Inverse Kinematics Based on Fuzzy Logic and Neural Networks for the WAM-Titan II Teleoperation System

The inverse kinematic problem is crucial for robotics. In this paper, a solution algorithm is presented using artificial intelligence to improve the pseudo-inverse Jacobian calculation for the 7-DOF Whole Arm Manipulator (WAM) and 6-DOF Titan II teleoperation system. An investigation of the inverse kinematics based on fuzzy logic and artificial neural networks for the teleoperation system was undertaken. Various methods such as Adaptive Neural-Fuzzy Inference System (ANFIS), Genetic Algorithms (GA), Multilayer Perceptrons (MLP) Feedforward Networks, Radial Basis Function Networks (RBF) and Generalized Regression Neural Networks (GRNN) were tested and simulated using MATLAB. Each method for identification of the pseudo-inverse problem was tested, and the best method was selected from the simulation results and the error analysis. From the results, the Multilayer Perceptrons with Levenberg-Marquardt (MLP-LM) method had the smallest error and the fastest computation among the other methods. For the WAM-Titan II teleoperation system, the new inverse kinematics calculations for the Titan II were simulated and analyzed using MATLAB. Finally, extensive C code for the alternative algorithm was developed, and the inverse kinematics based on the artificial neural network with LM method is implemented in the real system. The maximum error of Cartesian position was 1.3 inches, and from several trajectories, 75 % of time implementation was achieved compared to the conventional method. Because fast performance of a real time system in the teleoperation is vital, these results show that the new inverse kinematics method based on the MLP-LM is very successful with the acceptable error

Novel Artificial Neural Network Application for Prediction of Inverse Kinematics of Robot Manipulator

The robot control problem can be divided into two main areas, kinematics control (the coordination of the links of kinematics chain to produce desire motion of the robot), and dynamic control (driving the actuator of the mechanism to follow the commanded position velocities). In general the control strategies used in robot involves position coordination in Cartesian space by direct or indirect kinematics method. Inverse kinematics comprises the computation need to find the join angles for a given Cartesian position and orientation of the end effectors. This computation is fundamental to control of robot arms but it is very difficult to calculate an inverse kinematics solution of robot manipulator. For this solution most industrial robot arms are designed by using a non-linear algebraic computation to finding the inverse kinematics solution. From the literature it is well described that there is no unique solution for the inverse kinematics. That is why it is significant to apply an artificial neural network models. Here structured artificial neural network (ANN) models an approach has been proposed to control the motion of robot manipulator. In these work two types of ANN models were used. The first kind ANN model is MLP (multi-layer perceptrons) which was famous as back propagation neural network model. In this network gradient descent type of learning rules are applied. The second kind of ANN model is PPN (polynomial poly-processor neural network) where polynomial equation was used. Here, work has been undertaken to find the best ANN configuration for the problem. It was found that between MLP and PPN, MLP gives better result as compared to PPN by considering average percentage error, as the performance index

