Manipulation Algorithms Research Articles

An adaptive finite‐time stable control law for manipulator robots with unknown parameters

AbstractThis article develops an adaptive finite‐time stable control algorithm for an accurate manipulation of robot arms considering unknown model parameters and external disturbances. First, a state‐dependent twisting control algorithm is developed for the robust manipulation of robot arms under the consideration of bounded unknown uncertainties conditions. The more challenging problem of robust control design under consideration of totally unknown dynamical parameters is investigated in a second stage by supporting the control law with a suitable online parameters estimator. The objective of the parameters estimator is to drive the vector of estimated parameters to some level that ensures the establishment of real second‐order sliding mode without overloading the discontinuous controller gains. This approach based on combining both techniques of robust and adaptive algorithms allows overcoming large levels of uncertainties while preserving a high level of tracking accuracy without exciting the unwanted phenomena of chattering. The system finite‐time stability is proven by using Lyapunov theory and the weighted homogeneity principle. Practical tests are reported to support theoretical study results.

Application of Robust Discontinuous Control Algorithm for a 5-DOF Industrial Robotic Manipulator in real-time

Robotic manipulators are nonlinear systems that are extensively used in industrial applications to perform many complex and specialized tasks which requires high precision and accurate trajectory tracking. This paper proposes the real-time implementation of a Robust Discontinuous Controller Algorithm to achieve trajectory tracking for a 5 degrees of freedom (DOF) robotic manipulator in a fast and accurate form with low chattering on the joint motors even under uncertainty and disturbance conditions. The trajectory design is developed by a fifth-order polynomial which ensures that robotic manipulator reaches the final desired position in a smooth way and enables to specify the endpoint positions, speeds and accelerations for each joint. A Lyapunov analysis is performed to guarantee system stability with the proposed controller algorithm. To verify the effectiveness of the proposed controller algorithm some simulations in Simulink ${}^{{\circledR }}$ environment are presented in comparison with a PD controller which is widely used for control of industrial robotic manipulators. Real time experimental results in a robotic manipulator CRS Catalyst-5 by Thermo Electron ®; are presented to support the simulation results and prove the effectiveness of the proposed controller.

An obstacle avoidance algorithm for robot manipulators based on decision-making force

Obstacle avoidance is a significant skill not only for mobile robots but also for robot manipulators working in unstructured environments. Various algorithms have been proposed to solve off-line planning and on-line adaption problems. However, it is still not able to ensure safety and flexibility in complex scenarios. In this paper, a novel obstacle avoidance algorithm is proposed to improve the robustness and flexibility. The method contains three components: A closed-loop control system is used to filter the preplanned trajectory and ensure the smoothness and stability of the robot motion; the dynamic repulsion field is adopted to fulfill the robot with primitive obstacle avoidance capability; to mimic human’s complex obstacle avoidance behavior and instant decision-making mechanism, a parametrized decision-making force is introduced to optimize all the feasible motions. The algorithms were implemented in planar and spatial robot manipulators. The comparative results show the robot can not only track the task trajectory smoothly but also avoid obstacles in different configurations. • Decision-making force can prevent the robot from falling into the local minimum. • For the same configuration, DRDF can generate multiple feasible paths. • DRDF’s performance is verified in a simulation and experimental platform. • Compared with DMPSF and DMPDF, the DRDF shows more robustness and smoothness.

Deep Reinforcement Learning-Based Path Planning for Multi-Arm Manipulators with Periodically Moving Obstacles

In the workspace of robot manipulators in practice, it is common that there are both static and periodic moving obstacles. Existing results in the literature have been focusing mainly on the static obstacles. This paper is concerned with multi-arm manipulators with periodically moving obstacles. Due to the high-dimensional property and the moving obstacles, existing results suffer from finding the optimal path for given arbitrary starting and goal points. To solve the path planning problem, this paper presents a SAC-based (Soft actor–critic) path planning algorithm for multi-arm manipulators with periodically moving obstacles. In particular, the deep neural networks in the SAC are designed such that they utilize the position information of the moving obstacles over the past finite time horizon. In addition, the hindsight experience replay (HER) technique is employed to use the training data efficiently. In order to show the performance of the proposed SAC-based path planning, both simulation and experiment results using open manipulators are given.

