Degrees Of Freedom Robot Manipulator Research Articles

Inverse kinematics of six degrees of freedom robot manipulator based on improved dung beetle optimizer algorithm

Inverse kinematics is a basic problem in robotics, which aims to solve the robot’s joint angles according to the end effector’s position and orientation. This paper proposed an improved spiral search multi-strategy dung beetle optimizer (DBO) algorithm for solving the inverse kinematics problem. The improved DBO algorithm considers not only the error between the target value and the current value but also the previous position of the robot to ensure minimum displacement during the movement. To solve the end position error and orientation error of the robot end effector more accurately, the quaternion is introduced as a penalty factor in the optimization objective function, which is of great significance for reducing the orientation error. Through the improved DBO algorithm, the position error is still accurate, and the orientation error is reduced from 9.5901 to 1.8718. Experimental results show that the proposed algorithm outperforms other swarm-intelligent algorithms in terms of accuracy and convergence speed. Overall, the proposed spiral search multi-strategy DBO algorithm provides an effective and efficient solution to the inverse kinematics problem in robotics.

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.

THE ROLE OF THE LATERAL ANKLE COMPLEX IN CONTROLLING LAXITY OF THE ANKLE JOINT: A BIOMECHANICAL INVESTIGATION

BackgroundLateral ankle instability is a common problem, but the precise role of the lateral ankle structures has not been accurately investigated. This study aimed to accurately investigate lateral ankle complex stability for the first time using a novel robotic testing platform.MethodA six degrees of freedom robot manipulator and a universal force/torque sensor were used to test 10 foot and ankle specimens. The system automatically defined the path of unloaded plantar/dorsi flexion. At four flexion angles: 20° dorsiflexion, neutral flexion, 20° and 40° of plantarflexion; anterior-posterior (90N), internal-external (5Nm) and inversion-eversion (8Nm) laxity were tested. The motion of the intact ankle was recorded first and then replayed following transection of the lateral retinaculum, Anterior Talofibular Ligament (ATFL) and Calcaneofibular Ligament (CFL). The decrease in force/torque reflected the contribution of the structure to restraining laxity. Data were analysed using repeated measures of variance and paired t-tests.ResultsThe ATFL was the primary restraint to anterior drawer (P< 0.01) and the CFL the primary restraint to inversion throughout range (P< 0.04), but with increased plantarflexion the ATFL's contribution increased. The ATFL had a significant role in resisting tibial external rotation, particularly at higher levels of plantarflexion, contributing 63% at 40° (P< 0.01). The CFL provided the greatest resistance to external tibial rotation, 22% at 40° plantarflexion (P< 0.01). The extensor retinaculum and skin did not offer significant restraint in any direction tested.ConclusionThis study shows accurately for the first time the significant role the ATFL and CFL have in rotational ankle stability. This significant loss in rotational stability may have implications in the aetiology of osteophyte formation and early degenerative changes in patients with chronic ankle instability. This is the first time the role of the lateral ankle complex has been quantified using a robotic testing platform.

Assistive feeding robot for upper limb impairment—Testing and validation

A personal care robotic system has been developed that can provide feeding assistance to those suffering from upper limb impairment. The system introduces a novel approach for feeding that prioritizes two ideas: generalized functionality to encompass multiple feeding tasks and seamless user interaction. Additionally, the system leveraged novel computer vision ideas to incorporate functionality that was not reported in the literature. For the functional prototype, the system was comprised of an off-the-shelf six degrees of freedom robotic manipulator, two depth cameras, and an electric gripper. Furthermore, various tools used during the operation were designed and constructed using a 3D printer. The system’s software has three main operation phases: food identification, acquisition, and delivery. One of the novel features of this system is that instead of attempting to identify the food, the robot identifies the method required for acquiring the food. During testing and validation, it was found that the system had minimal identification errors, high success rates for acquisition and delivery, and a fast safety response time.

