knowledge of sensing and control technologies.

Beyond providing accurate movements, achieving smooth motion trajectories is a long-standing goal of robotics control theory for arms aiming to replicate natural human movements. Drawing inspiration from biological agents, whose reaching control networks effortlessly give rise to smooth and precise movements, can simplify these control objectives for robot arms. Neuromorphic processors, which mimic the brain’s computational principles, are an ideal platform to approximate the accuracy and smoothness of biological controllers while maximizing their energy efficiency and robustness. However, the incompatibility of conventional control methods with neuromorphic hardware limits the computational efficiency and explainability of their existing adaptations. In contrast, the neuronal subnetworks underlying smooth and accurate reaching movements are effective, minimal, and inherently compatible with neuromorphic hardware. In this work, we emulate these networks with a biologically realistic spiking neural network for motor control on neuromorphic hardware. The proposed controller incorporates experimentally-identified short-term synaptic plasticity and specialized neurons that regulate sensory feedback gain to provide smooth and accurate joint control across a wide motion range. Concurrently, it preserves the minimal complexity of its biological counterpart and is directly deployable on Intel’s neuromorphic processor. Using the joint controller as a building block and inspired by joint coordination in human arms, we scaled up this approach to control real-world robot arms. The trajectories and smooth, bell-shaped velocity profiles of the resulting motions resembled those of humans, verifying the biological relevance of the controller. Notably, the method achieved state-of-the-art control performance while decreasing the motion jerk by 19% to improve motion smoothness. Overall, this work suggests that control solutions inspired by experimentally identified neuronal architectures can provide effective, neuromorphic-controlled robots.

Robots with remote access have the ability to perform tasks that are otherwise hazardous for human life. This project aims to develop an electrical pole climbing robot, which can help to reduce the risk of on electrician while carrying out maintenance activity on distribution lines. The robot will be monitored by IP camera with live access to control the movements. This robot is an improved model, which work on the principle of the linear motor and partially autonomous. This robot uses web camera for transmitting the visual assistance by the operator and has an on-board IoT device for semi-autonomous function, which includes ATmega32 Microcontroller, and Wi-Fi Module-(ESP8266).This paper also suggested some alterations to the pole climbing robot, which can be used for surveillance in the forests, wind mill climbers and wall climbers. The paper also presents the simulation results of the pole climbing robot. [ABSTRACT FROM AUTHOR]

In this article, future different-layer linear equation and bounded inequality (DLLEBI) is investigated as a new and challenging problem. The continuous bounded inequality is converted into equality by introducing a time-variant nonnegative vector. A continuous zeroing neural network (CZNN) model is proposed for solving the corresponding continuous DLLEBI by utilizing the ZNN method. Adams–Bashforth (AB) methods are combined with the CZNN model to improve the computational precision. Hence, AB discrete ZNN (AB-DZNN) models are proposed to solve future DLLEBI. Specifically, a four-step AB-DZNN model with high precision is proposed. Three-, two-, and one-step AB-DZNN models are also developed for comparative analyses. Theoretical analyses and numerical results substantiate the validity and superiority of the proposed four-step AB-DZNN model for solving future DLLEBI. In addition, motion control problems of three-link, mobile, and physical Kinova JACO $^2$ robot arms are formulated as three specific future DLLEBI problems. These problems can be solved by the four proposed AB-DZNN models. Comparative numerical results provide further evidence that the proposed four-step AB-DZNN model has the most superior computational performance among the four AB-DZNN models. [ABSTRACT FROM AUTHOR]

The conventional limitations of the robotic actuation mechanisms have led to many researchers needing to explore novel biomimetic motor mechanisms as the antagonistic human motor system. In this way, it is of interest to understand the inherent adaptive stiffness, or compliance, and modulation, in different alternative actuation architectures such as the antagonistic bi-articular (AbA) system. These novel AbA actuation mechanisms are characterized by resembling the efficient tendon and muscle build-up over our skeletal structure. In this paper, we propose a Cartesian neuro-controller for a robot manipulator actuated by a simplified adaptive viscoelastic linear AbA system. It is shown that the adaptive closed-loop system enforces terminal attractors, induced by a continuous model-free sliding mode control, simultaneously with a learning algorithm to compensate parametric uncertainties of AbA system through a low dimensional neural network. Numerical simulation results exhibit the feasibility of this approach. [ABSTRACT FROM AUTHOR]

We introduce an ultrahigh-resolution (50 μm ) robotic micro-CT design for localized imaging of carotid plaques using robotic arms, cutting-edge detector, and machine learning technologies. To combat geometric error-induced artifacts in interior CT scans, we propose a data-driven geometry estimation method that maximizes the consistency between projection data and the reprojection counterparts of a reconstructed volume. Particularly, we use a normalized cross correlation metric to overcome the projection truncation effect. Our approach is validated on a robotic CT scan of a sacrificed mouse and a micro-CT phantom scan, both producing sharper images with finer details than that prior correction.