Genetic Optimization of a Manipulator: Comparison between Straight, Rounded, and Curved Mechanism Links

There are several ubiquitous kinematic structures that are used in industrial robots, with the most prominent being a six-axis angular structure. However, researchers are experimenting with task-based mechanism synthesis that could provide higher efficiency with custom optimized manipulators. Many studies have focused on finding the most efficient optimization algorithm for task-based robot manipulators. These manipulators, however, are usually optimized from simple modular joints and links, without exploring more elaborate modules. Here, we show that link modules defined by small numbers of parameters have better performance than more complicated ones. We compare four different manipulator link types, namely basic predefined links with fixed dimensions, straight links that can be optimized for different lengths, rounded links, and links with a curvature defined by a Hermite spline. Manipulators are then built from these modules using a genetic algorithm and are optimized for three different tasks. The results demonstrate that manipulators built from simple links not only converge faster, which is expected given the fewer optimized parameters, but also converge on lower cost values.

Non-destructive Testing Assisted by Six-Axis Manipulator Based on POE Formula

At present, aluminum plates are widely used in aerospace, machinery, and other fields because of their light weight, good manufacturability, and strong corrosion resistance. However, during long-term use, defects that will affect the structural strength such as cracks are easy to appear. Therefore, it is necessary to conduct automatic non-destructive testing of large metal plates to determine the location, size, and structural damage to their defects. Compared with manual detection, automated detection has many advantages such as high detection efficiency, high repeatability, strong flexibility, and low labor intensity. Based on the spinor theory, this paper uses the product of exponentials (POE) to model the kinematics of the Aubo-i5 manipulator and obtain the positive kinematics equations, which are compiled by analytic geometry and algebraic methods combined with Paden-Kahan subproblems and matrix theory. The inverse kinematics algorithm of the 6R manipulator is based on this model. An automated non-destructive testing system including a robotic arm and ultrasonic phased array testing equipment was built. At the same time, a robotic arm simulation platform was built based on ROS, and the inverse kinematics algorithm was used to control the robotic arm through the MoveIt!. The angle error can be controlled within 2.5°.

PolyFrame, Efficient Computation for 3D Graphic Statics

In this paper, we introduce a structural form finding plugin called PolyFrame for the Rhinoceros software. This plugin is developed based on the methods of 3D Graphic Statics and Polyhedral Reciprocal Diagrams. The computational framework of this plugin uses new robust and efficient algorithms for the creation and modification of complex funicular, compression-only structural forms and is freely available for students, designers, researchers, and practitioners in the fields of architecture, structural engineering, mechanical engineering, and material science. The geometry-based structural design methods are one of the most intuitive yet powerful structural design methods that have recently been extended to 3D based on the Principles of the Equilibrium of Polyhedral Frames. Still, the increased geometrical complexities of the polyhedral diagrams hinder more in-depth practical applications and the research in this field. The framework proposed in this paper can manage, in near real-time, the creation and transformation of reciprocal polyhedral diagrams with a large number of elements as form and force diagrams for structural design purposes. The paper also introduces a hybrid object-oriented data structure that extends and generalizes the previously proposed approaches and thus allows the users to incorporate a variety of different geometric constraints, including edge lengths and the location of the supports from the initial stages of design. Additionally, a new parallel manipulation algorithm is introduced that is capable of transforming polyhedral diagrams while preserving the edge directions and face normal. As a result, a designer can effectively manipulate both structural form and its force distribution without breaking their reciprocity.

Improved Hybrid Trajectory Tracking Algorithm for a 3-link Manipulator Using Artificial Neural Network and Kalman Filter

Improved Hybrid Trajectory Tracking Algorithm for a 3-link Manipulator Using Artificial Neural Network and Kalman Filter

A Motion Planning Algorithm for Redundant Manipulators Using Rapidly Exploring Randomized Trees and Artificial Potential Fields

This paper presents a novel motion planner for redundant robotic manipulators by utilizing rapidly exploring randomized trees and artificial potential fields. Rapidly exploring randomized trees and artificial potential fields are two well-known navigational strategies in the robotics industry; however, their potential benefits and synergy when implemented together has been largely unexplored. In the proposed motion planner, rapidly exploring randomized trees is first used to determine a suitable path for the end-effector to follow that maneuvers around all obstacles in the robot's workspace. Once a path has been determined, attractive and repulsive potential fields are implemented at all points along the path and are used in a gradient optimization algorithm to determine joint trajectories to reach the desired location. To supplement the attractive and repulsive potential fields, an orientation field is proposed to minimize the error between the actual end-effector orientation and the desired orientation during joint trajectory planning. The motion planner is examined through both analytical and experimental evaluation using the 7 degrees of freedom Kinova JACO and Kinova Gen3 robotic arms. The conclusions drawn from this work substantiate the efficacy and superiority of the proposed two-stage motion planner for the control of redundant manipulators in obstacle-ridden environments.