Surrogate models for the design and control of soft mechanical systems

Soft mechanical systems constitute stretchable skins, tissue-like appendages, fibers and fluids, and utilize material deformation to transmit forces or motion to perform a mechanical task. These systems may possess infinite degrees of freedom with finite modes of actuation and sensing, and this creates challenges in modeling, design and controls. This thesis explores the use of surrogate models to approximate the complex physics between the inputs and outputs of a soft mechanical system composed of a ubiquitous soft building block known as Fiber Reinforced Elastomeric Enclosures (FREEs). Towards this the thesis is divided into two parts, with the first part investigating reduced order models for design and the other part investigating reinforcement learning (RL) framework for controls. The reduced order models for design is motivated by the need for repeated quick and accurate evaluation of the system performance. Two mechanics-based models are investigated: (a) A Pseudo Rigid Body model (PRB) with lumped spring and link elements, and (b) a Homogenized Strain Induced (HIS) model that can be implemented in a finite element framework. The parameters of the two models are fit either directly with experiments on FREE prototypes or with a high fidelity robust finite element model. These models capture fundamental insights on design by isolating a fundamental dyad building block of contracting FREEs that can be configured to either obtain large stroke (displacement) or large force. Furthermore, the thesis proposes a novel building block-based design framework where soft FREE actuators are systematically integrated in a compliant system to yield a given motion requirement. The design process is deemed useful in shape morphing adaptive structures such as airfoils, soft skins, and wearable devices for the upper extremities. Soft robotic systems such as manipulators are challenging to control because of their flexibility, ability to undergo large spatial deformations that are dependent on the external load. The second part of this work focuses on the control of a unique soft continuum arm known as the BR2 manipulator using reinforcement learning (RL). The BR2 manipulator has a unique parallel architecture with a combined bending mode and torsional modes, and its inherent asymmetric nature precludes well defined analytical models to capture its forward kinematics. Two RL-based frameworks are evaluated on the BR2 manipulator and their efficacy in carrying out position control using simple state feedback is reported in this work. The results highlight external load invariance of the learnt control policies which is a significant factor for deformable continuum arms for applications involving pick and place operations. The manipulator is deemed useful in berry harvesting and other agricultural applications

Inverse Kinematic Analysis of Robot Manipulators

An important part of industrial robot manipulators is to achieve desired position and orientation of end effector or tool so as to complete the pre-specified task. To achieve the above stated goal one should have the sound knowledge of inverse kinematic problem. The problem of getting inverse kinematic solution has been on the outline of various researchers and is deliberated as thorough researched and mature problem. There are many fields of applications of robot manipulators to execute the given tasks such as material handling, pick-n-place, planetary and undersea explorations, space manipulation, and hazardous field etc. Moreover, medical field robotics catches applications in rehabilitation and surgery that involve kinematic, dynamic and control operations. Therefore, industrial robot manipulators are required to have proper knowledge of its joint variables as well as understanding of kinematic parameters. The motion of the end effector or manipulator is controlled by their joint actuator and this produces the required motion in each joints. Therefore, the controller should always supply an accurate value of joint variables analogous to the end effector position. Even though industrial robots are in the advanced stage, some of the basic problems in kinematics are still unsolved and constitute an active focus for research. Among these unsolved problems, the direct kinematics problem for parallel mechanism and inverse kinematics for serial chains constitute a decent share of research domain. The forward kinematics of robot manipulator is simpler problem and it has unique or closed form solution. The forward kinematics can be given by the conversion of joint space to Cartesian space of the manipulator. On the other hand inverse kinematics can be determined by the conversion of Cartesian space to joint space. The inverse kinematic of the robot manipulator does not provide the closed form solution. Hence, industrial manipulator can achieve a desired task or end effector position in more than one configuration. Therefore, to achieve exact solution of the joint variables has been the main concern to the researchers. A brief introduction of industrial robot manipulators, evolution and classification is presented. The basic configurations of robot manipulator are demonstrated and their benefits and drawbacks are deliberated along with the applications. The difficulties to solve forward and inverse kinematics of robot manipulator are discussed and solution of inverse kinematic is introduced through conventional methods. In order to accomplish the desired objective of the work and attain the solution of inverse kinematic problem an efficient study of the existing tools and techniques has been done. A review of literature survey and various tools used to solve inverse kinematic problem on different aspects is discussed. The various approaches of inverse kinematic solution is categorized in four sections namely structural analysis of mechanism, conventional approaches, intelligence or soft computing approaches and optimization based approaches. A portion of important and more significant literatures are thoroughly discussed and brief investigation is made on conclusions and gaps with respect to the inverse kinematic solution of industrial robot manipulators. Based on the survey of tools and techniques used for the kinematic analysis the broad objective of the present research work is presented as; to carry out the kinematic analyses of different configurations of industrial robot manipulators. The mathematical modelling of selected robot manipulator using existing tools and techniques has to be made for the comparative study of proposed method. On the other hand, development of new algorithm and their mathematical modelling for the solution of inverse kinematic problem has to be made for the analysis of quality and efficiency of the obtained solutions. Therefore, the study of appropriate tools and techniques used for the solution of inverse kinematic problems and comparison with proposed method is considered. Moreover, recommendation of the appropriate method for the solution of inverse kinematic problem is presented in the work. Apart from the forward kinematic analysis, the inverse kinematic analysis is quite complex, due to its non-linear formulations and having multiple solutions. There is no unique solution for the inverse kinematics thus necessitating application of appropriate predictive models from the soft computing domain. Artificial neural network (ANN) can be gainfully used to yield the desired results. Therefore, in the present work several models of artificial neural network (ANN) are used for the solution of the inverse kinematic problem. This model of ANN does not rely on higher mathematical formulations and are adept to solve NP-hard, non-linear and higher degree of polynomial equations. Although intelligent approaches are not new in this field but some selected models of ANN and their hybridization has been presented for the comparative evaluation of inverse kinematic. The hybridization scheme of ANN and an investigation has been made on accuracies of adopted algorithms. On the other hand, any Optimization algorithms which are capable of solving various multimodal functions can be implemented to solve the inverse kinematic problem. To overcome the problem of conventional tool and intelligent based method the optimization based approach can be implemented. In general, the optimization based approaches are more stable and often converge to the global solution. The major problem of ANN based approaches are its slow convergence and often stuck in local optimum point. Therefore, in present work different optimization based approaches are considered. The formulation of the objective function and associated constrained are discussed thoroughly. The comparison of all adopted algorithms on the basis of number of solutions, mathematical operations and computational time has been presented. The thesis concludes the summary with contributions and scope of the future research work