Motion compensation system for robotic based in situ bioprinting to balance patient physiological movements

The aim of this study is to design and develop a robotic system capable of compensating a patient's periodic movement, such as a beating heart, breath-induced thoracic cavity motion, and able to avoid collisions in case of sudden and unexpected motions, caused by pain, tremor, or other diseases, during an in situ bioprinting process. Based on the previous work carried out on the IMAGObot platform (a 5 Degrees of Freedom robotic manipulator), the aim is to print on moving and non-planar surfaces, following the trajectory of a fiducial marker placed onto the patient, inside the robot workspace. For this purpose, a monocular vision system (featured by a webcam and fiducial markers positioned in the robot environment) and a software interface communicating with the robot controller were developed. The control algorithm was entirely developed in the Python environment using the OpenCV library for marker pose estimation and used to update the robot trajectory concerning the detected marker motion on LinuxCNC software. Moreover, in order to mimic the physiological displacement of a patient's rib cage due to breathing, a moving 3D-printed platform and a silicone chest phantom were fabricated. The motion compensation system was tested by regenerating a defect on the chest phantom during the respiratory phase through extrusion based in situ bioprinting.

Non-compact positively invariant sets and their application to the control of speed constrained mechanical systems with saturated actuators

This paper deals with the control of mechanical systems subject to speed constraints. The proposed control law is designed by using non-compact positively invariant sets. Non-compact sets are used to explore the position regulation control problem under speed constraints for a mechanical system using a nonlinear controller under the restriction that the speed on every joint may never transgress a preestablished bound. In contrast with compact set approaches, this is done while not imposing a bound on the position error. The nonlinear controller is verified through experimental evaluation on a two degree of freedom robotic manipulator.

A Deep Reinforcement-Learning Approach for Inverse Kinematics Solution of a High Degree of Freedom Robotic Manipulator

The foundation and emphasis of robotic manipulator control is Inverse Kinematics (IK). Due to the complexity of derivation, difficulty of computation, and redundancy, traditional IK solutions pose numerous challenges to the operation of a variety of robotic manipulators. This paper develops a Deep Reinforcement Learning (RL) approach for solving the IK problem of a 7-Degree of Freedom (DOF) robotic manipulator using Product of Exponentials (PoE) as a Forward Kinematics (FK) computation tool and the Deep Q-Network (DQN) as an IK solver. The selected approach is architecturally simpler, making it faster and easier to implement, as well as more stable, because it is less sensitive to hyperparameters than continuous action spaces algorithms. The algorithm is designed to produce joint-space trajectories from a given end-effector trajectory. Different network architectures were explored and the output of the DQN was implemented experimentally on a Sawyer robotic arm. The DQN was able to find different trajectories corresponding to a specified Cartesian path of the end-effector. The network agent was able to learn random Bézier and straight-line end-effector trajectories in a reasonable time frame with good accuracy, demonstrating that even though DQN is mainly used in discrete solution spaces, it could be applied to generate joint space trajectories.

Control Performance Assessment of Fractional-Order PID Controllers Applied to Tracking Trajectory Control of Robotic Systems

Performing tracking tasks in robotic manipulators presents many challenges for the controller design, especially in presence of external disturbances that affects the dynamical behavior of the robotic system. This paper presents the design of a Fractional-Order PID controller with the computed torque control strategy for the tracking control of a two degree of freedom robotic manipulator. The proposed technique is contrasted against the classical PID controller with the computed torque control strategy. To validate the proposed controllers, the robotic system is simulated using an MSC-ADAMS/MATLAB co-simulation model, which is employed for identification and control tasks. The proposed model is tested in presence of external disturbances in the applied torque, random noise in the feedback loop and payload variations. Obtained results show that the Fractional-Order PID controller with the computed torque control strategy has a better performance in presence of the analyzed external disturbances for tracking tasks.

Inverse Kinematic Solution of a 7 DOF Robotic Manipulator using Boundary Restricted Particle Swarm Optimization

Robotic manipulators have widespread applications in the industry, medical surgery, space exploration, and many more. It is an essential requirement for a manipulator to have high precision in positioning the end-effector at the desired location. The solution of this inverse kinematic problem is complex and computationally expensive. In this paper, a boundary restricted particle swarm optimization (BRPSO) is proposed for obtaining the solution of the inverse kinematic problem of a 7 degree of freedom (DOF) robotic manipulator. The boundary restriction method for the decision variables of the PSO algorithm is important as violations of boundaries may lead to infeasible solutions. Results show the proposed method is suitable for solving the inverse kinematic problem while producing feasible solutions within the respective physical limits of the joint angles of the considered 7 DOF manipulator. Furthermore, a comparative study of the positional errors of the end-effector obtained by the proposed method along with results obtained by algorithms like ABC, GSA, and different variants of PSO is presented. The results and the comparative analysis testify that the proposed method surpass some of the popular methods available in the literature.