Multi-dimensional cubic symmetric block cipher algorithm for encrypting big data

The advanced technology in the internet and social media, communication companies, health care records and cloud computing applications made the data around us increase dramatically every minute and continuously. These renewals big data involve sensitive information such as password, PIN number, credential numbers, secret identifications and etc. which require maintaining with some high secret procedures. The present paper involves proposing a secret multi-dimensional symmetric cipher with six dimensions as a cubic algorithm. The proposed algorithm works with the substitution permutation network (SPN) structure and supports a high processing data rate in six directions. The introduced algorithm includes six symmetry rounds transformations for encryption the plaintext, where each dimension represents an independent algorithm for big data manipulation. The proposed cipher deals with parallel encryption structures of the 128-bit data block for each dimension in order to handle large volumes of data. The submitted cipher compensates for six algorithms working simultaneously each with 128-bit according to various irreducible polynomials of order eight. The round transformation includes four main encryption stages where each stage with a cubic form of six dimensions.

Fault Diagnosis and Fault Tolerant Control for Manipulator with Actuator Multiplicative Fault

In this paper, a new fault diagnosis and fault tolerant control algorithm for manipulators with actuator multiplicative fault is proposed. The dynamic model of the manipulator with disturbance is taken as the research object. When faults occur in the actuator, a nonlinear observer based on radial basis function (RBF) neural network is used to estimate the fault information. After the fault information is obtained, an adaptive back-stepping sliding mode controller is used to control the manipulator to reach the desired trajectory. At last, an illustrated example is given to demonstrate the efficiency of the proposed algorithm, and satisfactory results have been obtained.

Prediction of gas-oil capillary pressure of carbonate rock using pore network modeling

This study suggests a frame work to predict the capillary pressure curve of the carbonate rocks using a pore network simulation. A novel pore network manipulation algorithm is introduced to match three curves and predict only one curve (air-oil). This implies that the matching process is much more complicated. In return, more valid results are obtained and the problem of non-uniqueness of statistical PNM approach is mitigated. Finally, a collection of the most significant parameters governing capillary pressure are tabulated.Two plugs with air-mercury, air-oil and water-oil drainage and water-oil imbibition capillary pressure tests are used. Water-oil drainage and imbibition and air-mercury drainage are matched with the same pore network. The pore network is constructed based on a statistical distribution of the network elements. Then, properties of air and oil are input to the pore network to estimate air-oil capillary pressure.Matching three capillary pressure curves using a network is a complicated task to do, especially when no 3D image of the plugs is available. On the other hand, it is evident by the acceptable results that the error of prediction is moderated using the suggested method. This estimation technic is a new application of pore network modeling that can be used for reservoirs with no 3D image of plugs and limited SCAL data.

Bit Manipulation: Conditional Statement using Bit-wise operators with C++

All of information that manipulated by a computer is represented in the form of bits, so in the programming language it is necessary to understand bitwise operations at the first. This paper aims to create a concept of making Conditional Statements with Bitwise operators in C ++. By doing so, we hope that people is easy to understand the operation behind conditional statements. A conditional operator is also known as a ternary operator. It takes three operands. A conditional operator is closely related with if else statement. The method used is a literature study studying the bit manipulation algorithm in the C ++ language. The results obtained are a function using bitwise operations in C ++ that implement conditional statements.

High-speed algorithms for automatic manipulators control systems

This article presents options for implementing high-speed algorithms for controlling automatic manipulators operating as part of robotic complexes (RTCs). Software control systems for automatic manipulators with the use of noise-resistant coding algorithms are considered in the conditions of operation in production conditions, when there is instability in the supply of energy resources, the presence of sources of network interference, interference that violates the safe working conditions of radio-electronic equipment. Examples of implemented control algorithms with different software cycles are given, regarding into account the peculiarities of production. A comparative analysis is made with analogs that have similar technological characteristics.

A Trajectory Planning Algorithm for Medical Manipulators Based on Adaptive Particle Swarm Optimization and Fuzzy Neural Network

A Trajectory Planning Algorithm for Medical Manipulators Based on Adaptive Particle Swarm Optimization and Fuzzy Neural Network

SplineLib: A modern multi-purpose C++ spline library

This paper provides the description of a novel, multi-purpose spline library. In accordance with the increasingly diverse modes of usage of splines, it is multi-purpose in the sense that it supports geometry representation, finite element analysis, and optimization. The library features reading and writing for various file formats and a wide range of spline manipulation algorithms. Further, a new efficient and objective-oriented algorithm for B-spline basis function evaluation is included. All features are available by a spline-type independent interface. The library is written in modern C++ with CMake as build system. This enables it for usage in typical scientific applications. It is provided as open-source library.