Robot manipulators are playing increasingly significant roles in scientific researches and engineering applications in recent years. Using manipulators to save labors and increase accuracies are becoming common practices in industry. Neural networks, which feature high-speed parallel distributed processing, and can be readily implemented by hardware, have been recognized as a powerful tool for real-time processing and successfully applied widely in various control systems. Particularly, using neural networks for the control of robot manipulators have attracted much attention and various related schemes and methods have been proposed and investigated. In this paper, we make a review of research progress about controlling manipulators by means of neural networks. The problem foundation of manipulator control and the theoretical ideas on using neural network to solve this problem are first analyzed and then the latest progresses on this topic in recent years are described and reviewed in detail. Finally, toward practical applications, some potential directions possibly deserving investigation in controlling manipulators by neural networks are pointed out and discussed. [ABSTRACT FROM AUTHOR]

The PID controller with constant feedback gains has withstood as the preferred choice for control of linear plants or linearized plants, and under certain conditions for non-linear ones, where the control of robotic arms excels. In this paper a model-free self-tuning PID controller is proposed for tracking tasks. The key idea is to exploit the passivity-based formulation for robotic arms in order to shape the damping injection to enforce dissipativity and to guarantee semiglobal exponential convergence in the sense of Lyapunov. It is shown that a neuro-fuzzy network can be used to tune dissipation rate gain through a self-tuning policy of a single gain. Experimental studies are presented to confirm the viability of the proposed approach. [ABSTRACT FROM AUTHOR]

Bi-articular actuators – actuator spanning two joints – play fundamental role in robot arms designed under the human musculoskeletal actuation paradigm. Unlike kinematic redundancy, actuator redundancy resulting from bi-articular actuation brings advantages such as increasing stability, reducing link's inertia, and decreasing non-linearity of the end-effector force with respect to the force direction. The traditional phase different control (PDC) resolves actuator redundancy on the basis of a linearized model derived from measured human muscle activity. Such linear model produces a non-zero error in calculation between a desired output force and necessary inputs. In this paper, the non-linear phase different control (NLPDC) is proposed to resolve actuator redundancy with no error. The maximum end-effector force of BiWi, bi-articularly actuated, and wire-driven arm, is measured using both PDC and NLPDC. When the robot arm moves towards singular configurations, the measured error in output force remains within the modeling error if using NLPDC, while such error increases significantly for PDC. Furthermore, unlike PDC, the proposed NLPDC allows design of joint stiffness and torque independently, reduction of necessary total muscle input force, and precise calculation of maximum output force. [ABSTRACT FROM PUBLISHER]

To solve the inverse kinematic problem of redundant robot manipulators, two redundancy-resolution schemes are investigated: one is resolved at joint-velocity level, and the other is resolved at joint-acceleration level. Both schemes are reformulated as a quadratic programming (QP) problem. Two recurrent neural networks (RNNs) are then developed for the online solution of the resultant QP problem. The first RNN solver is based on the gradient-descent method and is termed as gradient neural network (GNN). The other solver is based on Zhang 's neural-dynamic method and is termed as Zhang neural network (ZNN). The computer simulations performed on a three-link planar robot arm and the PUMA560 manipulator demonstrate the efficacy of the two redundancy-resolution schemes and two RNN QP-solvers presented, as well as the superiority of the ZNN QP-solver compared to the GNN one. More importantly, the simulation results show that the solutions of the two presented schemes fit well with each other, i.e., the two different-level redundancy-resolution schemes could be equivalent in some sense. The theoretical analysis based on the gradient-descent method and Zhang 's neural-dynamic method further substantiates the new finding about the different-level redundancy-resolution equivalence. [ABSTRACT FROM PUBLISHER]