Intelligent swarm algorithms for optimizing nonlinear sliding mode controller for robot manipulator

This work introduces an accurate and fast approach for optimizing the parameters of robot manipulator controller. The approach of sliding mode control (SMC) was proposed as it documented an effective tool for designing robust controllers for complex high-order linear and nonlinear dynamic systems operating under uncertain conditions. In this work Intelligent particle swarm optimization (PSO) and social spider optimization (SSO) were used for obtaining the best values for the parameters of sliding mode control (SMC) to achieve consistency, stability and robustness. Additional design of integral sliding mode control (ISMC) was implemented to the dynamic system to achieve the high control theory of sliding mode controller. For designing particle swarm optimizer (PSO) and social spider optimization (SSO) processes, mean square error performances index was considered. The effectiveness of the proposed system was tested with six degrees of freedom robot manipulator by using (PUMA) robot. The iteration of SSO and PSO algorithms with mean square error and objective function were obtained, with best fitness for (SSO) =4.4876 𝑒<sup>-6</sup> and (PSO)=3.4948 𝑒<sup>-4</sup>.

Implementation of six degree-of-freedom high-precision robotic phantom on commercial industrial robotic manipulator

This technical note discloses our implementation of a six degree-of-freedom (DOF) high-precision robotic phantom on a commercially available industrial robot manipulator. These manipulators are designed to optimize their set point tracking accuracy as it is the most important performance metric for industrial manipulators. Their in-house controllers are tuned to suppress its error less than a few tens of micrometers. However, the use of industrial robot manipulators in six DOF robotic phantom can be a difficult problem since their in-house controller are not optimized for continuous path tracking in general. Although instantaneous tracking error in a continuous path tracking task will not exceed five millimeters during motion with the in-house controller, it seriously matters for a robotic phantom, as the tracking error should remain within one millimeter in three dimensional space for all time during motion. The difficulty of the task is further increased since the reference trajectory of a robotic phantom, which is a six DOF tumor motion of a patient, cannot be as smooth as the ones used in factories. The present study presents a feedforward controller for a feedback-controlled industrial six DOF robotic manipulator to be used as a six DOF robotic phantom to drive the water equivalent phantom (WEP). We first trained a set of six recurrent neural networks (RNNs) to capture the six DOF input/output behavior of the robotic manipulator controlled by its in-house controller, and we proceed to formulate an iterative learning control (ILC) using the trained model to generate an augmented reference trajectory for a specific patient that enables very high tracking accuracy to that trajectory. Experimental evaluation results demonstrate clear improvements in the accuracy of the proposed robotic phantom compared to our previous robotic phantom, which uses the same manipulator but is driven by a different corrected reference trajectory.

A modified firefly algorithm for the inverse kinematics solutions of robotic manipulators

The inverse kinematics of robotic manipulators consists of finding a joint configuration to reach a desired end-effector pose. Since inverse kinematics is a complex non-linear problem with redundant solutions, sophisticated optimization techniques are often required to solve this problem; a possible solution can be found in metaheuristic algorithms. In this work, a modified version of the firefly algorithm for multimodal optimization is proposed to solve the inverse kinematics. This modified version can provide multiple joint configurations leading to the same end-effector pose, improving the classic firefly algorithm performance. Moreover, the proposed approach avoids singularities because it does not require any Jacobian matrix inversion, which is the main problem of conventional approaches. The proposed approach can be implemented in robotic manipulators composed of revolute or prismatic joints of n degrees of freedom considering joint limits constrains. Simulations with different robotic manipulators show the accuracy and robustness of the proposed approach. Additionally, non-parametric statistical tests are included to show that the proposed method has a statistically significant improvement over other multimodal optimization algorithms. Finally, real-time experiments on five degrees of freedom robotic manipulator illustrate the applicability of this approach.