Commande dynamique de robots déformables basée sur un modèle numérique

This work focuses on modeling and control of soft robots. It covers the entire development of the controller, from the modeling step to the practical experimental validation.From a theoretical point a view, large-scale dynamical systems along with model reduction algorithms are studied. In addition to the theoretical studies, different experimental setups are used to illustrate the results. A cable-driven soft robot and a pressurized soft arm are used to test the control algorithms. Through these different setups, we show that the method can handle different types of actuation, different geometries and mechanical properties. This emphasizes one of the interests of the method, its genericity.Cette thèse s'intéresse à la modélisation et à la commande de robots déformables (robots dont le mouvement se fait par déformation). Nous nous intéressons à la conception de lois de contrôle en boucle fermée répondant aux besoins spécifiques du contrôle dynamique de ces robots, sans restrictions fortes sur leur géométrie. La résolution de ce défi soulève des questions théoriques qui nous amènent au deuxième objectif de cette thèse: développer de nouvelles stratégies pour étudier les systèmes de grandes dimensions

Efficient Sub-Optimal Inverse Kinematic Solution for Redundant Manipulators

Master'sMASTER OF ENGINEERIN

Advanced Strategies for Robot Manipulators

Amongst the robotic systems, robot manipulators have proven themselves to be of increasing importance and are widely adopted to substitute for human in repetitive and/or hazardous tasks. Modern manipulators are designed complicatedly and need to do more precise, crucial and critical tasks. So, the simple traditional control methods cannot be efficient, and advanced control strategies with considering special constraints are needed to establish. In spite of the fact that groundbreaking researches have been carried out in this realm until now, there are still many novel aspects which have to be explored

Multi-point static dexterous posture manipulation for the stiffness identification of serial kinematic end-effectors.