The idea in this paper is to present some analytical and numerical results on the investigation of the dynamics of a nonlinear electromechanical system including a cantilever robot arm manipulator, harmonically actuated through an electric circuit. We use the method of harmonic balance to derive oscillatory solutions. Forced vibrations are analyzed showing that numerical results are in agreement with those obtained analytically for the stationary response. The system presents various types of nonlinear behaviors including chaos. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes the automation of a vibro-lance for ground improvement on a construction site. Here, a vibrating probe is lowered into the ground and penetrates downwards by means of a two-arm excavator, compacting the surrounding soil. The control system is straightforward to install on a conventional, hydraulically controlled excavator, such as the one utilised for the field tests reported in the paper. The new system supports the operator by automatically maintaining the verticality of the probe, increasing the speed of operation by a factor of three, whilst also yielding improved accuracy and, therefore, a potential increase in tool life. In particular, the research demonstrates the successful design and implementation of Proportional-Integral-Plus (PIP) control systems for joint control on-site. To the authors'' knowledge, this represents the first operational use of automation for vibro-lance ground compaction. [Copyright &y& Elsevier]

A description is presented of software and hardware components designed for enabling robots to perform interactive assembly tasks. Topics include architecture, functionality development tools, and the processes involving cognition, scheduling and execution.

Abstract: It is now well established that using actuators, which have faster speeds of response than d.c. servomotors, can solve the positional errors of the robot arms. One of the possible robotic actuators can be an electro-rheological (ER) clutch. To justify this objective, the authors measured the output torque response of a co-axial ER clutch. However, due to the dynamic inefficiency of a torque transducer, the measured torque response is inaccurate for analytical studies. Therefore, this measured torque is signal processed by using the transfer functions of this torque sensor and a filter to yield the ER torque response. The validity of this ER torque is investigated by comparing the numerical errors between the measured torque and its inverse torque response. From the torque error analysis, it is concluded that the ER clutch can be an actuator to improve the positioning accuracies of the robot arms. [Copyright &y& Elsevier]

Positioning accuracies of the robot arms are important at fast maneuvering speeds of the manipulators. In the past, many researchers had investigated the precisions of the robotic arms. They reported that these robotic positioning errors are caused by the dynamic inefficiencies of the robotic actuators; DC servomotors. Therefore, in order to resolve this actuator's issue, a twin electro-rheological (ER) clutch mechanism that consists of two counter-rotating ER clutches is employed. This is due to its output dynamics being unaffected by the output cogging torque of the driving motors. Also, this clutch mechanism can perform rapid reversal responses in bi-directional mode. In this present paper, two kinematics models that describe the output angular velocity and displacement behaviors of this clutch mechanism undergone validations. Next, the trend studies of these angular kinematics responses are conducted in order to determine the methodology to generate the fastest reversal motions of the clutch mechanism. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to an online trajectory planning of robot arms for the interception of a fast- maneuvering object under torque and velocity constraints. A body axis is newly introduced as a trajectory-planning coordinate in order to meet the position and the velocity matching conditions for a smooth grasp of the fast-maneuvering object. Using the position of the object and the end-effector in the inertia axis, the acceleration commands are generated in the X-, Y-, and Z-directions of the body axis and the acceleration commands are modified considering the torque and the velocity constraints. The trajectory planning in the X-direction becomes the speed planning to achieve the maximum speed, whereas the trajectory planning in the Y- and Z-directions becomes the direction planning where a missile-guidance algorithm is employed to intercept the maneuvering object. Finally, the acceleration commands in the body axis are transformed into the angle commands of the end-effector in the joint axis, which is used as the actual trajectory commands in robot arms. [ABSTRACT FROM AUTHOR]

Robust control of robots including motor dynamics is considered based on a dynamic game approach. The control law is composed of three main components. The first component utilizes feedback linearization to cancel the system''s nominal nonlinearity and achieve tracking via pole-placement. The discrepancy between the nominal functions used in feedback linearization and the true ones is compensated for using the second control component that is based on an adaptive hierarchical fuzzy algorithm. The third control component ensures robustness by attenuating the worst-case effect of both the residue due to fuzzy cancellation and the external disturbances. The main contribution of this work is the derivation of the hierarchical adaptive fuzzy algorithm to avoid the rule explosion phenomenon that characterizes traditional fuzzy systems. We also prove that the proposed control algorithm is locally stable. Simulation of a two-link manipulator illustrates a successful performance of our controller and investigates the effect of the design parameters. [Copyright &y& Elsevier]