Formulation of lagrangian equation and dynamic analysis of 2 - DoF Industrial robot using Roboanalyzer

Industrial robot is extensively used in all major manufacturing and assembly line throughout the world. In order to properly actuate the robot links, it is necessary to calculate the torque required at link joints. In this paper we consider a two degrees of freedom robot manipulator having two rotary joints J1 and J2. This is a classic example of inverse dynamics problem, inverse dynamics in robotics used to calculate torque or force required at joints from known values of link mass, velocity, and acceleration and we choose lagrangian formulation to solve equation of motion. In addition, Robo analyzer software we plot the torque values for predefined value of centre of gravity and link mass.

Robotic Detection and Grasp of Maize and Sorghum: Stem Measurement with Contact

Frequent measurements of the plant phenotypes make it possible to monitor plant status during the growing season. Stem diameter is an important proxy for overall plant biomass and health. However, the manual measurement of stem diameter in plants is time consuming, error prone, and laborious. The use of agricultural robots to automatically collect plant phenotypic data for trait measurements can overcome many of the drawbacks of manual phenotyping. The objective of this research was to develop a robotic system that can automatically detect and grasp the stem, and measure its diameter of maize and sorghum plants. The robotic system comprises of a four degree of freedom robotic manipulator, a time-of-flight camera for vision system, and a linear potentiometer sensor to measure the stem diameter. Deep learning and conventional image processing were used to detect stem in images and find grasping point of stem, respectively. An experiment was conducted in a greenhouse using maize and sorghum plants to evaluate the performance of the robotic system. The system demonstrated successful grasping of stem and a high correlation between manual and robotic measurements of diameter depicting its ability to be used as a prototype to integrate other sensors to measure different physiological and chemical attributes of the stem.

Adaptive high-order sliding mode control based on quasi-time delay estimation for uncertain robot manipulator

This paper presents the design, and validation of a new adaptive control system based on quasi-time delay estimation (QTDE) augmented with new integral second-order terminal sliding mode control (ISOTSMC) for a manipulator robot with unknown dynamic uncertainty and disturbances. Contrary to the conventional TDE, the proposed Q-TDE becomes sufficient to invoke a fixed artificial time delay and utilize the past data only of the control input to approximate the unknown system’s dynamic uncertainties. The incorporating of new adaptive reaching law with ISOTSMC augmented with Q-TDE policy ensures the continuous performance tracking of the robot manipulator’s trajectories using output feedback. This combination may achieve high performance with a significant chattering reducing procedure. By utilizing the Lyapunov function theory, it can be demonstrated that the robot system is stable and all signals in closed-loop are converging in finite time. Consequently, Simulation and comparative studies with two degrees of freedom robot manipulator were carried out to validate the effectiveness of the designed control scheme.

Downsizing an orbital space robot: A dynamic system based evaluation

Small space robots have the potential to revolutionise space exploration by facilitating the on-orbit assembly of infrastructure, in shorter time scales, at reduced costs. Their commercial appeal will be further improved if such a system is also capable of performing on-orbit servicing missions, in line with the current drive to limit space debris and prolong the lifetime of satellites already in orbit. Whilst there have been a limited number of successful demonstrations of technologies capable of these on-orbit operations, the systems remain large and bespoke. The recent surge in small satellite technologies is changing the economics of space and in the near future, downsizing a space robot might become be a viable option with a host of benefits. This industry wide shift means some of the technologies for use with a downsized space robot, such as power and communication subsystems, now exist. However, there are still dynamic and control issues that need to be overcome before a downsized space robot can be capable of undertaking useful missions. This paper first outlines these issues, before analyzing the effect of downsizing a system on its operational capability. Therefore presenting the smallest controllable system such that the benefits of a small space robot can be achieved with current technologies. The sizing of the base spacecraft and manipulator are addressed here. The design presented consists of a 3 link, 6 degrees of freedom robotic manipulator mounted on a 12U form factor satellite. The feasibility of this 12U space robot was evaluated in simulation and the in-depth results presented here support the hypothesis that a small space robot is a viable solution for in-orbit operations.