Masters Degree. University of KwaZulu-Natal, Durban.The low stiffness inherent in serial robots hinders its application to perform advanced operations due to its reduced accuracy imparted through deformations within the links and joints. The high repeatability, extended workspace, and speed of serial manipulators make them appealing to perform precision operations as opposed to its alternative, the CNC machine. However, due to the serial arrangement of the linkages of the system, they lack the accuracy to meet present-day demands. To address the low stiffness problem, this research provided a low-cost dexterous posture identification method. The study investigated the joint stiffness of a Fanuc M10-iA 6 Degree of Freedom (DOF) serial manipulator. The investigation involved a multivariable analysis that focused on the robot’s workspace, kinematic singularity, and dexterity to locate high stiffness areas and postures. The joint stiffness modelling applied the Virtual Joint Method (VJM), which replaced the complicated mechanical robot joints with one-dimensional (1-D) springs. The effects of stress and deflection are linearly related; the highest stress in a robot’s structure is distributed to the higher load-bearing elements such as the robot joints, end-effector, and tool. Therefore, by locating optimal postures, the induced stresses can be better regulated throughout the robot’s structure, thereby reducing resonant vibrations of the system and improving process accuracy and repeatability. These aspects are quantifiably pitched in terms of the magnitude differences in the end-effector deflection. The unique combination of the dexterity and the stiffness analyses aimed to provide roboticists and manufacturers with an easy and systematic solution to improve the stiffness, accuracy, and repeatability of their serial robots. A simple, user-friendly and cost-effective alternative to deflection measurements using accelerometers is provided, which offers an alternative to laser tracking devices that are commonly used for studies of this nature. The first investigation focused on identifying the overall workspace of the Fanuc M-10iA robot. The reachable workspace was investigated to understand the functionality and potential of the Fanuc robot. Most robotic studies stem from analysing the workspace since the workspace is a governing factor of the manipulator and end-effector placement, and its operations, in a manufacturing setting. The second investigation looked at identifying non-reachable areas and points surrounding the robot. This analysis, along with the workspace examination, provided a conclusive testing platform to test the dexterity and stiffness methodologies. Although the research focused on fixing the end-effector at a point (static case), the testing platform was structured precisely to cater for all robotic manufacturing tasks that are subjected to high applied forces and vibrations. Such tasks include, but are not limited to, drilling, tapping, fastening, or welding, and some dynamic and hybrid manufacturing operations. The third investigation was the application of a dexterous study that applied an Inverse Kinematic (IK) method to localise multiple robot configurations about a user-defined point in space. This process was necessary since the study is based on a multi-point dexterous posture identification technique to improve the stiffness of Serial Kinematic Machines (SKMs). The stiffness at various points and configurations were tested, which provided a series of stiff and non-stiff areas and postures within the robot’s workspace. MATLAB®, a technical computing software, was used to model the workspace and singularity of the robot. The dexterity and stiffness analyses were numerically evaluated using Wolfram Mathematica. The multivariable analyses served to improve the accuracy of serial robots and promote their functionality towards high force application manufacturing tasks. Apart from the improved stiffness performance offered, the future benefit of the method could advance the longevity of the robot as well as minimise the regular robot maintenance that is often required due to excessive loading, stress, and strain on the robot motors, joints, and links

Analytical Workspace, Kinematics, and Foot Force Based Stability of Hexapod Walking Robots

Many environments are inaccessible or hazardous for humans. Remaining debris after earthquake and fire, ship hulls, bridge installations, and oil rigs are some examples. For these environments, major effort is being placed into replacing humans with robots for manipulation purposes such as search and rescue, inspection, repair, and maintenance. Mobility, manipulability, and stability are the basic needs for a robot to traverse, maneuver, and manipulate in such irregular and highly obstructed terrain. Hexapod walking robots are as a salient solution because of their extra degrees of mobility, compared to mobile wheeled robots. However, it is essential for any multi-legged walking robot to maintain its stability over the terrain or under external stimuli. For manipulation purposes, the robot must also have a sufficient workspace to satisfy the required manipulability. Therefore, analysis of both workspace and stability becomes very important. An accurate and concise inverse kinematic solution for multi-legged robots is developed and validated. The closed-form solution of lateral and spatial reachable workspace of axially symmetric hexapod walking robots are derived and validated through simulation which aid in the design and optimization of the robot parameters and workspace. To control the stability of the robot, a novel stability margin based on the normal contact forces of the robot is developed and then modified to account for the geometrical and physical attributes of the robot. The margin and its modified version are validated by comparison with a widely known stability criterion through simulated and physical experiments. A control scheme is developed to integrate the workspace and stability of multi-legged walking robots resulting in a bio-inspired reactive control strategy which is validated experimentally