A variable viscous damper using electrorheological fluid is developed to suppress vibrations of industrial robots. An integrated system consisting of drive motor, harmonic drive, arm linkage, and electrorheological (ER) damper is experimentally measured, and it is found that the resonant/antiresonant phenomenon is effectively reduced by increasing the viscosity of the ER damper. A position control system based on the conventional semi-closed-loop control with this new type of ER damper is analyzed, and the relationship between the properties of the control system and the ER damping coefficient is discussed by means of pole-assignment technique. It is shown from analysis and experiment that the ER damper contributes both to vibration suppression and disturbance rejection [ABSTRACT FROM PUBLISHER]

Abstract- When a human operator performs a task via a bilateral manipulator, the "feel" of the task is embodied in the mechanical impedance of the manipulator. Traditionally, a bilateral manipulator is designed for transparency; i.e., so that the impedance reflected through the manipulator closely approximates that of the task. "Impedance shaping bilateral control," introduced here, differs in that it treats the bilateral manipulator as a means of constructively altering the impedance of a task. This concept is particularly valuable if the characteristic dimensions (e.g., force, length, time) of the task impedance are very different from those of the human limb. It is shown that a general form of impedance shaping control consists of a conventional power-scaling bilateral controller augmented with a real-time interactive task simulation (i.e., a virtual environment). An approach to impedance shaping based on kinematic similarity between tasks of different scale is introduced and illustrated with an example. It is shown that an important consideration in impedance shaping controller design is robustness; i.e., guaranteeing the stability of the operator/manipulator/task system. A general condition for the robustness of a bilateral manipulator is derived. This condition is based on the structured singular value ([mu]). An example of robust impedance shaping bilateral control is presented and discussed.

Abstract - Force/position control strategies provide an effective framework to deal with tasks involving interaction with the environment. In this paper the parallel approach to force/position control of robotic manipulators is presented. It allows a complete use of the available sensor measurements by operating the control action in a full dimensional space without using selection matrices. Conflicting situations between the position and force tasks are managed using a priority strategy: the force control loop is designed to prevail over the position control loop. This choice ensures limited deviations from the prescribed force trajectory in every situation, guaranteeing automatic recovery from unplanned collisions. A dynamic force/position parallel control law is presented and its performance in presence of an elastic environment is analyzed; simplification of the dynamic control law is also discussed leading to a PID-type parallel controller. Two case studies are worked out that show the effectiveness of the approach in application to an industrial robot.

An open-loop control system is designed using frequency-domain techniques to compute a desired hub torque profile. The motion of the arm after tip impact, including hub actuation, is simulated. Simulation results are compared to experimental data. It is shown that the controller damps vibration caused by the impact and allows the steady-state tip deflection to be predetermined.

The paper presents a systematic approach for the numerical kinematic analysis of general parallel robotic manipulators. This approach consists of two parts. The first part deals with the structural analysis. Based on graph theory and the Depth First Search algorithm, a method for identifying and orientating the independent kinematic loops of the robot is developed. This method not only facilitates the assignment of the local coordinate systems attached to the robot, but also arranges them in the correct order to allow efficient recursive coordinate transformation. The second part of this approach deals with the displacement analysis. A set of recursion formulae is first developed for computing the forward coordinate transformations, and these formulae are then adopted in a two-phase computational algorithm to obtain the numerical solutions to the direct and the inverse kinematics problems. The two-phase algorithm developed in this paper is not only insensitive to the initial approximation of the solution vector, but also gives a rapid convergence rate. Furthermore, it is also useful for finding multiple solutions for the robot as well as for continuous trajectory planning, as shown by the numerical examples presented in this paper.

In this work, we prove that if the manipulator Jacobian is properly scaled, the largest singular value is bounded within a small interval close to unity. In this case, the inverse of the smallest singular value is a good estimate of the condition number. This is useful in singularity analysis. Bounds are derived for a general six-joint manipulator and for the ABB IRb 2000 industrial robot in a case study.

This paper presents a new model-based adaptive controller and proof of its global asymptotic stability with respect to the standard rigid-body model of robot-arm dynamics. Experimental data from a study of one new and several established globally asymptotically stable adaptive controllers on two very different robot arms 1) demonstrate the superior tracking performance afforded by the model-based algorithms over conventional PD control, 2) demonstrate and compare the superior performance of adaptive model-based algorithms over their nonadaptive counterparts, 3) reconcile several previous contrasting empirical studies, and 4) examine contexts that compromise their advantage.