Deep Learning Aided Dynamic Parameter Identification of 6-DOF Robot Manipulators

Generally, structural uncertainty of the robot dynamics system refers to model error caused by parameter identification, unstructured uncertainty is the unmodeled dynamic characteristic. No matter how elaborate modeling methods are used, there always be uncertainty. Therefore, this article applies deep learning for the first time to aid robot dynamic parameter identification of 6 degrees of freedom robot manipulator for compensation of uncertain factors. Firstly, the relatively accurate prediction of torque is obtained by the physical dynamic model using the parameters identification method (errors are less than 10% of the maximum torques). Secondly, we propose a novel deep neural network based approach called Uncertainty Compensation Model (UCM) to compensate the torque error introduced by the uncertainty. The UCM mainly composed by proposed Input Control Module (ICM) and Error Learning Models (ELMs) based on Long-Short-Term Memory and attention mechanism. The proposed ICM, which effectively avoids the unnecessary interference, is used to control valid input for ELMs. The ELMs, consisted by ELM units, concern extracting salient features from sequence data to predict the joint error. Also, this article summarizes the effects of valid input, timestep and attention mechanism on the performance of the UCM. Finally, the verification of parameter identification and torques compensation is carried out by a Universal Robot 5 manipulator. Compared with the prediction torques of physical dynamic model, the proposed UCM has a good ability to capture the friction characteristics and compensate for the error of local maximum torques, which effectively solves the deficiency of the physical dynamics model and improves prediction accuracy (errors are less than 6% of the maximum torques).

Additive Manufacturing Path Generation for Robot Manipulators Based on CAD Models

Abstract Traditional extrusion based additive manufacturing (AM) is realized using a 3 degrees of freedom (DOF), translation only, 3D printer. It then follows that the printer must be larger than the printed part. One way of enabling AM on a larger scale is to combine AM with robotics. By using a 6 DOF robot manipulator to extrude a fast-curing material, the workspace of the build would be greatly expanded. In addition, since the structures would no longer have to be built with the bottom-up or top-down approach which is necessary for most existing forms of AM, the flexibility of the building process would also increase. This could possibly reduce the need for support structures to the point of only relying of anchoring and stabilizing. In this paper, a method for generating a path for AM using robot manipulators that takes advantage of the robot’s DOF is presented. The path is generated based on simple surfaces in CAD models. First, the surface is sampled and the samples are gathered in a point cloud. Then, a path is generated based on the point cloud. Three different approaches for generating a path are tested where the weighted greedy choice algorithm gave the most promising result. With this algorithm, printing along curved surfaces and in nonlinear paths are enabled.

Asymptotical stability contouring control of dual‐arm robot with holonomic constraints: modified distributed control framework

The authors analyse the contour error dynamics in contouring control of dual-arm robotic manipulators with holonomic constraints. With the modified dynamics of robot, the dynamics of dual-arm robot with holonomic constraints is symetrically transformed into dynamics error problem by considering the equivalent error method. It becomes the control problem of stabilisation such that it is suitable for robust control approach to improve control performance in terms of contouring control, i.e. contour accuracy with high speed. The proposed method can deal with both analytic and non-analytic functions of desired path in task space. The experimental results reveal that the proposed method yields excellent performance in comparison to conventional distributed control. As a result, the proposed method can lead to the development of a modified distributed control for high degree of freedom robot manipulators.

A Robotic Platform for 3D Forelimb Rehabilitation with Rats.

In an attempt to promote greater functional recovery after spinal cord injury, researchers have begun exploring combinatorial treatments, such as robotic rehabilitation combined with stem cell transplantation. Since these treatment methods are in their nascent stages, rodent models have been proposed for initial investigations. Robots have been built for locomotion rehabilitation and planar forelimb reach and grasp assessment with rodents; however, a robotic platform suitable for three-dimensional movement rehabilitation of the rodent forelimb has not yet been developed. In this paper, a novel three degree of freedom robotic manipulator for automated forelimb rehabilitation combined with stem cell transplantation after cervical spinal cord injury with rats is proposed. The robot interfaces with a rat in an end-effector manner, measuring and interacting with the forelimb in the 3D Cartesian space. In this work, we trained two rats through behavioral shaping to actively interact with the device during two robot control modes. This work provides preliminary investigations into the feasibility of 3D forelimb rehabilitation with rats, which could be translated as a paradigm for combinatorial treatments after spinal cord injury in a controlled manner.