ModMan: An Advanced Reconfigurable Manipulator System With Genderless Connector and Automatic Kinematic Modeling Algorithm

With the current trend of dwindling life cycle of production, necessity of systems with high-adaptability is on the rise. With their high adaptability and easy maintenance, reconfigurable manipulators are strong candidates to replace conventional non-reconfigurable manipulators in such trend. However, most of existing reconfigurable robots are designed for non-industrial use and remained in laboratory level because of their low accuracy and low mechanical/electrical capacity. In this letter, we present our newly developed manually reconfigurable manipulator, ModMan, equipped with genderless connectors which feature high mechanical/electrical capacity and multi-DOF modules, which are to increase the number of possible configurations while minimizing loss of manipulator performance. An automatic kinematic modeling algorithm for reconfigurable manipulators is also presented to deal with complexities due to genderless connections and multi-DOF modules. Evaluations of repeatability of 6-DOF configuration are performed to prove that the performance of ModMan is comparable to existing non-reconfigurable manipulators. Experiments on reconfiguration of kinematics for arbitrary connections of modules are also demonstrated.

Development of innovative artificial neural networks for simultaneous determination of lapatinib and foretinib in human urine by micellar enhanced synchronous spectrofluorimetry.

A highly selective and simple micellar synchronous spectrofluorimetric method was described for simultaneous analysis of two tyrosine kinase inhibitors (TKIs); namely lapatinib (LPB) and foretinib (FTB) in human urine. The method depended on measuring synchronous fluorescence of the two drugs in micellar media composed of cremophor RH 40 (Cr RH 40) surfactant using feed-forward and cascade-forward neural networks preceded by genetic algorithm for data manipulation. Different experimental conditions that affect fluorescence of the cited drugs are optimized including pH, diluting solvent, surfactant's type and concentration. A training set of nine mixtures containing different concentrations of both drugs was prepared for models' construction. Extra validation set composed of other nine mixtures was prepared to validate prediction performance for the constructed models. Root mean square error of prediction (RMSEP) was used as a tool to compare prediction power of each model. The method was extended for quantification of LPB and FTB in spiked human urine.

An Obstacle Avoidance Algorithm for Manipulators Based on Six-Order Polynomial Trajectory Planning

Aiming at a series of requirements of obstacle avoidance trajectory planning of manipulators, a new algorithm based on six-order polynomial trajectory planning is proposed. Firstly, the six-order polynomial is used for the trajectory planning of the manipulator. Assuming that the coefficients of the sixth order term in the curve equation are undetermined parameters, by adjusting these parameters, the shape of the curve can be changed to make manipulators avoid the obstacle and to optimize performance indicators of the trajectory simultaneously. Thus, the obstacle avoidance trajectory planning of manipulators is transformed into a multi-objective optimization problem. Secondly, combining collision detection results and kinematics indexes, a fitness function is defined by the weighting coefficient method. At last, an ideal collision-free trajectory that is collaborative optimized in kinematics, trajectory length and rotation angle is planned in the joint space through genetic algorithm optimization. Additionally, the algorithm is validated by simulation experiments with MATLAB, the results show that the method of this study can effectively plan obstacle-free trajectories satisfying the performance requirements of the manipulator.

An analytical C3 continuous tool path corner smoothing algorithm for 6R robot manipulator

Abstract Tool path smoothness is important to guarantee good dynamic and tracking performance of robot manipulators. An analytical C3 continuous tool path corner smoothing algorithm is proposed for robot manipulators with 6 rotational (6R) joints. The tool tip position is smoothed directly in the workpiece coordinate system (WCS). The tool orientation is smoothed after transferring the tool orientation matrix as three rotary angles. Micro-splines of the tool tip position and tool orientation are constructed under the constraints of the maximum deviation error tolerances in the WCS. Then the tool orientation and tool tip position are synchronized to the tool tip displacement with C3 continuity by replacing the remaining linear segments using specially constructed B-splines. Control points of the locally inserted micro-splines are all evaluated analytically without any iterative calculations. Simulation and experimental results show that the proposed algorithm satisfies constraints of the preset tool tip position and the tool orientation tolerances. The proposed corner smoothing algorithm achieves smoother and lower jerks than C2 continuous corner smoothing algorithm. Experimental results show that the tracking errors associated to the execution of the C3 continuous tool path are up to 10% smaller than C2 continuous path errors.